| "Mathematics Grade 8 (4 of 5) - Measurement" |
| "Welcome to this Mathematics Grade 8 course, module 4 of 5 on Measurement.The aim of this course is for it to be taught in a 21st century mindset. The course is actually fun, it uses technology, the internet and general knowledge to better equip a student for real life applications.This course is intended for absolutely age 12-14 years old whom already has a foundation phase understanding in Mathematics. This course is also suitable for any end of middle-school to start of High School (US) or Grade 7-9 (UK) teachers who want to improve upon classroom teaching methods. This course is packed with 2+ hours of video content encouraging students to answer questions throughout the videos. There are 5 modules in total, this is module 4.1. Numbers, Operations & Relationships.2. Patterns, Functions & Algebra. 3. Space and Shape.4. Measurement.5. Data Handling.The course has been broken into 5 modules to i) allow students to only enroll in what sections they are struggling with, ii) save the parent or student cost as each module is the price of a tutoring lesson and lastly, iii) to encourage students to break goals into bite-sized chunks. There is 16+ hours of video in all five modules.This course is intended to be a resource to help students throughout the year, it can be completed slowly and referred to throughout your year of study. The course has been broken down into the following sub-sections, each containing their own set of videos:Area and Perimeter of 2D ShapesVolume and Surface Area of 3D ShapesTheorem of PythagorasEnjoy!"
Price: 39.99 |
| "Stock Fundamental Analysis with Excel" |
| "Full Course Content Last Update 01/2019Learn stock fundamental analysis through a practical course with Microsoft Excel using Apple Inc. data for historical analysis. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.Become a Stock Fundamental Analysis Expert in this Practical Course with ExcelUnderstand main financial statements such as balance sheet, income statement and cash flow statement.Interpret financial ratios to analyze operating activities, investing activities, liquidity, solvency and profitability.Comprehend investment valuation ratios to assess relative magnitudes of stock price or enterprise value to key financial statements numerical values and corresponding yields.Estimate cost of equity through capital asset pricing model (CAPM), Fama-French three factors model or arbitrage pricing theory model (APT) and cost of capital through weighted average cost of capital model (WACC).Assess economic profit through economic value added model (EVA) and observed market value added model (MVA).Approximate theoretical stock price through discounted cash flow models (DCF) such as discounted dividends model (DDM), discounted free cash flow to equity model (DFCFE) and discounted free cash flow to firm model (DFCFF).Estimate stock option prices through Black and Scholes model, one-step binomial tree model, two-steps binomial tree model and Monte Carlo Simulation method (MCS).Become a Stock Fundamental Analysis Expert and Put Your Knowledge in PracticeLearning stock fundamental analysis is essential for finance careers in areas such as equity research, investment banking, private equity or venture capital. It is also indispensable for academic careers in finance or business research. And it is necessary for experienced investors research of stock fundamentals.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using Apple Inc. data for historical analysis and to achieve greater effectiveness. Content and OverviewThis practical course contains 49 lectures and 9 hours of content. Its designed for all stock fundamental analysis knowledge levels and a basic understanding of Microsoft Excel is useful but not required.At first, youll learn how to perform stock fundamental analysis operations using built-in functions and array calculations. Next, youll learn how to do random number generation for option pricing calculation using Microsoft Excel Add-in.Then, youll define financial statements. For financial statements, youll define balance sheet, income statement and cash flow statement. After that, youll define financial ratios and compare them with historical average and competitor. For financial ratios, youll define operating activities, investing activities, liquidity, solvency and profitability areas. For models based on financial ratios, youll define DuPont analysis, Altman Z-Score and Piotroski F-Score.Later, youll define investment valuation ratios and compare them with historical average, competitor, market benchmark and risk-free rate of return. For investment valuation ratios, youll define price ratios, enterprise value ratios and yield ratios.Next, youll define cost of equity. For cost of equity, youll define capital asset pricing model (CAPM), Fama-French three factors model and arbitrage pricing theory model (APT). Then, youll define cost of capital. For cost of capital, youll define weighted average cost of capital model (WACC).After that, youll define economic profit. For economic profit, youll define economic value added model (EVA) and observed market value added model (MVA). Later, youll estimate theoretical stock price through discounted cash flow models (DCF). For discounted cash flow models (DCF), youll define discounted dividends model (DDM), discounted free cash flow to equity model (DFCFE) and discounted free cash flow to firm model (DFCFF).Finally, youll define financial options. For financial options pricing, youll define Black and Scholes model, one-step binomial tree model, two-steps binomial tree model and Monte Carlo simulation method (MCS)."
Price: 49.99 |
| "Excel for Data Analysis: Basic to Expert Level" |
| "Full Course Content Last Update 12/2019Learn data analysis through a practical course with Microsoft Excel using S&P 500 Index ETF prices historical data. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business data analysis research. All of this while exploring the wisdom of best academics and practitioners in the field.Become a Data Analysis Expert in this Practical Course with ExcelUse main Excel file or workbook types and protect them before sharing. Store data in workbooks consisting of one of more worksheets.Navigate worksheets through their cells which can be grouped in ranges.Move quickly through worksheet using keyboard shortcuts. Perform data calculations using basic arithmetic formulas, main categories built-in functions and arrays.Correct formula errors and perform user input data validation. Organize data interactively using tables and customizable pivot tables while sorting, filtering and performing calculations on their contents.Visualize data using conditional formatting, main chart types and single cell sparklines.Implement data scenarios summary forecast using what-if analysis and find value for achieving certain calculation result using goal seek changing cell iteration.Analyze data and estimate optimal parameter using data analysis tools package and solver Microsoft Excel Add-ins.Identify data trends using moving average and exponential smoothing tools.Summarize data descriptive statistics and print its frequency histogram. Estimate correlation between variables and analyze regression summary output between explained and explanatory variables.Generate random numbers based on specific probability distribution.Estimate optimal parameter using constrained minimization.Become a Data Analysis Expert and Put Your Knowledge in PracticeLearning data analysis is indispensable for business data analysis applications in areas such as consumer analytics, finance, banking, health care, e-commerce or social media. It is also essential for academic careers in data analysis, applied statistics, economics, econometrics and quantitative finance. And its necessary for business data analysis research.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for data analysis to achieve greater effectiveness.Content and OverviewThis practical course contains 42 lectures and 6.5 hours of content. Its designed for all data analysis knowledge levels and a basic understanding of Microsoft Excel is useful but not required.At first, youll define data analysis operations using Microsoft Excel. Next, youll define data analysis tools package and solver Microsoft Excel Add-ins.Then, youll define Excel file, file options, workbook and main workbook types. Next, youll define ribbon. For ribbon, youll define file tab, home tab, insert tab, page layout tab, formulas tab, data tab, review tab and view tab. After that, youll define worksheet. Later, youll define cells and cells references. For cells references, youll define relative reference, absolute reference, mixed reference and 3D reference. Then, youll define ranges and ranges operators. For ranges operators, youll define union range operator, intersect range operator and mathematical union. Next, youll define keyboard shortcuts.Next, youll define formulas, status bar and functions. For functions, youll define date & time functions category, financial functions category, logical functions category, lookup & reference functions category, mathematic & trigonometric functions category, statistics functions category and text functions category. Then, youll define array formulas. After that, youll define formula errors and formula auditing options. Later, youll define range of cells defined name and user input data validation.After that, youll define data analysis. Then, youll define sorting and filtering table data. Next, youll define conditional formatting. For conditional formatting, youll define highlight cell rules, top/bottom rules, data bars rules, color scales rules and icon set rules. Later, youll define column chart, line chart, pie chart, scatter chart, line sparkline, column sparkline and win/loss sparkline. After that, youll define tables and customizable pivot tables. For customizable pivot tables, youll define field selection feature, sorting and filtering feature, value field settings feature and chart feature. Then, youll define what-if data scenario summary and goal seek changing cell iteration. Next, youll define data analysis tools package Microsoft Excel Add-in. For data analysis tools package, youll define moving average analysis tool, exponential smoothing analysis tool, descriptive statistics analysis tool, histogram analysis tool, correlation analysis tool, regression analysis tool and random number generation analysis tool. Finally, youll define solver Microsoft Excel Add-in. For solver, youll define optimal parameter estimation through constrained minimization."
Price: 49.99 |
| "Investment Portfolio Analysis with Excel" |
| "Full Course Content Last Update 12/2018Learn investment portfolio analysis through a practical course with Microsoft Excel using index replicating ETFs and Mutual Funds historical data for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.Become an Investment Portfolio Analysis Expert in this Practical Course with ExcelCompare main asset classes benchmark indexes replicating funds returns and risks tradeoffs for cash, bonds, stocks, commodities, real estate and currencies.Estimate portfolio expected returns, historical and market participants implied volatility. Approximate portfolio expected excess returns using capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT).Hedge portfolio systematic risk through options trading strategies benchmark indexes replicating funds.Evaluate hedge fund index performance and assess portfolio returns and risks amplification through leverage.Calculate portfolio performance metrics such as Sharpe, Treynor, Sortino, and Kelly ratios.Estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers.Optimize global portfolios asset allocation weights for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives within training range based on Markowitz portfolio theory.Approximate global portfolios returns from periodically rebalanced optimized asset allocations within testing range and compare them with equal weighted and well-known investment managers benchmark portfolios.Evaluate global portfolios performance through global risk factors model and estimate their expected return, expected excess return and expected return contribution from global risk factors exposure while assessing investment costs impact on portfolio performance.Become an Investment Portfolio Analysis Expert and Put Your Knowledge in PracticeLearning investment portfolio analysis is indispensable for finance careers in areas such as asset management, private wealth management, and risk management within institutional investors represented by banks, insurance companies, pension funds, hedge funds, investment advisors, endowments and mutual funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors optimized asset allocation strategies research and development.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using index replicating funds historical data for back-testing and to achieve greater effectiveness. Content and OverviewThis practical course contains 43 lectures and 8.5 hours of content. Its designed for all investment portfolio analysis knowledge levels and a basic understanding of Microsoft Excel is useful but not required.At first, youll learn how to perform investment portfolio analysis operations using built-in functions and array calculations. Next, youll learn how to do portfolio optimization parameters estimation and linear regression calculation using Microsoft Excel Add-ins.Then, youll define main asset classes by comparing their benchmark indexes replicating funds returns and risks tradeoffs. After that, youll segment main asset classes into traditional and alternative ones. For traditional asset classes, youll define cash and cash equivalents, fixed income or bonds and equities or stocks. Regarding cash and cash equivalents traditional asset class, youll use U.S. total money market benchmark index replicating fund. Regarding cash and cash equivalents traditional asset class, youll use U.S. total money market benchmark index replicating fund is used. Regarding fixed income or bonds traditional asset class, U.S. total bond market, U.S. short term bond market, U.S. long term bond market and international total bond market benchmark indexes replicating funds. Regarding equities or stocks traditional asset class, youll use U.S. total stock market, U.S. large cap stock market, U.S. small cap stock market, U.S. small cap growth stock market, U.S. small cap value stock market, international total stock market, international developed stock market and international emerging stock market benchmark indexes replicating funds. For alternative asset classes, youll define commodities, real estate and currencies or foreign exchange. Regarding commodities alternative asset class, youll use oil and gold prices benchmark indexes replicating funds. Regarding real estate alternative asset class, youll use U.S. real estate investment trust market benchmark index replicating fund. Regarding currencies or foreign exchange alternative asset class, youll use U.S. dollar major currencies benchmark index replicating fund.Next, youll define returns and risks using U.S. large cap stocks market benchmark index replicating fund. After that, youll calculate expected returns through historical returns mean and median. Then, youll estimate risks through historical returns standard deviation, mean absolute deviation and market participants implied volatility. Later, youll approximate portfolio expected excess returns through capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT). Next, youll hedge portfolio systematic risk through options trading strategies and evaluate hedge fund index performance together with the assessment of returns and risks amplification through portfolio leverage. After that, youll define portfolio optimization through global assets allocation. Next, youll calculate Sharpe ratio, Treynor ratio, Sortino ratio and Kelly ratio portfolio performance metrics. Then, youll estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers. Later, youll optimize global asset allocation weights within training range for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives based on Markowitz portfolio theory. After that, youll calculate global portfolio returns within testing range using previously optimized periodically rebalance asset allocation weights and compare them with equal weighted and well-known investment managers benchmark portfolios.Later, youll evaluate optimized portfolios performance through global risk factors model. After that, youll estimate optimized portfolios expected returns, expected excess returns and global risk factors exposure returns contribution. Finally, youll assess investment costs impact on portfolio performance."
Price: 49.99 |
| "Forecasting Models with Excel" |
| "Full Course Content Last Update 06/2018Learn forecasting models through a practical course with Microsoft Excel using S&P 500 Index ETF prices historical data. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. All of this while exploring the wisdom of best academics and practitioners in the field.Become a Forecasting Models Expert in this Practical Course with ExcelEstimate simple forecasting methods such as arithmetic mean, random walk, seasonal random walk and random walk with drift.Evaluate simple forecasting methods forecasting accuracy through mean absolute error, root mean squared error scale-dependent and mean absolute percentage error, mean absolute scaled error scale-independent metrics.Approximate simple moving averages and exponential smoothing methods with no trend or seasonal patterns such as Brown simple exponential smoothing method.Estimate exponential smoothing methods with only trend patterns such as Holt linear trend, exponential trend, Gardner additive damped trend and Taylor multiplicative damped trend methods.Approximate exponential smoothing methods with trend and seasonal patterns such as Holt-Winters additive, Holt-Winters multiplicative and Holt-Winters damped methods.Select exponential smoothing method with lowest Akaike, corrected Akaike and Schwarz Bayesian information loss criteria.Identify Box-Jenkins autoregressive integrated moving average model integration order through level and differentiated first order trend stationary time series deterministic test and Phillips-Perron unit root test.Recognize autoregressive integrated moving average model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions.Estimate non-seasonal autoregressive integrated moving average models such as random walk with drift, differentiated first order autoregressive, Brown simple exponential smoothing, simple exponential smoothing with growth, Holt linear trend and Gardner additive damped trend models.Approximate seasonal autoregressive integrated moving average models such as seasonal random walk, seasonal random trend and seasonally differentiated first order autoregressive models.Choose autoregressive integrated moving average model with lowest Akaike, corrected Akaike and Schwarz Bayesian information loss criteria.Assess highest forecasting accuracy autoregressive integrated moving average model residuals or forecasting errors white noise requirement through Ljung-Box lagged autocorrelation test.Become a Forecasting Models Expert and Put Your Knowledge in PracticeLearning forecasting models is indispensable for business or financial data science applications in areas such as sales and financial forecasting, inventory optimization, demand and operations planning, and cash flow management. It is also essential for academic careers in data science, applied statistics, operations research, economics, econometrics and quantitative finance. And its necessary for business forecasting research.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for forecast modelling to achieve greater effectiveness.Content and OverviewThis practical course contains 42 lectures and 8 hours of content. Its designed for all forecasting models knowledge levels and a basic understanding of Microsoft Excel is useful but not required.At first, youll learn how to perform forecasting models operations using built-in functions and array calculations. Next, youll learn how to do optimal parameter estimation or fine tuning and linear regression calculation using Microsoft Excel Add-ins.Then, youll define simple forecasting methods such as arithmetic mean, random walk, seasonal random walk and random walk with drift. Next, youll evaluate simple methods forecasting accuracy through scale-dependent and scale-independent error metrics. For scale-dependent metrics, youll define mean absolute error and root mean squared error. For scale-independent metrics, youll define mean absolute percentage error and mean absolute scaled error.Next, youll define simple moving averages and exponential smoothing methods. For exponential smoothing methods with no trend or seasonal patterns, youll define Brown simple exponential smoothing method. For exponential smoothing methods with only trend patterns, youll define Holt linear trend, exponential trend, Gardner additive damped trend and Taylor multiplicative damped trend methods. For exponential smoothing methods with trend and seasonal patterns, youll define Holt-Winters additive, Holt-Winters multiplicative and Holt-Winters damped methods. After that, youll select exponential smoothing method with lowest information loss criteria. For information loss criteria, youll define Akaike, corrected Akaike and Schwarz Bayesian information loss criteria. Later, youll evaluate simple moving average and exponential smoothing methods forecasting accuracy through scale-dependent and scale-independent error metrics. For scale-dependent metrics, youll define mean absolute error and root mean squared error. For scale-independent metrics, youll define mean absolute percentage error and mean absolute scaled error.After that, youll define Box-Jenkins autoregressive integrated moving average models. Then, youll identify autoregressive integrated moving average model integration order through level and differentiated time series first order trend stationary deterministic test and Phillips-Perron unit root test. Next, youll identify autoregressive integrated moving average model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions. For non-seasonal autoregressive integrated moving average models, youll define random walk with drift, differentiated first order autoregressive, Brown simple exponential smoothing, simple exponential smoothing with growth, Holt linear trend and Gardner additive damped trend models. For seasonal autoregressive integrated moving average models, youll define seasonal random walk, seasonal random trend and seasonally differentiated first order autoregressive models. After that, youll select autoregressive integrated moving average model with lowest information loss criteria. For information loss criteria, youll define Akaike, corrected Akaike and Schwarz Bayesian information loss criteria. Later, youll evaluate models forecasting accuracy through scale-dependent and scale-independent error metrics. For scale-dependent metrics, youll define mean absolute error and root mean squared error. For scale-independent metrics, youll define mean absolute percentage error and mean absolute scaled error. Finally, youll assess highest forecasting accuracy autoregressive integrated moving average model residuals or forecasting errors white noise requirement through Ljung-Box lagged autocorrelation test."
Price: 49.99 |
| "Forecasting Models with R" |
| "Full Course Content Last Update 04/2018Learn forecasting models through a practical course with R statistical software using S&P 500 Index ETF prices historical data. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. All of this while exploring the wisdom of best academics and practitioners in the field.Become a Forecasting Models Expert in this Practical Course with RRead S&P 500 Index ETF prices data and perform forecasting models operations by installing related packages and running script code on RStudio IDE.Estimate simple forecasting methods such as arithmetic mean, random walk, seasonal random walk and random walk with drift.Evaluate simple forecasting methods forecasting accuracy through mean absolute error, root mean squared error scale-dependent and mean absolute percentage error scale-independent metrics.Approximate simple moving averages and exponential smoothing methods with no trend or seasonal patterns such as Brown simple exponential smoothing method.Estimate exponential smoothing methods with only trend patterns such as Holt linear trend, exponential trend, Gardner additive damped trend and Taylor multiplicative damped trend methods.Approximate exponential smoothing methods with trend and seasonal patters such as Holt-Winters additive, Holt-Winters multiplicative and Holt-Winters damped methods.Automatically select exponential smoothing method with lowest information loss criteria.Asses simple moving average and exponential smoothing methods forecasting accuracy through Hyndman-Koehler mean absolute scaled error scale-independent metric.Identify Box-Jenkins autoregressive integrated moving average model integration order through level and differentiated first order trend stationary time series augmented Dickey-Fuller and Phillips-Perron unit root tests.Recognize autoregressive integrated moving average model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions.Estimate non-seasonal autoregressive integrated moving average models such as random walk, random walk with drift, differentiated first order autoregressive, Brown simple exponential smoothing, simple exponential smoothing with growth, Holt linear trend and Gardner additive damped trend models.Approximate seasonal autoregressive integrated moving average models such as seasonal random walk, seasonal random trend, general seasonal, general first order autoregressive seasonal, seasonally differentiated first order autoregressive and Holt-Winters additive models.Automatically choose autoregressive integrated moving average model with lowest information loss criteria.Evaluate autoregressive integrated moving average models forecasting accuracy through scale-dependent, scale-independent metrics, Akaike, corrected Akaike and Schwarz Bayesian information loss criteria.Assess lowest information loss criteria autoregressive integrated moving average model residuals or forecasting errors white noise requirement through Ljung-Box lagged autocorrelation test.Become a Forecasting Models Expert and Put Your Knowledge in PracticeLearning forecasting models is indispensable for business or financial data science applications in areas such as sales and financial forecasting, inventory optimization, demand and operations planning, and cash flow management. It is also essential for academic careers in data science, applied statistics, operations research, economics, econometrics and quantitative finance. And its necessary for business forecasting research.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for forecast modelling to achieve greater effectiveness.Content and OverviewThis practical course contains 40 lectures and 6 hours of content. Its designed for all forecasting models knowledge levels and a basic understanding of R statistical software is useful but not required.At first, youll learn how to read S&P 500 Index ETF prices historical data to perform forecasting models operations by installing related packages and running script code on RStudio IDE.Then, youll define simple forecasting methods such as arithmetic mean, random walk, seasonal random walk and random walk with drift. Next, youll evaluate simple methods forecasting accuracy through scale-dependent and scale-independent error metrics. For scale-dependent metrics, youll define mean absolute error and root mean squared error. For scale-independent metrics, youll define mean absolute percentage error.Next, youll define simple moving averages and exponential smoothing methods. For exponential smoothing methods with no trend or seasonal patterns, youll define Brown simple exponential smoothing method. For exponential smoothing methods with only trend patterns, youll define Holt linear trend, exponential trend, Gardner additive damped trend and Taylor multiplicative damped trend methods. For exponential smoothing methods with trend and seasonal patterns, youll define Holt-Winters additive, Holt-Winters multiplicative and Holt-Winters damped methods. After that, youll automatically select exponential smoothing method with lowest information loss criteria. Later, youll evaluate simple moving average and exponential smoothing methods forecasting accuracy through Hyndman-Koehler scale-independent mean absolute scaled error metric.After that, youll define Box-Jenkins autoregressive integrated moving average models. Then, youll identify autoregressive integrated moving average model integration order through level and differentiated time series first order trend stationary augmented Dickey-Fuller and Phillips-Perron unit root tests. Next, youll identify autoregressive integrated moving average model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions. For non-seasonal autoregressive integrated moving average models, youll define random walk, random walk with drift, differentiated first order autoregressive, Brown simple exponential smoothing, simple exponential smoothing with growth, Holt linear trend and Gardner additive damped trend models. For seasonal autoregressive integrated moving average models, youll define seasonal random walk, seasonal random trend, general seasonal, general first order autoregressive seasonal, seasonally differentiated first order autoregressive and Holt-Winters additive models. After that, youll automatically select autoregressive integrated moving average model with lowest information loss criteria. Later, youll evaluate models forecasting accuracy and information loss criteria. For information loss criteria, youll define Akaike, corrected Akaike and Schwarz Bayesian information loss criteria. Finally, youll assess lowest information loss criteria autoregressive integrated moving average model residuals or forecasting errors white noise requirement through Ljung-Box lagged autocorrelation test."
Price: 49.99 |
| "Stock Technical Analysis with R" |
| "Full Course Content Last Update 12/2017Learn stock technical analysis through a practical course with R statistical software using S&P 500 Index ETF historical data for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do research as experienced investor. All of this while referencing best practitioners in the field.Become a Stock Technical Analysis Expert in this Practical Course with RRead or download S&P 500 Index ETF prices data and perform technical analysis operations by installing related packages and running script code on RStudio IDE.Compute lagging stock technical indicators or overlays such as moving averages, Bollinger bands, parabolic stop and reverse.Calculate leading stock technical indicators or oscillators such as average directional movement index, commodity channel index, moving averages convergence/divergence, rate of change, relative strength index, stochastic momentum index and Williams %R.Determine single technical indicator based stock trading opportunities through price, double, bands, centerline and signal crossovers.Define multiple technical indicators based stock trading occasions through price crossovers confirmed by bands crossovers.Outline long-only stock trading strategies based on single or multiple technical indicators trading openings.Assess stock trading strategies performance by comparing their annualized return, standard deviation and Sharpe ratio against buy and hold benchmark.Become a Stock Technical Analysis Expert and Put Your Knowledge in PracticeLearning stock technical analysis is indispensable for finance careers in areas such as equity research and equity trading. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors stock technical trading research and development.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for back-testing to achieve greater effectiveness.Content and OverviewThis practical course contains 46 lectures and 7 hours of content. Its designed for all stock technical analysis knowledge levels and a basic understanding of R statistical software is useful but not required.At first, youll learn how to read or download S&P 500 Index ETF prices historical data to perform technical analysis operations by installing related packages and running script code on RStudio IDE.Next, youll calculate lagging stock technical indicators such as simple moving averages (SMA), exponential moving averages (EMA), Bollinger bands (BB), parabolic stop and reverse (SAR). After that, youll compute leading stock technical indicators such as average directional movement index (ADX), commodity channel index (CCI), moving averages convergence/divergence (MACD), rate of change (ROC), relative strength index (RSI), stochastic momentum index (SMI) and Williams %R.Then, youll define single technical indicator based stock trading openings through price, double, bands, centerline and signal crossovers. Next, youll determine multiple technical indicators based trading opportunities through price crossovers which need to be confirmed by second technical indicator band crossover. Later, youll give shape to long-only stock trading strategies using single or multiple technical indicators trading occasions.Finally, youll evaluate stock trading strategies performance with buy and hold as initial benchmark and comparing their annualized return for performance, annualized standard deviation for volatility or risk and annualized Sharpe ratio for risk adjusted return."
Price: 49.99 |
| "Stock Technical Analysis with Python" |
| "Full Course Content Last Update 06/2017Learn stock technical analysis through a practical course with Python programming language using S&P 500 Index ETF historical data for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do research as experienced investor. All of this while referencing best practitioners in the field.Become a Stock Technical Analysis Expert in this Practical Course with PythonRead or download S&P 500 Index ETF prices data and perform technical analysis operations by installing related packages and running code on Python IDE.Compute lagging stock technical indicators or overlays such as moving averages, Bollinger bands, parabolic stop and reverse.Calculate leading stock technical indicators or oscillators such as average directional movement index, commodity channel index, moving averages convergence/divergence, rate of change, relative strength index, stochastic oscillator and Williams %R.Determine single technical indicator based stock trading opportunities through price, double, bands, centerline and signal crossovers.Define multiple technical indicators based stock trading occasions through price crossovers confirmed by bands crossovers.Outline long (buy) or short (sell) stock trading strategies based on single or multiple technical indicators trading openings.Evaluate stock trading strategies performances by comparing them against buy and hold benchmark.Become a Stock Technical Analysis Expert and Put Your Knowledge in PracticeLearning stock technical analysis is indispensable for finance careers in areas such as equity research and equity trading. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors stock technical trading research and development.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for back-testing to achieve greater effectiveness.Content and OverviewThis practical course contains 45 lectures and 8.5 hours of content. Its designed for all stock technical analysis knowledge levels and a basic understanding of Python programming language is useful but not required.At first, youll learn how to read or download S&P 500 Index ETF prices historical data to perform technical analysis operations by installing related packages and runningcode on Python IDE.Next, youll calculate lagging stock technical indicators such as simple moving averages (SMA), exponential moving averages (EMA), Bollinger bands(BB), parabolic stop and reverse (SAR). After that, youll compute leading stock technical indicators such as average directional movement index (ADX), commodity channel index (CCI), moving averages convergence/divergence (MACD), rate of change (ROC), relative strength index (RSI), stochastic oscillator (Full STO) and Williams %R.Then, youll define single technical indicator based stock trading openings through price, double, bands and signal crossovers. Next, youll determine multiple technical indicators based trading opportunities through price crossovers which need to be confirmed by second technical indicator band crossover. Later, youll give shape to stock trading strategies which are long (buying) or short (selling) using single or multiple technical indicators trading occasions.Finally, youll evaluate stock trading strategies performance with buy and hold as initial benchmark and comparing their annualized return for performance, annualized standard deviation for volatility or risk and annualized Sharpe ratio for risk adjusted return."
Price: 49.99 |
| "Stock Technical Analysis with Excel" |
| "Full Course Content Last Update 06/2019Learn stock technical analysis through a practical course with Microsoft Excel using S&P 500 Index ETF historical data for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do research as experienced investor. All of this while referencing best practitioners in the field.Become a Stock Technical Analysis Expert in this Practical Course with ExcelCompute lagging stock technical indicators or overlays such as moving averages, Bollinger bands, parabolic stop and reverse.Calculate leading stock technical indicators or oscillators such as average directional movement index, commodity channel index, moving averages convergence/divergence, rate of change, relative strength index, stochastic momentum index, stochastic oscillator and Williams %R. Determine single technical indicator based stock trading opportunities through price, double, bands, centerline and signal crossovers.Define multiple technical indicators based stock trading occasions through price crossovers confirmed by bands crossovers.Outline long-only stock trading strategies based on single or multiple technical indicators trading openings.Assess stock trading strategies performance by comparing their annualized return, standard deviation and Sharpe ratio against buy and hold benchmark.Become a Stock Technical Analysis Expert and Put Your Knowledge in PracticeLearning stock technical analysis is indispensable for finance careers in areas such as equity research and equity trading. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors stock technical trading research and development.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for back-testing to achieve greater effectiveness.Content and OverviewThis practical course contains 43 lectures and 8 hours of content. Its designed for all stock technical analysis knowledge levels and a basic understanding of Microsoft Excel is useful but not required.At first, youll learn how to perform stock technical analysis operations using Microsoft Excel built-in functions and array calculations. Next, youll calculate lagging stock technical indicators such as simple moving averages, exponential moving averages, Bollinger bands, parabolic stop and reverse. After that, youll compute leading stock technical indicators such as average directional movement index, commodity channel index, moving averages convergence/divergence, rate of change, relative strength index, stochastic momentum index, stochastic oscillator and Williams %R.Then, youll define single technical indicator based stock trading openings through price, double, bands, centerline and signal crossovers. Next, youll determine multiple technical indicators based trading opportunities through price crossovers which need to be confirmed by second technical indicator band crossover. Later, youll give shape to long-only stock trading strategies using single or multiple technical indicators trading occasions.Finally, youll evaluate stock trading strategies performance with buy and hold as initial benchmark and comparing their annualized return for performance, annualized standard deviation for volatility or risk and annualized Sharpe ratio for risk adjusted return."
Price: 49.99 |
| "Forecasting Models with Python" |
| "Full Course Content Last Update 06/2018Learn forecasting models through a practical course with Python programming language using S&P 500 Index ETF prices historical data. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. All of this while exploring the wisdom of best academics and practitioners in the field.Become a Forecasting Models Expert in this Practical Course with PythonRead S&P 500 Index ETF prices data and perform forecasting models operations by installing related packages and running code on Python PyCharm IDE.Estimate simple forecasting methods such as arithmetic mean, random walk, seasonal random walk and random walk with drift.Evaluate simple forecasting methods forecasting accuracy through mean absolute error and root mean squared error scale-dependent metrics.Approximate simple moving averages and exponential smoothing methods with no trend or seasonal patterns such as Brown simple exponential smoothing method.Estimate exponential smoothing methods with only trend patterns such as Holt linear trend, exponential trend, Gardner additive damped trend and Taylor multiplicative damped trend methods.Approximate exponential smoothing methods with trend and seasonal patters such as Holt-Winters additive seasonality and Holt-Winters multiplicative seasonality methods.Select exponential smoothing method with lowest Akaike and Schwarz Bayesian information loss criteria.Asses simple moving average and exponential smoothing methods forecasting accuracy through mean absolute error and root mean squared error scale-dependent metrics.Identify Box-Jenkins autoregressive integrated moving average model integration order through level and differentiated first order trend stationary time series augmented Dickey-Fuller unit root test.Recognize autoregressive integrated moving average model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions.Estimate non-seasonal autoregressive integrated moving average models such as random walk with drift, differentiated first order autoregressive, Brown simple exponential smoothing, Holt linear trend and Gardner additive damped trend models.Approximate seasonal autoregressive integrated moving average models such as seasonal random walk with drift, seasonally differentiated first order autoregressive and Holt-Winters additive seasonality models.Choose autoregressive integrated moving average model with lowest Akaike and Schwarz Bayesian information loss criteria.Evaluate autoregressive integrated moving average models forecasting accuracy through mean absolute error and root mean squared error scale-dependent metrics.Assess highest forecasting accuracy autoregressive integrated moving average model residuals or forecasting errors white noise requirement through Ljung-Box lagged autocorrelation test.Become a Forecasting Models Expert and Put Your Knowledge in PracticeLearning forecasting models is indispensable for business or financial data science applications in areas such as sales and financial forecasting, inventory optimization, demand and operations planning, and cash flow management. It is also essential for academic careers in data science, applied statistics, operations research, economics, econometrics and quantitative finance. And its necessary for business forecasting research.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for forecast modelling to achieve greater effectiveness.Content and OverviewThis practical course contains 41 lectures and 5.5 hours of content. Its designed for all forecasting models knowledge levels and a basic understanding of Python programming language is useful but not required.At first, youll learn how to read S&P 500 Index ETF prices historical data to perform forecasting models operations by installing related packages and running code on Python PyCharm IDE.Then, youll define simple forecasting methods such as arithmetic mean, random walk, seasonal random walk and random walk with drift. Next, youll evaluate simple methods forecasting accuracy through mean absolute error and root mean squared error scale-dependent metrics.Next, youll define simple moving averages and exponential smoothing methods. For exponential smoothing methods with no trend or seasonal patterns, youll define Brown simple exponential smoothing method. For exponential smoothing methods with only trend patterns, youll define Holt linear trend, exponential trend, Gardner additive damped trend and Taylor multiplicative damped trend methods. For exponential smoothing methods with trend and seasonal patterns, youll define Holt-Winters additive seasonality and Holt-Winters multiplicative seasonality methods. After that, youll select exponential smoothing method with lowest Akaike and Schwarz Bayesian information loss criteria. Later, youll evaluate simple moving average and exponential smoothing methods forecasting accuracy through mean absolute error and root mean squared error scale-dependent metrics.After that, youll define Box-Jenkins autoregressive integrated moving average models. Then, youll identify autoregressive integrated moving average model integration order through level and differentiated time series first order trend stationary augmented Dickey-Fuller unit root test. Next, youll identify autoregressive integrated moving average model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions. For non-seasonal autoregressive integrated moving average models, youll define random walk with drift, differentiated first order autoregressive, Brown simple exponential smoothing, Holt linear trend and Gardner additive damped trend models. For seasonal autoregressive integrated moving average models, youll define seasonal random walk with drift, seasonally differentiated first order autoregressive and Holt-Winters additive seasonality models. After that, youll select autoregressive integrated moving average model with lowest Akaike and Schwarz Bayesian information loss criteria. Later, youll evaluate models forecasting accuracy through mean absolute error and root mean squared error scale-dependent metrics. Finally, youll assess highest forecasting accuracy autoregressive integrated moving average model residuals or forecasting errors white noise requirement through Ljung-Box lagged autocorrelation test."
Price: 49.99 |
| "Investment Portfolio Analysis with R" |
| "Full Course Content Last Update 01/2018Learn investment portfolio analysis through a practical course with R statistical software using index replicating ETFs and Mutual Funds historical data for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.Become an Investment Portfolio Analysis Expert in this Practical Course with RRead or download main asset classes benchmark indexes replicating funds data to perform investment portfolio analysis operations by installing related packages and running script code on RStudio IDE.Compare main asset classes benchmark indexes replicating funds returns and risks tradeoffs for cash, bonds, stocks, commodities, real estate and currencies.Estimate portfolio expected returns, historical and market participants implied volatility.Approximate portfolio expected excess returns using capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT).Hedge portfolio systematic risk through options trading strategies benchmark indexes replicating funds.Evaluate hedge fund index performance and assess portfolio returns and risks amplification through leverage.Calculate portfolio performance metrics such as Sharpe, Treynor, Sortino, and Kelly ratios.Estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers.Optimize global portfolios asset allocation weights for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives within training range based on Markowitz portfolio theory.Approximate global portfolios returns from periodically rebalanced optimized asset allocations within testing range and compare them with equal weighted and well-known investment managers benchmark portfolios.Evaluate global portfolios performance through global risk factors model and estimate their expected return, expected excess return and expected return contribution from global risk factors exposure while assessing investment costs impact on portfolio performance.Become an Investment Portfolio Analysis Expert and Put Your Knowledge in PracticeLearning investment portfolio analysis is indispensable for finance careers in areas such as asset management, private wealth management, and risk management within institutional investors represented by banks, insurance companies, pension funds, hedge funds, investment advisors, endowments and mutual funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors optimized asset allocation strategies research and development.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using index replicating funds historical data for back-testing and to achieve greater effectiveness.Content and OverviewThis practical course contains 44 lectures and 6.5 hours of content. Its designed for all investment portfolio analysis knowledge levels and a basic understanding of R statistical software is useful but not required.At first, youll learn how to read or download index replicating funds historical data to perform investment portfolio analysis operations by installing related packages and running script code on RStudio IDE.Then, youll define main asset classes by comparing their benchmark indexes replicating funds returns and risks tradeoffs. After that, youll segment main asset classes into traditional and alternative ones. For traditional asset classes, youll define cash and cash equivalents, fixed income or bonds and equities or stocks. Regarding cash and cash equivalents traditional asset class, youll use U.S. total money market benchmark index replicating fund. Regarding cash and cash equivalents traditional asset class, youll use U.S. total money market benchmark index replicating fund is used. Regarding fixed income or bonds traditional asset class, U.S. total bond market, U.S. short term bond market, U.S. long term bond market and international total bond market benchmark indexes replicating funds. Regarding equities or stocks traditional asset class, youll use U.S. total stock market, U.S. large cap stock market, U.S. small cap stock market, U.S. small cap growth stock market, U.S. small cap value stock market, international total stock market, international developed stock market and international emerging stock market benchmark indexes replicating funds. For alternative asset classes, youll define commodities, real estate and currencies or foreign exchange. Regarding commodities alternative asset class, youll use oil and gold prices benchmark indexes replicating funds. Regarding real estate alternative asset class, youll use U.S. real estate investment trust market benchmark index replicating fund. Regarding currencies or foreign exchange alternative asset class, youll use U.S. dollar major currencies benchmark index replicating fund.Next, youll define returns and risks using U.S. large cap stocks market benchmark index replicating fund. After that, youll calculate expected returns through historical returns mean and media. Then, youll estimate risks through historical returns standard deviation, mean absolute deviation and market participants implied volatility. Later, youll approximate portfolio expected excess returns through capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT). Next, youll hedge portfolio systematic risk through options trading strategies and evaluate hedge fund index performance together with the assessment of returns and risks amplification through portfolio leverage.After that, youll define portfolio optimization through global assets allocation. Next, youll calculate Sharpe ratio, Treynor ratio, Sortino ratio and Kelly ratio portfolio performance metrics. Then, youll estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers. Later, youll optimize global asset allocation weights within training range for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives based on Markowitz portfolio theory. After that, youll calculate global portfolio returns within testing range using previously optimized periodically rebalance asset allocation weights and compared with equal weighted and well-known investment managers benchmark portfolios.Later, youll evaluate optimized portfolios performance through global risk factors model. After that, youll estimate optimized portfolios expected returns, expected excess returns and global risk factors exposure returns contribution. Finally, youll assess investment costs impact on portfolio performance."
Price: 49.99 |
| "Investment Portfolio Analysis with Python" |
| "Full Course Content Last Update 03/2018Learn investment portfolio analysis through a practical course with Python programming language using index replicating ETFs and Mutual Funds historical data for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.Become an Investment Portfolio Analysis Expert in this Practical Course with PythonRead or download main asset classes benchmark indexes replicating funds data to perform investment portfolio analysis operations by installing related packages and running code on Python IDE.Compare main asset classes benchmark indexes replicating funds returns and risks tradeoffs for cash, bonds, stocks, commodities, real estate and currencies.Estimate portfolio expected returns, historical and market participants implied volatility.Approximate portfolio expected excess returns using capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT).Hedge portfolio systematic risk through options trading strategies benchmark indexes replicating funds.Evaluate hedge fund index performance and assess portfolio returns and risks amplification through leverage.Calculate portfolio performance metrics such as Sharpe, Treynor, Sortino, and Kelly ratios.Estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers.Optimize global portfolios asset allocation weights for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives within training range based on Markowitz portfolio theory.Approximate global portfolios returns from periodically rebalanced optimized asset allocations within testing range and compare them with equal weighted and well-known investment managers benchmark portfolios.Evaluate global portfolios performance through global risk factors model and estimate their expected return, expected excess return and expected return contribution from global risk factors exposure while assessing investment costs impact on portfolio performance.Become an Investment Portfolio Analysis Expert and Put Your Knowledge in PracticeLearning investment portfolio analysis is indispensable for finance careers in areas such as asset management, private wealth management, and risk management within institutional investors represented by banks, insurance companies, pension funds, hedge funds, investment advisors, endowments and mutual funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors optimized asset allocation strategies research and development.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using index replicating funds historical data for back-testing and to achieve greater effectiveness.Content and OverviewThis practical course contains 44 lectures and 7.5 hours of content. Its designed for all investment portfolio analysis knowledge levels and a basic understanding of Python programming language is useful but not required.At first, youll learn how to read or download index replicating funds historical data to perform investment portfolio analysis operations by installing related packages and running code on Python IDE.Then, youll define main asset classes by comparing their benchmark indexes replicating funds returns and risks tradeoffs. After that, youll segment main asset classes into traditional and alternative ones. For traditional asset classes, youll define cash and cash equivalents, fixed income or bonds and equities or stocks. Regarding cash and cash equivalents traditional asset class, youll use U.S. total money market benchmark index replicating fund. Regarding cash and cash equivalents traditional asset class, youll use U.S. total money market benchmark index replicating fund is used. Regarding fixed income or bonds traditional asset class, U.S. total bond market, U.S. short term bond market, U.S. long term bond market and international total bond market benchmark indexes replicating funds. Regarding equities or stocks traditional asset class, youll use U.S. total stock market, U.S. large cap stock market, U.S. small cap stock market, U.S. small cap growth stock market, U.S. small cap value stock market, international total stock market, international developed stock market and international emerging stock market benchmark indexes replicating funds. For alternative asset classes, youll define commodities, real estate and currencies or foreign exchange. Regarding commodities alternative asset class, youll use oil and gold prices benchmark indexes replicating funds. Regarding real estate alternative asset class, youll use U.S. real estate investment trust market benchmark index replicating fund. Regarding currencies or foreign exchange alternative asset class, youll use U.S. dollar major currencies benchmark index replicating fund.Next, youll define returns and risks using U.S. large cap stocks market benchmark index replicating fund. After that, youll calculate expected returns through historical returns mean and media. Then, youll estimate risks through historical returns standard deviation, mean absolute deviation and market participants implied volatility. Later, youll approximate portfolio expected excess returns through capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT). Next, youll hedge portfolio systematic risk through options trading strategies and evaluate hedge fund index performance together with the assessment of returns and risks amplification through portfolio leverage.After that, youll define portfolio optimization through global assets allocation. Next, youll calculate Sharpe ratio, Treynor ratio, Sortino ratio and Kelly ratio portfolio performance metrics. Then, youll estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers. Later, youll optimize global asset allocation weights within training range for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives based on Markowitz portfolio theory. After that, youll calculate global portfolio returns within testing range using previously optimized periodically rebalance asset allocation weights and compared with equal weighted and well-known investment managers benchmark portfolios.Later, youll evaluate optimized portfolios performance through global risk factors model. After that, youll estimate optimized portfolios expected returns, expected excess returns and global risk factors exposure returns contribution. Finally, youll assess investment costs impact on portfolio performance."
Price: 49.99 |
| "Regression Machine Learning with Python" |
| "Full Course Content Last Update 07/2018Learn regression machine learning through a practical course with Python programming language using S&P 500 Index ETF prices historical data for algorithm learning. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. All of this while exploring the wisdom of best academics and practitioners in the field.Become a Regression Machine Learning Expert in this Practical Course with PythonRead S&P 500 Index ETF prices data and perform regression machine learning operations by installing related packages and running code on Python IDE.Create target and predictor algorithm features for supervised regression learning task.Select relevant predictor features subset through Student t-test, ANOVA F-test, false discovery rate and family-wise error rate univariate filter methods.Choose relevant predictor features subset through recursive feature elimination deterministic wrapper method.Designate relevant predictor features subset through least absolute shrinkage and selection operator embedded method.Extract predictor features transformations through principal component analysis.Train algorithm for mapping optimal relationship between target and predictor features.Test algorithm for evaluating previously optimized relationship forecasting accuracy through mean absolute error and root mean squared error scale-dependent metrics.Calculate generalized linear models such as linear regression or Ridge regression and select optimal linear regression coefficients regularization parameter through time series cross-validation.Compute similarity methods such as k nearest neighbors and select optimal number of nearest neighbors parameter through time series cross-validation.Estimate frequency methods such as decision tree and select optimal maximum tree depth parameter through time series cross-validation.Calculate ensemble methods such as random forest or gradient boosting machine and select optimal maximum trees depth parameter through time series cross-validation.Compute maximum margin methods such as linear or non-linear support vector machines and select optimal error term penalization parameter through time series cross-validation.Estimate multi-layer perceptron methods such as artificial neural network and select optimal node connection weight decay regularization parameter through time series cross-validation.Compare regression machine learning algorithms training and testing.Become a Regression Machine Learning Expert and Put Your Knowledge in PracticeLearning regression machine learning is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. And it is necessary for business forecasting research.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for algorithm learning to achieve greater effectiveness.Content and OverviewThis practical course contains 56 lectures and 6 hours of content. Its designed for all regression machine learning knowledge levels and a basic understanding of Python programming language is useful but not required.At first, youll learn how to read S&P 500 Index ETF prices historical data to perform regression machine learning operations by installing related packages and running code on Python IDE.Then, youll define algorithm features by creating target and predictor variables for supervised regression learning task. Next, youll only include relevant predictor features subset or transformations in algorithm learning through features selection and features extraction procedures. For features selection, youll define univariate filter methods, deterministic wrapper methods and embedded methods. For univariate filter methods, youll implement Student t-test, ANOVA F-test, false discovery rate and family-wise error rate. For deterministic wrapper methods, youll implement recursive feature elimination. For embedded methods, youll implement least absolute shrinkage and selection operator or lasso. For features extraction, youll implement principal component analysis. After that, youll define algorithm training through mapping optimal relationship between target and predictor features within training range. For algorithm training, optimal parameters selection or fine tuning, bias-variance trade-off, optimal model complexity and time series cross-validation are defined. Later, youll define algorithm testing through evaluating previously optimized relationship forecasting accuracy through scale-dependent metrics within testing range. For scale-dependent metrics, youll define mean absolute error and root mean squared error.After that, youll define generalized linear models such as linear regression and Ridge regression. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and linear regression coefficients regularization optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent error metrics within testing range.Then, youll define similarity methods such as k nearest neighbors. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and number of nearest neighbors optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent error metrics within testing range.After that, youll define frequency methods such as decision tree. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and maximum tree depth optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent error metrics within testing range.Then, youll define ensemble methods such as random forest and gradient boosting machine. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and maximum tree depth optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent error metrics within testing range.After that, youll define maximum margin methods such as linear and non-linear or radial basis function support vector machines. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and error term penalization optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent error metrics within testing range.Then, youll define multi-layer perceptron methods such as artificial neural network. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and node connection weight decay regularization optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent error metrics within testing range. Finally, youll compare regression machine learning algorithms training and testing."
Price: 49.99 |
| "Regression Machine Learning with R" |
| "Full Course Content Last Update 07/2018Learn regression machine learning through a practical course with R statistical software using S&P 500 Index ETF prices historical data for algorithm learning. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. All of this while exploring the wisdom of best academics and practitioners in the field.Become a Regression Machine Learning Expert in this Practical Course with RRead S&P 500 Index ETF prices data and perform regression machine learning operations by installing related packages and running code on RStudio IDE.Create target and predictor algorithm features for supervised regression learning task.Select relevant predictor features subset through Student t-test and ANOVA F-test univariate filter methods.Choose relevant predictor features subset through recursive feature elimination deterministic wrapper method. Designate relevant predictor features subset through least absolute shrinkage and selection operator embedded method.Extract predictor features transformations through principal component analysis.Train algorithm for mapping optimal relationship between target and predictor features.Test algorithm for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics.Calculate generalized linear models such as linear regression or elastic net regression and select optimal linear regression coefficients regularization parameter through time series cross-validation.Compute similarity methods such as k nearest neighbors and select optimal number of nearest neighbors parameter through time series cross-validation.Estimate frequency methods such as decision tree and select optimal maximum tree depth parameter through time series cross-validation.Calculate ensemble methods such as random forest or extreme gradient boosting machine and select optimal number of randomly selected predictors or maximum trees depth parameter through time series cross-validation.Compute maximum margin methods such as linear or non-linear support vector machines and select optimal error term penalization parameter through time series cross-validation.Estimate multi-layer perceptron methods such as artificial neural network and select optimal node connection weight decay regularization parameter through time series cross-validation.Compare regression machine learning algorithms training and testing.Become a Regression Machine Learning Expert and Put Your Knowledge in PracticeLearning regression machine learning is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. And it is necessary for business forecasting research.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for algorithm learning to achieve greater effectiveness.Content and OverviewThis practical course contains 56 lectures and 5.5 hours of content. Its designed for all regression machine learning knowledge levels and a basic understanding of R statistical software is useful but not required.At first, youll learn how to read S&P 500 Index ETF prices historical data to perform regression machine learning operations by installing related packages and running script code on RStudio IDE.Then, youll define algorithm features by creating target and predictor variables for supervised regression learning task. Next, youll only include relevant predictor features subset or transformations in algorithm learning through features selection and features extraction procedures. For features selection, youll define univariate filter methods, deterministic wrapper methods and embedded methods. For univariate filter methods, youll implement Student t-test and ANOVA F-test. For deterministic wrapper methods, youll implement recursive feature elimination. For embedded methods, youll implement least absolute shrinkage and selection operator or lasso. For features extraction, youll implement principal component analysis. After that, youll define algorithm training through mapping optimal relationship between target and predictor features within training range. For algorithm training, optimal parameters selection or fine tuning, bias-variance trade-off, optimal model complexity and time series cross-validation are defined. Later, youll define algorithm testing through evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics within testing range. For scale-dependent metrics, youll define mean absolute error and root mean squared error. For scale-independent metrics, youll define mean absolute percentage error.After that, youll define generalized linear models such as linear regression and elastic net regression. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and linear regression coefficients regularization optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range.Then, youll define similarity methods such as k nearest neighbors. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and number of nearest neighbors optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range.After that, youll define frequency methods such as decision tree. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and maximum tree depth optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range.Then, youll define ensemble methods such as random forest and extreme gradient boosting machine. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and number of randomly selected predictors or maximum tree depth optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range.After that, youll define maximum margin methods such as linear and non-linear or radial basis function support vector machines. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and error term penalization optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range.Then, youll define multi-layer perceptron methods such as artificial neural network. Next, youll implement algorithm training for mapping optimal relationship between target and predictor features within training range. For algorithm training, youll use only relevant predictor features subset or transformations through principal component analysis procedure and node connection weight decay regularization optimal parameter estimation or fine tuning through time series cross-validation. Later, youll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent error metrics within testing range. Finally, youll compare regression machine learning algorithms training and testing."
Price: 49.99 |
| "Como configurar una red de datos desde Cero de forma fcil" |
| "Cansado de no entender como funciona una red de datos?Quieres configurar una red de datos y no sabes por donde empezar?Tienes problemas en tu red de casa u oficina y no sabes cul es el problema?Bienvenidos al curso de ""Como configurar una red de datos desde Cero de forma fcil"" el curso en el que aprenderspaso a pasoy de una formafcil yestructuradalas basestericasde como configurar una red de datos desde Cero de forma fcil. Daremos nfasis en los siguientes tpicos del aprendizaje:Elementos fsicos de una Red de DatosConfiguracin de una Red de Datos con emulador de redResolucin de problemas de Redes de DatosMontaje de un proyecto final con emulador Cisco Packet TracerEn este curso aprenders conceptos muy importantes para entender el funcionamiento de una red, como son los elementos de una red, conceptos tcnicos bsicos, como se direccionan los equipos en una red por medio de direcciones IP, como se configura un switch y un router, revisar servicios de red como DNS, DHCP, entre otros servicios cruciales para el funcionamiento de una red de datos. La estructura de este curso est pensada para un fcil entendimiento del funcionamiento lgico de una red de datos, no cubre aspectos de instalacin de una red, pero si todos los componentes de una red de datos, con prctica de configuracin en un simulador de red como es Cisco Packet Tracer Al final del curso realizaremos un proyecto de una red con la ayuda del emulador de redes. Usaremos tanto equipos de comunicaciones, dispositivos finales y servidores simulados, que permitir a los estudiantes experimentar con el comportamiento de una red de rea local.Sin duda alguna, este curso te guiar paso a paso por el mundo de las redes, podrs contar con un conocimiento sin igual, que te permitir avanzar en tu aprendizaje, en tu trabajo y en tu negocio, No te lo puedes perder!No esperes ms y empecemos a aprender juntos desde hoy mismo!"
Price: 49.99 |
| "Guia bsica de Cisco Packet Tracer para el CCNA" |
| "Ests listo para el examen CCNA?Te has preparado los suficiente con prcticas de laboratorio?No corras riesgos y preprate aprendiendo la magia de Cisco Packet TracerAprende a usar el potente simulador de redes Cisco Packet Tracer y revisa en detalle el comportamiento de las redes, como se configuran cada uno de los elementos de una red, como interactan los protocolos, como es el funcionamiento de equipos clientes, Switch, Router, Servidores, como se configuran servicios de Red como DNS, DHCP y Web. Iniciamos con la introduccin del uso de Cisco Packet Tracer 6.2 (valido para 7.0, 7.1 y 7.2), revisamos los entornos de trabajo y sus modos de funcionamiento.Realizaremos prcticas y laboratorios en la configuracin de redes, podremos revisar y validar conceptos, y sistemas de redes tcnicos y complejos, por medio de la herramienta Cisco Packet Tracer.Con este curso podrs prepararte para rendir los exmenes de Cisco CCNA y CCNP, ya que podrs contar con el conocimiento y prctica de esta vital herramienta de simulacin de redes.No esperes ms y empecemos a aprender juntos desde hoy mismo!"
Price: 49.99 |
| "Cisco CCNA 200-125 - Practicas de configuracion en espaol" |
| "Eres un profesional dentro del rea de las tecnologas de la informacin y quieres Certificarte en Redes Cisco?Buscando un curso que no solo tenga teora si no que tambin puedas realizar practicas para afianzar tus conocimientos?Cansado de no entender como funcionan los protocolos de capa 2 y 3, adems de routing y todo lo que conlleva a la configuracin de equipos Cisco?Bienvenido al curso de ""Cisco CCNA 200-125 - Practica de configuracinen espaol"",curso que te preparar para obtener los conocimientos bsicosy avanzados para la preparacin del examen ""Cisco CCNA Routing & Switching 200-125"".Las certificaciones Cisco CCNA es una de las importantes en la industria de las tecnologas de la informacin y que te abrir numerosas puertas de oportunidades en el mundo laboral.En este curso podrs realizar prcticas de configuracin de dispositivos Cisco, por medio del software de simulacin de redes llamado ""Cisco Packet Tracer"" y enfrentar as de mejor manera el examen de certificacin de Cisco.Este curso se enfoca en la explicacin de todos los tpicos que se preguntan en el examen CCNA 200-125 y que practicaras de manera simultnea en cada clase.Todo el material de estudio, est pensado para la certificacin Cisco, como adems de su aplicacin en el trabajo diario de tu vida laboral, como la configuracin de Switch y Router Cisco.La base del curso se base en la Curricula Completa como son:Fundamentos de RedesTecnologas LAN SwitchingTecnologas RoutingTecnologas WANInfraestructura de ServiciosInfraestructura de SeguridadInfraestructura de AdministracinEste curso es de Gran Utilidad para la certificacin Cisco CCNA, que se preocupa de la practica de todas las clases para un mayor entendimiento y que sea de forma fcil y amena. Este curso es complementario para a los cursos oficiales de Cisco, y pretende ser una ayuda para la comprensin por medio de laboratorios asociado a cada concepto que se va aprendiendoNo esperes ms y empecemos a aprender juntos desde hoy mismo! Aprendamos Practicando!"
Price: 69.99 |
| "Kingdom Glory: Release the Presence of God as a Christian" |
| "Hello, Welcome to the Kingdom Glory training series which is a highly interactive course that will propel you into the Glory and train you how to release the Kingdom of God in an unprecedented fashion! In these sessions there are three main themes throughout the course: Learning your Authority, Learning your Identity and Learning the Heart of God. Along with these teachings you will also have powerful activation's to put into practice what you have just learned to make it a part of your life. Also, there is a session that is a complete Bethel Treasure Hunt training where you will be trained in the gifts of the Spirit with an actual group to learn how to release God into any situation in life so that revival becomes a normal lifestyle!About the Author: Alexander Logia is a Bethel School of Supernatural Ministry graduate and is currently a ministry Associate with Kevin Dedmon and Jason Chin. Recently married, Alexander lives in Redding as an overseer for Treasure Hunts and Firestarters at Bethel Church. He has been doing crusades throughout Africa and Philippines with a passion to see worldwide harvest through the preaching of the gospel in a demonstration of the Holy Spirit and power. Thousands of people have been healed, saved and delivered in his ministry in various nations around the world-Fund Revival Around the World: As you join this training series you will also be sowing into the ministry and helping fund crusades and missions work in various nations. We ultimately want to take this glorious gospel to all creation and see Jesus get His full reward! Thank you for your support and we pray that you are abundantly blessed !"
Price: 24.99 |
| "Activate Your Miracle Consciousness" |
| "You may have been trying for a really long time to find the place within where miracles become an everyday occurrence so you can step into your greatest potential and access all of your longest held dreams, desires, and wishes.I have learned through over 20 years of research, study, and practice how to turn my life into a life where miracles surround me every day. I have learned how to tap into my own inner abundance to create what I call Miracle Consciousness.I would love to share with you what I have learned so you can turn your life into one that generates miracles everyday.In this course you will learn how to activate your Miracle Consciousness in an easy to follow step-by-step process. Once you understand how to embody your own Miracle Consciousness, you will never look outside of yourself again to bring anything into your life. You will manifest things with ease and grace and be living a joyful life always. You Have the Power to Tap Into Your Inner Reserves of Abundance""This course is taught in an easy to understand step-by-step video format that will guide you to the place within that all your miracles stem from.Create a Magical Life with Ease""Manifest things you have been wanting for a really long timeStep into your greatest potentialLive in freedom, passion, happiness, and joy everydayTap into your inner abundanceMake your outer life reflect your new inner stateContinue building the energy of your creationMagnetize with your heart energyMove through uncomfortable emotions with easeHave an inner foundation on which you can always rely"
Price: 19.99 |
| "Spiritual Awakening Mastery Course" |
| "The Spiritual Awakening Mastery Course is designed to take you step-by-step through your spiritual awakening process. If you are experiencing a spiritual awakening, you know it can be challenging. In this course you will learn how to move through difficulties with ease. If you have an awareness of what is going on, then you have the ability to change your circumstances into what you are wanting. This course has 8 modules and over 3.5 hours of instruction. In this course you will learn: Why you are awakeningStages of a spiritual awakeningSymptoms of an awakening and how to healYour spiritual giftsHandling practical mattersDivine relationshipsLiving your missionManifest with Ease This course will teach you to live your truth and follow your heart. You are discovering yourself on a deep inner level and are transforming out of old patterning and beliefs. Loving yourself is an important part of the spiritual awakening journey. You will learn through this course how to love you and treat yourself gently and with care always. Be everything that you are and love yourself deeply. Allow yourself to play and be in a state of wonder and excitement every day, that is what this spiritual journey is taking you into. You must take this course if you are experiencing a spiritual awakening and need guidance on how you can get through it while being happy, centered, and empowered."
Price: 19.99 |
| "Peters' Pointers on Piano Hymn Arrangements" |
| "I've identified a few techniques and important areas from 45 years of piano playing experience, which may shorten the time necessary to become proficient at composing a piano arrangement. This course consists of a survey of chords to create an emotional atmosphere in your arrangements. Then, I reveal some "tricks to the trade" that I've found helpful in making my own arrangements through the years. We will discuss and practice some textures that will complement the emotional language of the arrangements that you will create. As we progress, there will be some basic projects, which you can discuss with me through Udemy. To guarantee that this can be accomplished, I am committed to help you develop your arranging skill through the Udemy contact methods."
Price: 19.99 |
| "Ionic Framework 101: A Quickstart to Building Mobile Apps" |
| "This course will teach you how to develop web-hybrid mobile applications for Android and iOS using Ionic Framework. We'll look at what is necessary to configure Ionic Framework on your Windows, Linux or Macintosh computer and develop visually pleasing applications with minimal amounts of code and design skills necessary.Ionic Framework 101 is designed to be a quickstart for beginners looking to develop mobile applications. As a course goal, we'll create a fully functional URL shortener using the skills we developed through the span of the course."
Price: 24.99 |
| "NativeScript 101: A Quickstart to Building Mobile Apps" |
| "Have you ever wanted to build your very own mobile Android or iOS application? Already have some working knowledge of web development technologies like JavaScript, HTML, and CSS? Using NativeScript you can bring the web world and mobile world together.This course will teach you how to easily develop hybrid mobile applications for Android and iOS using NativeScript by Telerik. We'll look at what is necessary to configure NativeScript on your Windows, Linux or Macintosh computer and develop visually pleasing applications with minimal amounts of code and design skills necessary.NativeScript 101 is designed to be a quickstart for beginners looking to develop mobile applications. This course will put you track for building mobile applications using common web technologies such as JavaScript, XML, and CSS. We'll see everything from designing application views to performing HTTP requests against remote web services. As a course goal, we'll create a fully functional URL shortener using the skills we developed through the span of the course."
Price: 24.99 |
| "Native Android 101: A Quickstart to Building Android Apps" |
| "Have you ever wanted to build your very own mobile Android application? Already have some working knowledge of the Java programming language? Using the native Android SDK you can bring the Java world and mobile world together.This course will teach you how to easily develop native Android applications using Java and the Android SDK. We'll look at what is necessary to configure the Android SDK on your Windows, Linux or Macintosh computer and develop visually pleasing applications with minimal amounts of code and design skills necessary.Native Android 101 is designed to be a quickstart for beginners looking to develop mobile applications. This course will put you track for building mobile Android applications using common technologies such as Java, and XML. We'll see everything from designing application views to performing HTTP requests against remote web services. As a course goal, we'll create a fully functional URL shortener using the skills we developed through the span of the course.By the end of this course, students should have the skills necessary to build native Android applications on their own."
Price: 24.99 |
| "Animating Traditionally with Toon Boom" |
| "This course was originally created for students participating in Skills Ontario's Animation Challenge (similar to the 11 Second Club), as a way to quickly get them comfortable with both the fundamentals of traditional animation as well as practical knowledge of Toon Boom's software. To do this, the course is designed to walk through the completion of a scene starting from analyzing dialogue, to story-boarding, to creating actual animation, while being sure to emphasize the core principles of animation.With this in mind, the course is best suited for animation students that are either new to the medium, or those looking to bring their traditional skills to the digital realm, using Toon Boom. While there are many videos available online that feature Toon Boom's software, most of them focus on cut-out style animation, or puppets, rather than traditional frame-by-frame style animation. After getting many remarks from even industry pros surprised that Toon Boom can actually not only support traditional animation, but do it amazingly well, I felt it high time for some video tutorials focussing on the basics.This course features Toon Boom's Storyboard Pro, and Harmony 11, industry standards in animation software. Although, to keep up with all the version changes, I've made sure to focus on features that are also available in both older and newer versions of Harmony (including Harmony 17), as well Toon Boom's former Animate, Animate Pro, applications. And for those absolute beginners, there's still plenty of information that can be used, whether you're still drawing with a pencil, or deciding what type of software to pick up for your next animated project.This course includes 19, densely-packed lessons, over the span of exactly an hour.A great way to fast-track your working knowledge of both animation and Toon Boom basics!"
Price: 24.99 |
| "Be More Successful in Your Relationships - at Work and Home!" |
| "Quality relationships are fundamental to success at home and work but the problem is that we don't have a very good set of tools for understanding them. When things go well we use phrases like 'we just get along', or 'we just seem to click' - and when things go wrong, we say 'its just a clash' or 'she drives me mad' or 'its no good, there's no point talking to him!'.So, what if you had a better way of making sense of things? Just like any skill you've learnt in life - it started with understanding - the notes in music, the ingredients in a recipe, the tools for DIY, the rules of the road. In this course you'll learn a brilliant way of making sense of the people situations in your life.Improve your impact and effectiveness at home and workWhether you're interested in people or just find some of them really difficult(!) - change is possible once you understand what's going on. This is true for men and women, young or old. Discover the three effective psychological 'states' that we all have Learn to read these 'states' in yourself and others Reduce the time you spend in the ineffective 'states' Understand how to better influence others The good news is that you already have what it takes to be successful in your relationships - it's about recognising the effective states and spending more time in them - a 10% increase could make the world of difference to you.Contents and OverviewWithin just two videos you'll get the idea. That's how simple it is. By the end of Section One, you'll be starting to make connections to your own life.In Section Two you'll gain insights to your success at work - what is your impact with your boss and co-workers? You'll consider the same question about home - appreciating why things work well and why at times they might be difficult. If you're getting into arguments with people, Section Two is where you'll find out why and we'll give you some proven tips for getting out of this.The course will help you to understand both whats going on AND what you can do about it.In Section Three you'll learn techniques to improve the following: Parenting. Teenagers. Motivating a team. Difficult conversations. Giving a presentation or public speech. Surviving a toxic workplace. Long term relationships.The course material has been created for Udemy by a team of professional psychologists with over 20 years experience of using the model.Once you know what's going on between people - then you can do something about it. You can enjoy more successful relationships at work and home."
Price: 19.99 |
| "Become a fashion buyer - learn the essentials" |
| "Become a Fashion Buyer - Learn the essentials.This course will help you decide if becoming a fashion buyer is for you, and it will help you understand the key tasks of a fashion buyer and the skills required to become a successful fashion buyer.By the end of this course you will be in a position to understand what a fashion buyer does. You will have the confidence to either embark on further study or take the steps to securing your first job as a fashion buyer.Section 1:IntroductionSection 2: You will learn the buyers main responsibilities You will gain an understanding of the key areas a buyer is evaluated on in their role (otherwise referred to as KPI's or key performance indicators), and You will understand the key skills required to be successful in this role. Section 3: Structures of buying departments are discussed from small to large sized businesses , different types of buying roles (brand buyers to product developers) and We will cover other staff roles in a buying department that work alongside a buyer. Section 4: You will understand the steps involved in planning a fashion range Section 5: You will gain an understanding on various sources used in predicting fashion trends for your fashion range and a checklist is provided for you to create your own trend board. Section 6: You will gain a good overview of the financials involved in planning a fashion range, and gain an understanding of the considerations to factor in when building a fashion range. Section 7: You will understand what to look at when sourcing a fashion supplier, how to evaluate a supplier and understand the different types of suppliers. Section 8: Understand the steps involved in negotiating with a supplier. Section 9: You will understand the term landed cost price and how this is calculated. You will also understand the importance of strategies involved in setting the retail price. Gross Profit calculations are also included in this section as it is important financial part of a buyers role. Section 10: You will understand the fundamental analytical functions in a buying department and gain an understanding of what an open to buy is. There will be basic calculations and examples of an OTB. Section 11: You will gain an understanding of the key sales reports reviewed in a buyers role. Section 12: You will gain tips on the best way to land your first role. We will cover work experience, qualifications, finding available positions, cover letters and CV presentation, and interview preparation for meeting with a company."
Price: 49.99 |
| "Personal Effectiveness Blueprint: 17 Life-Changing Lessons" |
| "Personal Effectiveness is not something you're born with, and people aren't naturally winners or losers. Effectiveness and success are developed through habits, mindsets and tools - and this is what we'll develop in this course.Would you like to see exponential success in your personal and business life? Then Personal Effectiveness Blueprint training is for you.Course Description:Chris Mower is a successful serial entrepreneur, and has established several highly profitable businesses. Now he is passing on his top 17 Personal Effectiveness Life Tips to cultivate success across all areas of life. Ranging from quick fixes, to significant lifestyle changes, this course covers every aspect of your life and career. Many of these tips can be applied to family/personal life as well as rocketing your business and career growth, so truly will embed the Personal Effectiveness potential in you.Whether you're an entrepreneur or employee there are ideas in here that will change your life. This course is suitable for all ages and all levels of the career ladder - you're never too old or young to learn fantastic sales, management, and people skills. If your life is not how you always dreamed, now is the time to develop your Personal Effectiveness.Creating this course took a lifetime of learning. Chris has read hundreds of books and interviewed some of the greatest business minds in the country. All of that has been combined and distilled into 17 core lifestyle changes that will build your path to success. The Personal Effectiveness Blueprint Overview:Be memorable and popular at networking eventsDramatically increase your sales closing rateTune out negativity and become a positive thinkerHow your closest network of people determines your incomeMaking a great first impressionIdentifying the common denominator of successAdapting your leadership style to suit any employeeFinding your point of excellence to put you ahead of the packThis course also comes with a 100% Money Back Guarantee.Take this unique home-study course at your own pace and gain clarity, and confidence in areas ranging from sales, management, people skills and more. There's no time like the present to start building a life of success. Join the Personal Effectiveness Blueprint today."
Price: 64.99 |
| "Motivation Coaching Certification GRCMC Motivation Coaching" |
| "Why take our Motivation Coaching Certification - Get Results Motivation Coaching Course?What will a Motivation Coach Certificate and Accreditation + Credentials + Motivational Coach Directory Listing for Level 2 Graduates do for you?If you are searching for a coaching niche that is in demand, becoming a Motivation Coach will benefit you in numerous ways. Why is this important?FACT: Professionally, it is becoming more of a challenge to differentiate yourself in the increasingly competitive coaching world. This can be overwhelming if you dont feel like you have done enough, know enough or have enough current skills to coach people who need motivation to achieve their goals. How can you compete?FACT: Personally, you need to be confident, resilient and resourceful to adapt to this ever-demanding competitive world. This can be a challenge if self-doubt, insecurities and fears block you. How can you rise above these obstacles?MOTIVATION COACHING CERTIFICATION COURSE GOALThese facts dont have to be your future! We developed this comprehensive Motivation Coach Certification Course so that you can learn, achieve and master motivation and coach others to do the same.MOTIVATION COACHING CERTIFICATION COURSE GOAL SELF-ASSESSMENTWHAT IS YOUR GOAL?There are usually 3 different goals among the students who join this Coaching Program. Which one describes you?GOAL #1 - You dont care about receiving a certificate of completion or being fully certified as a Coach through accreditation. You want to learn this process yourself for your own personal and professional needs. If this describes you, then enjoy the course as it fits with your schedule. INCLUDED with the one-time low-cost enrollment fee on Udemy is life-time access to the course content and features on Udemy; including a Udemy generated certificate of completion if you ever need one. You also have access to Level 1 and Level 2 accreditation at no additional cost should your goal change. READ COURSE CONTENT SNAP-SHOT.GOAL #2 You are already a Coaching Professional. You dont care to be fully certified through accreditation or that the certificate of completion comes from Udemy. What you do care about is saving time and saving money. You want to be able to niche without having to develop the process yourself. You want to legally use our proprietary step-by-step and timed-out accredited coaching session guides to coach your clients WITHOUT PAYING our $995 right of use fee (details below). If this describes you, then enjoy the Level 1 program as it fits with your schedule knowing that your one-time low-cost enrollment fee on Udemy gives you access to Level 2 accreditation should your goal change. READ LEVEL 1 ACCREDITATION BENEFITS.GOAL #3 You are completely new to coaching and want guidance. You may have taken other coaching courses but do not feel prepared to coach or you simply want to be listed in our coaching directory on Get Results Coach Academy WITHOUT PAYING the private fee of $1791 (details below). Your goal is to learn our step-by-step coaching process and then be able to practice through fieldwork that is assessed for competency. You want a separate certificate of completion that is not from Udemy but from our Accredited Coaching Academy. You want your name listed in the coaching directory on Get Results Coach Academy so that prospective clients or employers can verify your Coaching Certification and confirm that you have Graduated from this accredited Coaching Training Program. If this describes you, enjoy the Level 2 program that is included with your one-time low-cost enrollment fee on Udemy. READ LEVEL 2 ACCREDITATION BENEFITS.MOTIVATION COACHING CERTIFICATION COURSE CONTENT SNAP-SHOTYou can keep scrolling to view the comprehensive course content and also to watch some of the free video lectures available but here is a quick outline of what is covered:Motivation Coaching Psychology: Do you know exactly what stops people in this area? You are walked-through our proprietary framework for assesses their underlying blocks and obstacles.Motivation Coaching People: Who will be your coaching clients? You discover the inside track so that you can understand people better than they understand themselves.Motivation Coaching Process: Do you have an exact Coaching process in this area of specialization that is proven to get results? Differentiate yourself by using our proprietary Coaching session guides that are timed-out and step-by-step easy to follow and adjust as needed.Motivation Coaching Phrasing: Do you know exactly what to say in any situation? Being stumped in a Coaching Session makes you look unprofessional. We give you the exact phrasing and questions to use so that you feel confident and prepared in every coaching session.Motivation Coaching Procedures: Do you know the Coaching procedures that will make an impact for your clients? In this course, we give you the exact techniques, tools and strategies so that you dont have to do the research or create the resources yourself.Motivation Coaching Practicum: Have you ever coached real clients before? You will benefit from the simple system we provide that makes coaching practice fun and doable. This is truly what differentiates our coaching course from others.Motivation Coaching Pricing and Promotion: Do you know what to charge coaching clients? Do you know how to find coaching clients? No more guessing what to do and how to do it. We have mapped out your success plan so that you feel confident and secure in this area.Motivation Coaching Professional Community: We want you to be successful! Once you join this course, you have access to a full range of ongoing coaching community support that will keep you focused and engaged.LEVEL 1 ACCREDITATION COST-SAVING BENEFITSIncluded with your one-time low-cost enrollment fee on Udemy is life-time access to the course content and features on Udemy; including a Udemy generated certificate of completion.WE COVER THE $995 U.S.D. RIGHT OF USE FEE: When you join this course on Udemy and stay enrolled (no refund), you have permission to use our Proprietary Accredited Coaching Session Guides to coach clients.PLEASE NOTE: A right-of-use fee is not a licensing fee. We own the copyright to the accredited session guides including the processes and material. You do not have permission to re-sell it or develop training, workshops, speaking or books based on our proprietary program.1) Watch 100% of the coursework lectures as fast or as slow as you want.2) Download all of the accredited resources including the session guides.3) Download your Udemy generated certificate of completion when you have completed 100% of the coursework.LEVEL 2 ACCREDITATION COST-SAVING BENEFITSIn addition to the cost-saving benefits and features of Level One, when you remain enrolled in the program past 35 days, you have access to LEVEL 2 benefits; a total of $1791 U.S.D in fees that we cover when you purchase this one course on Udemy and graduate Level 2.WE COVER THE $995 U.S.D. RIGHT OF USE FEE: When you join this course on Udemy and stay enrolled (no refund), you have permission to use our Proprietary Accredited Coaching Session Guides to coach clients.WE COVER THE $199 U.S.D. ACCREDITATION FEE: Level 2 requires that you complete a mandatory Coaching Practicum which assesses your competency using our accredited proprietary session guides with real clients. We call this hands-on coaching fieldwork. We make this step-by-step easy to complete but submission is mandatory for Level 2 Graduation. This is what differentiates our program and sets our Graduates above the rest. While a quickie diploma or certification from having just simply watched videos is appealing and what some coaching courses on Udemy use as a marketing tool to get you to buy, this is not the solution to long-term success. If that is your goal and expectation, this professional coaching program is not for you.WE COVER THE $99 U.S.D. CERTIFICATION FEE: This is a one-time fee to create one individualized certificate of completion from Get Results Coach Academy that displays your certification designation verifying your completion of accreditation.WE COVER THE $199 U.S.D. COACH DIRECTORY LISTING FEE: This is a one-time listing one-time fee to add your first and last name to this niche specific career coach directory on Get Results Coach Academy.WE COVER THE $299 U.S.D ASSOCIATION FEE: This is an annual membership fee to keep your certification current and valid.WE COVER $1791 U.S.D IN FEES WHEN YOU JOIN US ON UDEMY AND GRADUATE LEVEL 2Now you might be thinking this sounds too good to be true; whats the catch? or you might be saying Coaching Training like this should cost 5 thousand or more; why would such a valuable program be available on Udemy for such a low price of only $199 U.S.D.? There is no catch just a vision, a purpose and a mission inspired by our Founder and your Instructor. Keep reading to meet her!WHY OUR LEVEL 2 GRADUATES EXCELJoining this course and completing Level 2 of the program requires effort but you can do this! We want you to stand-out and excel as a coach but only you can do the work. We cant guarantee your success or that you will make money as a coach. Only you are responsible for the effort you put in and the success you achieve. What we can guarantee is that we make it as simple and easy regardless of your current skill level and we are there to guide you every step of the way; however long that takes. We want you to be satisfied and if you are not, you can request a refund from Udemy within 30 days of purchase. Be sure to read Udemys terms for details.THE POWER OF BELIEFBut when you commit and stay enrolled in the course (no refund), you put into motion the power of belief. When you buy the course on Udemy, it is a one-time low-cost investment of $199 U.S.D. that will continue to pay dividends in countless ways for years to come. Level 2 is simple: in addition to staying enrolled in the course for longer than 35 days, you complete 100% of the coursework, complete 100% of the coaching practicum fieldwork and submit it for approval and we cover the $1791 U.S.D. fees that we normally charge on our website for Level 2 Graduation.Why do we do this? When you meet Louise, you will understand the purpose-driven passion that propels our mission! You may have already seen this before but it is worth a second, third or fourth read.MEET YOUR INSTRUCTOR LOUISE ANNE MAURICEIf you have never met me before, my name is Louise Anne Maurice; Louise for short. When being interviewed, I am always asked the question what have been the most important aspects of your 30+ year career; your university and specialized education, the impressive skills you developed working for companies and educational institutions or boot-strapping it to build your company from the ground-up? My response is always ALL OF IT because every experience has shaped who I am, my purpose and the reason I am a Coaching Training and Development Specialist today.LOUISE TALKS OVERCOMING CHALLENGESYou see, I was not handed life on a silver platter! I worked hard to achieve what I have; overcoming many challenges along the way. How did I do this? I developed, what I later termed, my Empowered Coach Approach; a revolutionary framework founded on empowerment, integrity and respect that leverages my DE Effect to accelerate and strengthen transformation. Using this approach, I was able to power-past blocks and obstacles so I could achieve my goals and experience my limitless potential. The thousands and thousands of people I have either coached, trained, consulted or taught over the past 30+ years have also benefited from my Empowered Coach Approach.LOUISE ANNE MAURICES MISSIONBut that is just scratching the surface! I see a world where everyone is overcoming their challenges and living their full potential. Can you imagine the positive impact? But I cant do it alone! My mission is to train millions of coaches world-wide to coach millions of people world-wide to live their full potential. How will I do this? I have teamed up with the Udemy E-Learning Platform to make my proprietary coaching training programs easily accessible to a global audience at an affordable price. As Director of Coaching Training and Coaching Courses Development for Get Results Coach Academy, I use my Empowered Coach Approach to develop purposeful, comprehensive and streamlined curriculum that shortens the learning curve for you to become a Professional World-Class Coach.WHAT IS YOUR MISSION?Purpose is powerful! Yes, you might be fired-up at the cost-savings bonus that I have made exclusively available on Udemy. Who doesnt love a deal! More importantly though, I hope youre fired-up at the prospect of being a part of making a positive difference in the world; I know I am! Are you ready to do something more meaningful with your life; something that makes an impact and leaves a lasting legacy with your name written all over it? Whatever your mission, you will find a community that is here to support your purpose.ARE YOU READY TO START MAKING A DIFFERENCE?I invite you to join and start living your purpose today! Simply click on the Buy now button to enroll.I will see you in the first lecture. Copyright Louise Anne Maurice of Get Results Coach Academy"
Price: 199.99 |
| "Confidence Coaching Certification CGRCC Confidence Coaching" |
| "Why take our Confidence Coaching Certification Accredited Confidence Coach Course?What will a Confidence Coach Certificate and Accreditation + Credentials + Confidence Coach Directory Listing for Level 2 Graduates do for you?If you are searching for a coaching niche that is in demand, becoming a Confidence Coach will benefit you in numerous ways. Why is this important?FACT: Professionally, it is becoming more of a challenge to differentiate yourself in the increasingly competitive coaching world. This can be overwhelming if you dont feel like you have done enough, know enough or have enough current skills. How can you compete?FACT: Personally, you need to be confident, resilient and resourceful to adapt to this ever-demanding competitive world. This can be a challenge if self-doubt, insecurities and fears block you. How can you rise above these obstacles?CONFIDENCE COACHING CERTIFICATION COURSE GOALThese facts dont have to be your future! We developed this comprehensive Confidence Coach Certification Course so that you can learn, achieve and master this high-demand niche coaching area.CONFIDENCE COACHING CERTIFICATION COURSE GOAL SELF-ASSESSMENTWHAT IS YOUR GOAL?There are usually 3 different goals among the students who join this Coaching Program. Which one describes you?GOAL #1 - You dont care about receiving a certificate of completion or being fully certified as a Coach through accreditation. You want to learn this process yourself for your own personal and professional needs. If this describes you, then enjoy the course as it fits with your schedule. INCLUDED with the one-time low-cost enrollment fee on Udemy is life-time access to the course content and features on Udemy; including a Udemy generated certificate of completion if you ever need one. You also have access to Level 1 and Level 2 accreditation at no additional cost should your goal change. READ COURSE CONTENT SNAP-SHOT.GOAL #2 You are already a Coaching Professional. You dont care to be fully certified through accreditation or that the certificate of completion comes from Udemy. What you do care about is saving time and saving money. You want to be able to niche without having to develop the process yourself. You want to legally use our proprietary step-by-step and timed-out accredited coaching session guides to coach your clients WITHOUT PAYING our $995 right of use fee (details below). If this describes you, then enjoy the Level 1 program as it fits with your schedule knowing that your one-time low-cost enrollment fee on Udemy gives you access to Level 2 accreditation should your goal change. READ LEVEL 1 ACCREDITATION BENEFITS.GOAL #3 You are completely new to coaching and want guidance. You may have taken other coaching courses but do not feel prepared to coach or you simply want to be listed in our coaching directory on Get Results Coach Academy WITHOUT PAYING the private fee of $1791 (details below). Your goal is to learn our step-by-step coaching process and then be able to practice through fieldwork that is assessed for competency. You want a separate certificate of completion that is not from Udemy but from our Accredited Coaching Academy. You want your name listed in the coaching directory on Get Results Coach Academy so that prospective clients or employers can verify your Coaching Certification and confirm that you have Graduated from this accredited Coaching Training Program. If this describes you, enjoy the Level 2 program that is included with your one-time low-cost enrollment fee on Udemy. READ LEVEL 2 ACCREDITATION BENEFITS.CONFIDENCE COACHING CERTIFICATION COURSE CONTENT SNAP-SHOTYou can keep scrolling to view the comprehensive course content and also to watch some of the free video lectures available but here is a quick outline of what is covered:Confidence Coaching Psychology: Do you know exactly what stops people in this area? You are walked-through our proprietary framework for assesses their underlying blocks and obstacles.Confidence Coaching People: Who will be your coaching clients? You discover the inside track so that you can understand people better than they understand themselves.Confidence Coaching Process: Do you have an exact Coaching process in this area of specialization that is proven to get results? Differentiate yourself by using our proprietary Coaching session guides that are timed-out and step-by-step easy to follow and adjust as needed.Confidence Coaching Phrasing: Do you know exactly what to say in any situation? Being stumped in a Coaching Session makes you look unprofessional. We give you the exact phrasing and questions to use so that you feel confident and prepared in every coaching session.Confidence Coaching Procedures: Do you know the Coaching procedures that will make an impact for your clients? In this course, we give you the exact techniques, tools and strategies so that you dont have to do the research or create the resources yourself.Confidence Coaching Practicum: Have you ever coached real clients before? You will benefit from the simple system we provide that makes coaching practice fun and doable. This is truly what differentiates our coaching course from others.Confidence Coaching Pricing and Promotion: Do you know what to charge coaching clients? Do you know how to find coaching clients? No more guessing what to do and how to do it. We have mapped out your success plan so that you feel confident and secure in this area.Confidence Coaching Professional Community: We want you to be successful! Once you join this course, you have access to a full range of ongoing coaching community support that will keep you focused and engaged.LEVEL 1 ACCREDITATION COST-SAVING BENEFITSIncluded with your one-time low-cost enrollment fee on Udemy is life-time access to the course content and features on Udemy; including a Udemy generated certificate of completion.WE COVER THE $995 U.S.D. RIGHT OF USE FEE: When you join this course on Udemy and stay enrolled (no refund), you have permission to use our Proprietary Accredited Coaching Session Guides to coach clients.PLEASE NOTE: A right-of-use fee is not a licensing fee. We own the copyright to the accredited session guides including the processes and material. You do not have permission to re-sell it or develop training, workshops, speaking or books based on our proprietary program.1) Watch 100% of the coursework lectures as fast or as slow as you want.2) Download all of the accredited resources including the session guides.3) Download your Udemy generated certificate of completion when you have completed 100% of the coursework.LEVEL 2 ACCREDITATION COST-SAVING BENEFITSIn addition to the cost-saving benefits and features of Level One, when you remain enrolled in the program past 35 days, you have access to LEVEL 2 benefits; a total of $1791 U.S.D in fees that we cover when you purchase this one course on Udemy and graduate Level 2.WE COVER THE $995 U.S.D. RIGHT OF USE FEE: When you join this course on Udemy and stay enrolled (no refund), you have permission to use our Proprietary Accredited Coaching Session Guides to coach clients.WE COVER THE $199 U.S.D. ACCREDITATION FEE: Level 2 requires that you complete a mandatory Coaching Practicum which assesses your competency using our accredited proprietary session guides with real clients. We call this hands-on coaching fieldwork. We make this step-by-step easy to complete but submission is mandatory for Level 2 Graduation. This is what differentiates our program and sets our Graduates above the rest. While a quickie diploma or certification from having just simply watched videos is appealing and what some coaching courses on Udemy use as a marketing tool to get you to buy, this is not the solution to long-term success. If that is your goal and expectation, this professional coaching program is not for you.WE COVER THE $99 U.S.D. CERTIFICATION FEE: This is a one-time fee to create one individualized certificate of completion from Get Results Coach Academy that displays your certification designation verifying your completion of accreditation.WE COVER THE $199 U.S.D. COACH DIRECTORY LISTING FEE: This is a one-time listing one-time fee to add your first and last name to this niche specific career coach directory on Get Results Coach Academy.WE COVER THE $299 U.S.D ASSOCIATION FEE: This is an annual membership fee to keep your certification current and valid.WE COVER $1791 U.S.D IN FEES WHEN YOU JOIN US ON UDEMY AND GRADUATE LEVEL 2Now you might be thinking this sounds too good to be true; whats the catch? or you might be saying Coaching Training like this should cost 5 thousand or more; why would such a valuable program be available on Udemy for such a low price of only $199 U.S.D.? There is no catch just a vision, a purpose and a mission inspired by our Founder and your Instructor. Keep reading to meet her!WHY OUR LEVEL 2 GRADUATES EXCELJoining this course and completing Level 2 of the program requires effort but you can do this! We want you to stand-out and excel as a coach but only you can do the work. We cant guarantee your success or that you will make money as a coach. Only you are responsible for the effort you put in and the success you achieve. What we can guarantee is that we make it as simple and easy regardless of your current skill level and we are there to guide you every step of the way; however long that takes. We want you to be satisfied and if you are not, you can request a refund from Udemy within 30 days of purchase. Be sure to read Udemys terms for details.THE POWER OF BELIEFBut when you commit and stay enrolled in the course (no refund), you put into motion the power of belief. When you buy the course on Udemy, it is a one-time low-cost investment of $199 U.S.D. that will continue to pay dividends in countless ways for years to come. Level 2 is simple: in addition to staying enrolled in the course for longer than 35 days, you complete 100% of the coursework, complete 100% of the coaching practicum fieldwork and submit it for approval and we cover the $1791 U.S.D. fees that we normally charge on our website for Level 2 Graduation.Why do we do this? When you meet Louise, you will understand the purpose-driven passion that propels our mission! You may have already seen this before but it is worth a second, third or fourth read.MEET YOUR INSTRUCTOR LOUISE ANNE MAURICEIf you have never met me before, my name is Louise Anne Maurice; Louise for short. When being interviewed, I am always asked the question what have been the most important aspects of your 30+ year career; your university and specialized education, the impressive skills you developed working for companies and educational institutions or boot-strapping it to build your company from the ground-up? My response is always ALL OF IT because every experience has shaped who I am, my purpose and the reason I am a Coaching Training and Development Specialist today.LOUISE TALKS OVERCOMING CHALLENGESYou see, I was not handed life on a silver platter! I worked hard to achieve what I have; overcoming many challenges along the way. How did I do this? I developed, what I later termed, my Empowered Coach Approach; a revolutionary framework founded on empowerment, integrity and respect that leverages my DE Effect to accelerate and strengthen transformation. Using this approach, I was able to power-past blocks and obstacles so I could achieve my goals and experience my limitless potential. The thousands and thousands of people I have either coached, trained, consulted or taught over the past 30+ years have also benefited from my Empowered Coach Approach.LOUISE ANNE MAURICES MISSIONBut that is just scratching the surface! I see a world where everyone is overcoming their challenges and living their full potential. Can you imagine the positive impact? But I cant do it alone! My mission is to train millions of coaches world-wide to coach millions of people world-wide to live their full potential. How will I do this? I have teamed up with the Udemy E-Learning Platform to make my proprietary coaching training programs easily accessible to a global audience at an affordable price. As Director of Coaching Training and Coaching Courses Development for Get Results Coach Academy, I use my Empowered Coach Approach to develop purposeful, comprehensive and streamlined curriculum that shortens the learning curve for you to become a Professional World-Class Coach.WHAT IS YOUR MISSION?Purpose is powerful! Yes, you might be fired-up at the cost-savings bonus that I have made exclusively available on Udemy. Who doesnt love a deal! More importantly though, I hope youre fired-up at the prospect of being a part of making a positive difference in the world; I know I am! Are you ready to do something more meaningful with your life; something that makes an impact and leaves a lasting legacy with your name written all over it? Whatever your mission, you will find a community that is here to support your purpose.ARE YOU READY TO START MAKING A DIFFERENCE?I invite you to join and start living your purpose today! Simply click on the Buy now button to enroll.I will see you in the first lecture. Copyright Louise Anne Maurice of Get Results Coach Academy"
Price: 199.99 |
