| "How To Build A Successful Freelance Consulting Business" |
| "** This course is perfect for anyone wanting to build a Freelance Consultancy from scratch****Learn How toEfficientlyPlan and Manage Your Business,Convert Your Audience To Customers With These Marketing StrategiesandFoster Customer Loyalty Through Quality Customer Support After SaleThis course is perfect for:--Anyone withskillsthey can use to generate extra income in their spare time--Freelancers looking to professionalize theirbusiness--Freelancers looking for their next customersBy the end of this course you will know and be able to do the following:1. Increase your income, audience, and customer base with specific strategies2. Produce high quality goods and services through establishing a robust business foundation3. Convert your audience to loyal customers through tried and tested social media marketing strategiesI will work with you throughout the course to support your journey to mastery!Ready to build your financial freedom? Click ""Take this course now"" to get started!"
Price: 29.99 |
| "Bite-Sized French: Future Verb Tenses" |
| "You will learn the two mainways to speak and write in the future, in French.This is a great course for those wanting to learn the future tenses or for those wanting a quick review of French verbs in the future.-in just a short timeyou will be able to use French verbs to talk about something that will take place in a future time. You will learn:le futur proche (or the near future tense) andle futur simple (or the simple future tense)Together, we take a step-by-step approach to this course. You will watch video lessons, do practice exercises and assignmentsto try out what you have learned about the future. Many supplemental resources have been included in this course.In addition, the future tenses are incorporated in ""virtual visits"" to Qubec city and Montral. Discover what you might like to see in your future visits to FrenchCanada!Looking forward to seeing you in the course!"
Price: 24.99 |
| "English Grammar : The Four Past Tenses" |
| "Welcome to English Grammar: The Four Past Tenses. This is course that takes you step-by-step through the past tenses of English. Youwill learn: How to identify each past tense in English How to form the tense The differences between North American English and British English when forming verbs in the past tenses When to use each past tense (and when to avoid certain tenses) How to choose between two tenses in the pastHow to write sentences in the negative using this tenseHow to write questions correctly the common irregular verbs in EnglishWho should take this course? Your first language is not English, but you have a highbeginner or intermediate level of the language You could also be a native English speaker who wants to improve your knowledge of grammar You are a student who is ready to do the exercises provided in the courseThe course contains: apretestso that you will see where you need to practice video lessonswhere each tense is demonstrated a variety ofexerciseswhere you can practice what has been taught review activitiesfor each section a final activityso you can measure your progress Last updated: December 2017"
Price: 24.99 |
| "Language Learning in the 21st Century:" |
| "In this global village, we are fortunate to have access to many language-learning resources from traditional classes to podcasts, online speaking buddies, language-learning software and more. This class helps you navigate a path through this bewildering array of possibilities and provides suggestions for developing your own, personalized language-learning plan.Specifically, the class will provide you with:guidelines for your consideration when starting to learn a new language10 tips for language learningsuggestions for ( mostly) free resources that you might want to use on the path to fluency in your target language"
Price: 19.99 |
| "After Effects - Expresses e Scripts" |
| "Filmes, videoclips, novelas, propagandas, telejornais... Todos tm uma coisa em comum: a presena de vinhetas ou elementos de motion graphics. O design de elementos grficos em movimento se tornou parte integrante de qualquer produo audiovisual moderna.O curso prepara o aluno para trabalhar com ps produo e animao profissional de vdeo, shapes, imagens, com a utilizao de expresses e scripts. Estes so capazes de agilizar o nosso processo de animao, deixando nossos projetos cada vez mais profissionais."
Price: 54.99 |
| "Master Number Base Conversion" |
| "Welcome to a course that will teach you different Number Base Conversions, and how to do Arithmeticsin those bases.Number bases are different ways of writing and using the same number. In this course you will learnwhat we mean by number bases and you will also learn the important numbers bases like Base 2, 8, and 16.You will learnhow to convert from Base 10 to base 2, 8 and 16 and back.Also at the end, you will learn how to convert any Base to base 10 and vise versa.Number bases are very important and one of the skills which is useful to programmers is an understanding of the relationship between number bases. When you understand how numbers are represented in base 2(Binary), base 8(Octal), and base 16(Hexadecimal), you will understand different aspects in programming better.How is it delivered?I know visually seeing a problem getting solved is the easiest and the most direct way for a student to learn so I designed the course keeping this in mind. The materials are deliveredthrough videos to make thesubject easy to understand.How do I learn better?There are quizzes after each section so you can test your knowledge and see how much of the material has sank in. I suggest you go through each lesson several times to better understand the topics."
Price: 19.99 |
| "Pre Calculus Mastered" |
| "Having trouble learning Pre-calculus? Don't know where to start?Well you are in the right place. I want to welcome you toa course on Precalculus where you will acquire skills to become an Expert on a wide range of Functions, Trigonometry, Sequences & Series and Conic Sections.I have created this course for students to have a place where they can learn, understand and excel in Pre-calculus in order to have a strong foundation for more advanced courses like Calculus.The course consist of an extensive curriculumteaching you different concepts in Functions, Trigonometry, Sequences & Series and Conic Sections.At the end of this course, you will be able to,Find domain and range of functions.Determine behavior of a function from the graph of it.Transform and combine functions.Divide Polynomials.Master Logarithms and Exponential functions.know the Unite Circle.Constructand GraphTrigonometric Functions and Inverse Trig Functions.Determine domain and range of of Trigonometric Functions.Proof Trigonometric identities and equations.Master Sequences and Series and get to know the different kinds.Aquire thorough understanding of Conic Sections, and how to find their equations.How is it delivered?I know visually seeing a problem getting solved is the easiest and the most direct way for a student to learn so I designed the course keeping this in mind. I go through concepts and problems using electronic pen and paper, explaining each step along the way so you have a lear idea how to go from A to B to C without any problem.How do I learn better?There are quizzes after each section so you can test your knowledge and see how much of the material has sank in. There are also practice problemattached to the lectures so you could practice what you learn.I suggest you go through each lesson several times to better understand the topics."
Price: 149.99 |
| "Calculus 1 Mastered" |
| "Having trouble learning Calculus 1? Don't know where to start?Well you are in the right place. I want to welcome you toa course on Calculus 1whereyou will acquire skills to become anExpert on Limits, Limit Laws, Derivatives and its Applications.I have created this course for students to have a place where they canlearn, understand and excelin Calculus 1in order to have a strong foundation for more advanced courses like Calculus 2.The course consist of an extensive curriculumteaching you different essentialconcepts and skills.How is it delivered?I know visually seeing a problem getting solved is the easiest and the most direct way for a student to learn so I designed the course keeping this in mind. I go through concepts and problems using electronic pen and paper, explaining each step along the way so you have a lear idea how to go from A to B to C without any problem.How do I learn better?There are quizzes after each section so you can test your knowledge and see how much of the material has sank in. There are alsopractice problemattached to the lectures so you could practice what you learn.I suggest you go through each lesson several times to better understand the topics."
Price: 149.99 |
| "Trigonometry Mastered" |
| "Having trouble learning Trigonometry? Don't know where to start?Well you are in the right place. I want to welcome you toa course on Trigonometrywhere you will acquire skills to become anExpertonRight Angle, Unite Circle, Analytic Trigonometry and Polar Coordinates.I have created this course for students to have a place where they canlearn, understand and excelin Trigonometryin order to have a strong foundation for more advanced courses like Calculus.The course consist of an extensive curriculumteaching you different essentialconcepts and skills.At the end of this course, you will be able to,Fully understand the Unite Circle.Learn how to find Terminal Points, Reference Numbers and Reference Angles.Learn what are Trigonometric Functions.Master graphing Trigonometric Functions.Be able to determine Domain and Range of Trig Functions.Be able to use the Law of Sins and Cosines.Proof Trig functions and identities.Master the different Trigonometric Formulas.Be able to go from Polar to Rectangular Coordinate and back.Be able to graph Polar Equations.Use De Moiver's Theorem.Find the nth Roots of Complex Numbers.How is it delivered?I know visually seeing a problem getting solved is the easiest and the most direct way for a student to learn so I designed the course keeping this in mind. I go through concepts and problems using electronic pen and paper, explaining each step along the way so you have a lear idea how to go from A to B to C without any problem.How do I learn better?There are quizzes after each section so you can test your knowledge and see how much of the material has sank in. There are alsopractice problemattached to the lectures so you could practice what you learn.I suggest you go through each lesson several times to better understand the topics."
Price: 99.99 |
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Price: 149.99 |
| "Grner Smoothie Kurs: Kreire leckere grne Smoothies" |
| "Der grne Smoothies Kurs fr Einsteiger.Fhlst du dich mde und energielos? Wrdest du gerne ein paar Kilos abnehmen? Willst du deine aktuellen gesundheitlichen Beschwerden lindern? Willst du deine Ernhrung umstellen um schwerwiegende Zivilisationskrankheiten zu vermeiden?Dann lies dir diese 9 Gesundheitseffekte durch um dich zum Konsum von Grnen Smoothies zu motivieren!1. Grne Smoothies helfen dir beimAbnehmenDer vermehrte Konsum von Obst und Blattgemse, mit weniger Kalorien und mehr Nhrstoffen, fhrt zu einer negativen Kalorienbilanz. Das heit, du verbrauchst mehr als du isst und dadurch fangen die Pfunde an zu purzeln (vor allem wenn du damit eine Hauptmahlzeit ersetzt).2. Grne Smoothies geben dir EnergieDurch die Frchte erhlt dein Krper schnell verwertbaren, natrlichen Zucker, den dein Gehirn bentigt, um konzentriert zu arbeiten. Allerdings ist der Anstieg und der Abfall bei weitem nicht so drastisch wie bei Mehlspeisen oder anderen Sigkeiten, denn Grne Smoothies enthalten viele Ballaststoffe, die den Anstieg und Abfall des Blutzuckerspiegels sanft abdmpfen.3. Grne Smoothies geben dir Klarheit und LeichtigkeitFhlst du dich antriebslos, betrbt, unmotiviert oder sogar ein wenig depressiv? Deinem Krper fehlen wichtige Mikronhrstoffe wie Vitamine, Mineralstoffe, Enzyme und sekundre Pflanzenstoffe. Diese sorgen dafr dass der Hormonhaushalt im Krper optimal funktioniert. Grne Smoothies klren also deine geistigen Wolken und sorgen fr Sonnenschein in deinem Krper. Du fhlst dich klarer und leichter nach dem Konsum Grnen Smoothies!4. Grne Smoothies lassen dein Verlangen nach Sigkeiten, Kaffee und anderem Fast Food verschwindenWenn du hufig minderwertige Nahrung konsumierst wird dein Verlangen nach minderwertiger Nahrung immer grer. Du begibst dich in eine Abwrtsspirale, die langfristig zu mehr Kilos, weniger Bewegung, schlechterer Grundstimmung und mehr gesundheitlichen Beschwerden fhrt. Grne Smoothies helfen dir aus dieser Abwrtsspirale auszubrechen und deine Gesundheit in eine Aufwrtsspirale zu verwandeln. Das Verlangen nach gesunder Ernhrung wird steigen, weil du am eigenen Leib sprst, wie gut sich frische, vitalstoffreiche Lebensmittel in deinem Krper anfhlen.5. Grne Smoothies verbessern die Haut und reduzieren langfristig Akne und MitesserDenn Grne Smoothies regen deine Ausscheidungsorgane an, vor allem wenn du auch bitteres Blattgemse einbaust. Dein Krper muss dadurch nicht deine Haut als Notfall-Ausscheidungsorgan verwenden. Pickel oder Ausschlge sind zum Beispiel ein Zeichen dass dein Darm, die Nieren, die Leber und die Lunge berlastet sind.6. Grne Smoothies reduzieren FaltenDie Frchte im Grnen Smoothie enthalten viel Wasser. Auerdem enthlt das Grne Blattgemse gesunde Fette (wenn auch in geringen Mengen) die aber ausreichen um gemeinsam mit dem Wasser deiner Haut Feuchtigkeit zu spenden und die Spannkraft zu verbessern.7. Grne Smoothies verbessern deine VerdauungDie Bitterstoffe harmonisieren deine Magensureproduktion, was im Allgemeinen deine Verdauungskraft im Magen erhht und dazu fhrt, dass weniger Unverdautes in den Darm gelangt. Die Ballaststoffe frdern ein positives Bakterienklima im Darm, was wiederum eine starke positive Auswirkung auf dein Immunsystem hat. Die Strke deines Immunsystem hngt wesentlich von einer gesunden Darmflora ab. Darmablagerungen lsen sich nach und nach auf und aus dem Krper ausgeschieden. Dies erhht die Nhrstoffaufnahme ber die Darmzotten. Dein Krper kann so mehr Nhrstoffe aus der Nahrung wirklich verwerten.8. Grne Smoothies reduzieren das Risiko von ernsthaften ErkrankungenEs ist bekannt dass ein vermehrter Verzehr von Obst und Gemse dein Risiko auf die Zivilisationserkrankungen wie Bluthochdruck, Herzinfarkt, Schlaganfall, Krebs, Diabetes uvm. wesentlich reduziert.9. Grne Smoothies reduzieren deine gesundheitlichen BeschwerdenEgal welche gesundheitlichen Beschwerden dich im Moment plagen, Grne Smoothies helfen dir untersttzend um die Beschwerden nach und nach zu lindern. Gib dem Krper natrliche, unbehandelte, frische Lebensmittel, die ihm helfen sich selbst schneller zu heilen!Starte jetzt und sichere dir den grnen Smoothie-Kurs!Ich freu mich auf dich!"
Price: 64.99 |
| "Create Stunning Cinemagraphs in Adobe Photoshop" |
| "In the course you'll learn to createcinemagraphs in Adobe Photoshop.Cinemagraphs are static photos that contain areas of motion. These specialized graphicscan be leveraged for dynamic advertising, or to help you stand out on social media.In this course, you'll learn how to use Photoshop to createcinemagraphswhere you'lladd subtle movement to your photos. You will learnhow to combine images together to create beautiful moving photographs. Along the way you'lllearn tips and tricks for shooting a cinemagraph, common animation techniques, and how to mask layers to isolate movement to one part of an image."
Price: 19.99 |
| "Quantitative Trading Analysis with R" |
| "Full Course Content Last Update 08/2018Learn quantitative trading analysis through a practical course with R statistical software using S&P 500 Index ETF prices 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 a Quantitative Trading Analysis Expert in this Practical Course with RRead or download S&P 500 Index ETF prices data and perform quantitative trading analysis operations by installing related packages and running script code on RStudio IDE.Implement trading strategies based on their category and frequency by defining indicators, identifying signals they generate and outlining rules that accompany them.Explore strategy categories through trend-following indicators such as simple moving averages, moving averages convergence-divergence and mean-reversion indicators such as Bollinger bands, relative strength index, statistical arbitrage through z-score.Evaluate simulated strategy historical risk adjusted performance through trading statistics, performance metrics and risk management metrics.Calculate main trading statistics such as net trading profit and loss, gross profit, gross loss, profit ratio, maximum drawdown, profit to maximum drawdown and equity curve.Measure principal strategy performance metrics such as annualized returns, annualized standard deviation and annualized Sharpe ratio.Estimate key risk management metrics such as maximum adverse excursion and maximum favorable excursion.Maximize historical risk adjusted performance by optimizing strategy parameters through an exhaustive grid search of all indicators parameters combinations.Minimize optimization over-fitting or data snooping through walk forward analysis implemented as time-series or step-forward cross-validation by sequentially resampling asset prices data into rolling fixed length training subsets for in-sample strategy parameters optimizations and testing subsets for out-of-sample optimized strategy parameters validations.Become a Quantitative Trading Analysis Expert and Put Your Knowledge in PracticeLearning quantitative trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors quantitative 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 back-testing to achieve greater effectiveness. Content and OverviewThis practical course contains 59 lectures and 7 hours of content. Its designed for all quantitative trading 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 quantitative trading analysis operations by installing related packages and running script code on RStudio IDE.Then, youll implement trading strategy by defining indicators based on its category and frequency, identifying trading signals these generate, outlining trading rules that accompany them and applying all of the above. Next, youll explore main strategy categories such as trend-following and mean-reversion. For trend-following strategy category, youll use indicators such as simple moving averages and moving averages convergence-divergence. For mean-reversion strategy category, youll use indicators such as Bollinger bands, relative strength index and statistical arbitrage through z-score.After that, youll do strategy reporting by evaluating simulated strategy risk adjusted performance using historical data. Next, youll explore main strategy reporting areas such as trading statistics, performance metrics and risk management metrics. For trading statistics, youll use net trading profit and loss, gross profit, gross loss, profit factor, maximum drawdown, profit to maximum drawdown and equity curve. For performance metrics, youll use annualized return, annualized standard deviation and annualized Sharpe ratio. For risk management metrics, youll use maximum adverse excursion and maximum favorable excursion charts.Later, youll optimize strategy parameters by maximizing historical risk adjusted performance through an exhaustive grid search of all indicators parameters combinations. Next, youll explore main strategy parameters optimization objectives such as net trading profit and loss, maximum drawdown and profit to maximum drawdown metrics. Then, youll do strategy walk forward analysis to reduce historical parameters optimization over-fitting or data snooping through time-series or step-forward cross-validation. Next, youll implement asset prices time series data sequential resampling into fixed length training and testing without replacement subsets. For training data subsets, youll do sequential in-sample strategy parameters optimization. For testing data subsets, youll do sequential out-of-sample validation of previously optimized strategy parameters. Finally, youll repeat this process one step-forward up to the end of asset prices time series data."
Price: 49.99 |
| "Quantitative Trading Analysis with Python" |
| "Full Course Content Last Update 09/2018Learn quantitative trading analysis through a practical course with Python programming language using S&P 500 Index ETF prices 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 a Quantitative Trading Analysis Expert in this Practical Course with PythonRead or download S&P 500 Index ETF prices data and perform quantitative trading analysis operations by installing related packages and running code on Python PyCharm IDE.Implement trading strategies based on their category and frequency by defining indicators, identifying signals they generate and outlining rules that accompany them.Explore strategy categories through trend-following indicators such as simple moving averages, moving averages convergence-divergence and mean-reversion indicators such as Bollinger bands, relative strength index, statistical arbitrage through z-score.Evaluate simulated strategy historical risk adjusted performance through trading statistics and performance metrics.Calculate main trading statistics such as net trading profit and loss, maximum drawdown and equity curve.Measure principal strategy performance metrics such as annualized returns, annualized standard deviation and annualized Sharpe ratio.Maximize historical performance by optimizing strategy parameters through an exhaustive grid search of all indicators parameters combinations.Reduce optimization over-fitting or data snooping through asset prices data delimiting into training subset for in-sample strategy parameters optimization and testing subset for out-of-sample optimized strategy parameters validation.Become a Quantitative Trading Analysis Expert and Put Your Knowledge in PracticeLearning quantitative trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors quantitative 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 back-testing to achieve greater effectiveness. Content and OverviewThis practical course contains 50 lectures and 7 hours of content. Its designed for all quantitative trading 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 quantitative trading analysis operations by installing related packages and running code on Python PyCharm IDE.Then, youll implement trading strategy by defining indicators based on its category and frequency, identifying trading signals these generate and outlining trading rules that accompany them. Next, youll explore main strategy categories such as trend-following and mean-reversion. For trend-following strategy category, youll use indicators such as simple moving averages and moving averages convergence-divergence. For mean-reversion strategy category, youll use indicators such as Bollinger bands, relative strength index and statistical arbitrage through z-score.After that, youll do strategy reporting by evaluating simulated strategy risk adjusted performance using historical data. Next, youll explore main strategy reporting areas such as trading statistics and performance metrics. For trading statistics, youll use net trading profit and loss, maximum drawdown and equity curve. For performance metrics, youll use annualized return, annualized standard deviation and annualized Sharpe ratio. Later, youll optimize strategy parameters by maximizing historical performance through an exhaustive grid search of all indicators parameters combinations. Next, youll explore main strategy parameters optimization objective such as final portfolio equity metric. Finally, youll reduce optimization over-fitting or data snooping through asset prices data delimiting into training subset for in-sample strategy parameters optimization and testing subset for out-of-sample optimized strategy parameters validation."
Price: 49.99 |
| "Volatility Trading Analysis with R" |
| "Full Course Content Last Update 11/2018Learn volatility trading analysis through a practical course with R statistical software using CBOE and S&P 500 volatility strategies benchmark indexes and replicating ETFs or ETNs historical data for risk adjusted performance back-testing. It explores main concepts from advanced 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 sophisticated investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.Become a Volatility Trading Analysis Expert in this Practical Course with RDownload CBOE and S&P 500 volatility strategies benchmark indexes and replicating funds data to perform historical volatility trading analysis by installing related packages and running script on RStudio IDE.Estimate historical or realized volatility through close to close, Parkinson, Garman-Klass, Rogers-Satchell, Garman-Klass-Yang-Zhang and Yang-Zhang metrics.Calculate forecasted volatility through seasonal random walk, historical mean, simple moving average, exponentially weighted moving average, autoregressive integrated moving average and general autoregressive conditional heteroscedasticity models.Measure market participants implied volatility through related volatility index.Estimate futures prices and explore volatility and asset returns correlation, volatility risk premium, volatility term structure and volatility skew patterns.Assess volatility hedge futures trading strategy historical risk adjusted performance using related hedged equity volatility futures strategy benchmark index replicating ETF or ETN.Approximate options call and put prices through Black and Scholes, binomial trees models together with related option Greeks. Evaluate buy write, put write and volatility tail hedge options trading strategies historical risk adjusted performance using related buy write, put write and hedged equity volatility options strategy benchmark indexes and replicating ETFs.Become a Volatility Trading Analysis Expert and Put Your Knowledge in PracticeLearning volatility trading analysis is indispensable for finance careers in areas such as derivatives research, derivatives development, and derivatives trading mainly within investment banks and hedge funds. It is also essential for academic careers in derivatives finance. And it is necessary for experienced sophisticated investors volatility trading strategies research.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using CBOE and S&P 500 volatility strategies benchmark indexes and replicating ETFs or ETNs historical data for risk adjusted performance back-testing to achieve greater effectiveness.Content and OverviewThis practical course contains 45 lectures and 5 hours of content. Its designed for advanced volatility trading analysis knowledge level and a basic understanding of R statistical software is useful but not required.At first, youll learn how to download CBOE and S&P 500 volatility strategies benchmark indexes and replicating ETFs or ETNs data to perform historical volatility trading analysis by installing related packages and running script on RStudio IDE.Then, youll do volatility analysis by estimating historical or realized volatility through close to close, Parkinson, Garman-Klass, Rogers-Satchell, Garman-Klass-Yang-Zhang and Yang-Zhang metrics. After that, youll use these estimations to forecast volatility through seasonal random walk, historical mean, simple moving average, exponentially weighted moving average, autoregressive integrated moving average and general autoregressive conditional heteroscedasticity models. Next, youll measure market participants implied volatility through related volatility index.Later, youll estimate futures prices and compare them with actual historical data. Then, youll explore volatility and asset returns correlation, volatility risk premium, volatility term structure and volatility skew patterns. After that, youll assess volatility risk through historical implied volatility index daily returns probability distribution non-normality. Next, youll evaluate volatility hedge futures trading strategy historical risk adjusted performance using related hedged equity volatility futures strategy benchmark index replicating ETF or ETN.After that, youll estimate option call and put prices through Black and Scholes, binomial trees models together with related option Greeks. Next, youll assess asset returns risk through historical stock index daily returns probability distribution non-normality. Finally, youll evaluate covered call or buy write, cash secured short put or put write and volatility tail hedge options trading strategies historical risk adjusted performance using related buy write, put write and hedged equity volatility options strategy benchmark indexes and replicating ETFs."
Price: 49.99 |
| "Multiple Regression Analysis with Excel" |
| "Full Course Content Last Update 09/2019Learn multiple regression analysis through a practical course with Microsoft Excel using stocks, rates, prices and macroeconomic 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 Multiple Regression Analysis Expert in this Practical Course with ExcelDefine stocks dependent or explained variable and calculate its mean, standard deviation, skewness and kurtosis descriptive statistics.Outline rates, prices and macroeconomic independent or explanatory variables and calculate their descriptive statistics.Analyze multiple regression statistics output through coefficient of determination or R square, adjusted R square and regression standard error metrics.Examine multiple regression analysis of variance through regression, residuals and total degrees of freedom, sum of squares, mean square error, regression F statistic and regression p-value.Review multiple regression coefficients through their values, standard errors, t statistics and regression coefficients p-values.Evaluate regression correct specification through individual coefficients statistical significance and correct it through backward elimination stepwise regression.Assess regression no linear dependency through multicollinearity test and correct it through correct specification re-evaluation.Appraise regression correct functional form through Ramsey-RESET test and correct it through non-linear quadratic, logarithmic or reciprocal variables transformations.Evaluate residuals no autocorrelation through Breusch-Godfrey test and correct it by adding lagged dependent variable data as independent variables to original regression.Assess residuals homoscedasticity through White test and correct it through heteroscedasticity consistent standard errors estimation.Appraise residuals normality through Jarque-Bera test.Evaluate regression forecasting accuracy by comparing it with random walk and arithmetic mean benchmarks through mean absolute error, root mean square error and mean absolute percentage error metrics.Become a Multiple Regression Analysis Expert and Put Your Knowledge in PracticeLearning multiple regression analysis is indispensable for business data science 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 science, applied statistics, economics, econometrics or quantitative finance. And it is necessary for any business forecasting research.But as learning curve can become steep as complexity grows, this course helps by leading you through step by step using stocks, rates, prices and macroeconomic historical data for multiple regression analysis to achieve greater effectiveness.Content and OverviewThis practical course contains 35 lectures and 4.5 hours of content. Its designed for all multiple regression analysis knowledge levels and a basic understanding of Microsoft Excel is useful but not required.At first, youll learn how to perform multiple regression analysis operations using built-in functions and array calculations. Next, youll learn how to do multiple linear regression calculation using Microsoft Excel Add-in.Then, youll define stocks dependent or explained variable. Next, youll define independent or explanatory variables through their rates, prices and macroeconomic areas. After that, youll calculate dependent and independent variables mean, standard deviation, skewness and kurtosis descriptive statistics. Next, youll analyze multiple regression statistics analysis through coefficient of determination or R square, adjusted R square and regression standard error metrics. Then, youll analyze multiple regression analysis of variance or ANOVA through regression, residuals and total degrees of freedom, sum of squares, mean square error, regression F statistic and regression p-value. Later, youll analyze multiple regression coefficient analysis through regression coefficients values, standard errors, t statistics and regression coefficients p-values.After that, youll evaluate multiple regression correct specification through coefficients individual statistical significance and correct it through backward elimination stepwise regression. Then, youll evaluate multiple regression independent variables no linear dependence through multicollinearity test and correct it through correct specification re-evaluation. Next, youll evaluate multiple regression correct functional form through Ramsey-RESET linearity test and correct it through non-linear quadratic, logarithmic and reciprocal transformations of variables. Later, youll evaluate multiple regression residuals no autocorrelation through Breusch-Godfrey test and correct it by including lagged dependent variable data as independent variables in original regression. After that, youll evaluate multiple regression residuals homoscedasticity through White test and correct it through heteroscedasticity consistent standard errors estimation. Then, youll evaluate multiple regression residuals normality through Jarque-Bera test.Later, youll evaluate multiple regression forecasting accuracy by dividing data into training and testing ranges. After that, youll use training range for fitting best model by going through steps described in previous sections. Then, youll use best fitting model coefficient values to forecast through testing range. Finally, youll evaluate testing range forecasted values accuracy by comparing it with random walk and arithmetic mean benchmarks through mean absolute error, root mean square error and mean absolute percentage error metrics."
Price: 49.99 |
| "Multiple Regression Analysis with R" |
| "Full Course Content Last Update 09/2019Learn multiple regression analysis through a practical course with R statistical software using stocks, rates, prices and macroeconomic 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 Multiple Regression Analysis Expert in this Practical Course with RDefine stocks dependent or explained variable and calculate its mean, standard deviation, skewness and kurtosis descriptive statistics.Outline rates, prices and macroeconomic independent or explanatory variables and calculate their descriptive statistics.Analyze multiple regression statistics output through coefficient of determination or R square, adjusted R square and regression standard error metrics.Examine multiple regression analysis of variance through regression, residuals and total degrees of freedom, sum of squares, mean square error, regression F statistic and regression p-value.Review multiple regression coefficients through their values, standard errors, t statistics and regression coefficients p-values.Evaluate regression correct specification through individual coefficients statistical significance and correct it through backward elimination stepwise regression.Assess regression no linear dependency through multicollinearity test and correct it through correct specification re-evaluation.Appraise regression correct functional form through Ramsey-RESET test and correct it through non-linear quadratic, logarithmic or reciprocal variables transformations.Evaluate residuals no autocorrelation through Breusch-Godfrey test and correct it by adding lagged dependent variable data as independent variables to original regression.Assess residuals homoscedasticity through White, Breusch-Pagan tests and correct it through heteroscedasticity consistent standard errors estimation.Appraise residuals normality through Jarque-Bera test.Evaluate regression forecasting accuracy by comparing it with random walk and arithmetic mean benchmarks through mean absolute error, root mean square error and mean absolute percentage error metrics.Become a Multiple Regression Analysis Expert and Put Your Knowledge in PracticeLearning multiple regression analysis is indispensable for business data science 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 science, applied statistics, economics, econometrics or quantitative finance. And it is necessary for any business forecasting research.But as learning curve can become steep as complexity grows, this course helps by leading you through step by step using stocks, rates, prices and macroeconomic historical data for multiple regression analysis to achieve greater effectiveness.Content and OverviewThis practical course contains 36 lectures and 3.5 hours of content. Its designed for all multiple regression analysis knowledge levels and a basic understanding of R statistical software is useful but not required.At first, youll learn how to read stocks, rates, prices and macroeconomic historical data to perform multiple regression analysis operations by installing related packages and running script code on RStudio IDE.Then, youll define stocks dependent or explained variable. Next, youll define independent or explanatory variables through their rates, prices and macroeconomic areas. After that, youll calculate dependent and independent variables mean, standard deviation, skewness and kurtosis descriptive statistics. Later, youll compute independent variables transformations.Next, youll analyze multiple regression statistics analysis through coefficient of determination or R square, adjusted R square and regression standard error metrics. Then, youll analyze multiple regression analysis of variance or ANOVA through regression, residuals and total degrees of freedom, sum of squares, mean square error, regression F statistic and regression p-value. Later, youll analyze multiple regression coefficient analysis through regression coefficients values, standard errors, t statistics and regression coefficients p-values.After that, youll evaluate multiple regression correct specification through coefficients individual statistical significance and correct it through backward elimination stepwise regression. Then, youll evaluate multiple regression independent variables no linear dependence through multicollinearity test and correct it through correct specification re-evaluation. Next, youll evaluate multiple regression correct functional form through Ramsey-RESET linearity test and correct it through non-linear quadratic, logarithmic and reciprocal transformations of variables. Later, youll evaluate multiple regression residuals no autocorrelation through Breusch-Godfrey test and correct it by including lagged dependent variable data as independent variables in original regression. After that, youll evaluate multiple regression residuals homoscedasticity through White, Breusch-Pagan tests and correct it through heteroscedasticity consistent standard errors estimation. Then, youll evaluate multiple regression residuals normality through Jarque-Bera test.Later, youll evaluate multiple regression forecasting accuracy by dividing data into training and testing ranges. After that, youll use training range for fitting best model by going through steps described in previous sections. Then, youll use best fitting model coefficient values to forecast through testing range. Finally, youll evaluate testing range forecasted values accuracy by comparing it with random walk and arithmetic mean benchmarks through mean absolute error, root mean square error and mean absolute percentage error metrics."
Price: 49.99 |
| "Multiple Regression Analysis with Python" |
| "Full Course Content Last Update 09/2019Learn multiple regression analysis through a practical course with Python programming language using stocks, rates, prices and macroeconomic 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 Multiple Regression Analysis Expert in this Practical Course with PythonDefine stocks dependent or explained variable and calculate its mean, standard deviation, skewness and kurtosis descriptive statistics.Outline rates, prices and macroeconomic independent or explanatory variables and calculate their descriptive statistics.Analyze multiple regression statistics output through coefficient of determination or R square and adjusted R square metrics.Examine multiple regression analysis of variance through regression, residuals and total degrees of freedom, sum of squares, mean square error, regression F statistic and regression p-value.Review multiple regression coefficients through their values, standard errors, t statistics and regression coefficients p-values.Evaluate regression correct specification through individual coefficients statistical significance and correct it through backward elimination stepwise regression.Assess regression no linear dependency through multicollinearity test and correct it through correct specification re-evaluation.Appraise regression correct functional form through Ramsey-RESET test and correct it through non-linear quadratic, logarithmic or reciprocal variables transformations.Evaluate residuals no autocorrelation through Breusch-Godfrey test and correct it by adding lagged dependent variable data as independent variables to original regression.Assess residuals homoscedasticity through White, Breusch-Pagan tests and correct it through heteroscedasticity consistent standard errors estimation.Appraise residuals normality through Jarque-Bera test.Evaluate regression forecasting accuracy by comparing it with random walk and arithmetic mean benchmarks through mean absolute error, mean square error and root mean square error metrics.Become a Multiple Regression Analysis Expert and Put Your Knowledge in PracticeLearning multiple regression analysis is indispensable for business data science 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 science, applied statistics, economics, econometrics or quantitative finance. And it is necessary for any business forecasting research.But as learning curve can become steep as complexity grows, this course helps by leading you through step by step using stocks, rates, prices and macroeconomic historical data for multiple regression analysis to achieve greater effectiveness.Content and OverviewThis practical course contains 36 lectures and 4 hours of content. Its designed for all multiple regression analysis knowledge levels and a basic understanding of Python programming language is useful but not required.At first, youll learn how to read stocks, rates, prices and macroeconomic historical data to perform multiple regression analysis operations by installing related packages and running code on Python PyCharm IDE.Then, youll define stocks dependent or explained variable. Next, youll define independent or explanatory variables through their rates, prices and macroeconomic areas. After that, youll calculate dependent and independent variables mean, standard deviation, skewness and kurtosis descriptive statistics. Later, youll compute independent variables transformations.Next, youll analyze multiple regression statistics analysis through coefficient of determination or R square and adjusted R square metrics. Then, youll analyze multiple regression analysis of variance or ANOVA through regression, residuals and total degrees of freedom, sum of squares, mean square error, regression F statistic and regression p-value. Later, youll analyze multiple regression coefficient analysis through regression coefficients values, standard errors, t statistics and regression coefficients p-values.After that, youll evaluate multiple regression correct specification through coefficients individual statistical significance and correct it through backward elimination stepwise regression. Then, youll evaluate multiple regression independent variables no linear dependence through multicollinearity test and correct it through correct specification re-evaluation. Next, youll evaluate multiple regression correct functional form through Ramsey-RESET linearity test and correct it through non-linear quadratic, logarithmic and reciprocal transformations of variables. Later, youll evaluate multiple regression residuals no autocorrelation through Breusch-Godfrey test and correct it by including lagged dependent variable data as independent variables in original regression. After that, youll evaluate multiple regression residuals homoscedasticity through White, Breusch-Pagan tests and correct it through heteroscedasticity consistent standard errors estimation. Then, youll evaluate multiple regression residuals normality through Jarque-Bera test.Later, youll evaluate multiple regression forecasting accuracy by dividing data into training and testing ranges. After that, youll use training range for fitting best model by going through steps described in previous sections. Then, youll use best fitting model coefficient values to forecast through testing range. Finally, youll evaluate testing range forecasted values accuracy by comparing it with random walk and arithmetic mean benchmarks through mean absolute error, mean square error and root mean square error metrics."
Price: 49.99 |
| "Pairs Trading Analysis with R" |
| "Full Course Content Last Update 12/2018Learn pairs trading analysis through a practical course with R statistical software using MSCI countries indexes ETFs 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 a Pairs Trading Analysis Expert in this Practical Course with RRead or download MSCI Countries Indexes ETF prices data and perform pairs trading analysis operations by installing related packages and running script code on RStudio IDE.Identify pairs of international countries stock indexes prices with similar behavior based on fundamental factors of countries with comparable economies which have relevant commodities sector and countries from specific region.Test pairs short term statistical relationship through their price returns correlation coefficient.Assess single pairs spread co-integration or long term statistical relationship through Engle-Granger test.Evaluate if paired assets prices spread is stationary after testing individual price time series are non-stationary and individual price time series differences are stationary through augmented Dickey-Fuller and Phillips-Perron tests.Measure multiple pairs spread vectors co-integration or long term statistical relationship through Johansen test.Calculate trading strategies for co-integrated pairs spreads.Generate entry or exit trading signals based on rolling spread normalized time series or z-score crossing certain bands thresholds.Produce long or short trading positions associated to trading signals.Assess trading strategies performance against buy and hold benchmarks using annualized return, annualized standard deviation, annualized Sharpe ratio metrics and cumulative returns, maximum drawdown charts.Become a Pairs Trading Analysis Expert and Put Your Knowledge in PracticeLearning pairs trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors quantitative trading research and development.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using MSCI Countries Indexes ETF prices historical data for back-testing to achieve greater effectiveness. Content and OverviewThis practical course contains 49 lectures and 6 hours of content. Its designed for all pairs trading 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 MSCI Countries Indexes ETF prices historical data to perform pairs trading analysis operations by installing related packages and running script code on RStudio IDE.Then, youll identify pairs for international countries stock indexes prices with similar behavior based on fundamental factors of countries with comparable economies which have relevant commodities sector and from specific region. After that, youll test pairs short term statistical relationship through their price returns correlation coefficient.Next, youll asses single pairs spread co-integration or long term statistical relationship through Engle-Granger test. Later, youll evaluate if paired asset prices spread is stationary after testing individual price time series are non-stationary and individual price time series differences are stationary through augmented Dickey-Fuller and Phillips-Perron tests. Then, youll evaluate multiple pairs spread vectors co-integration or long term statistical relationship through Johansen test.After that, youll calculate co-integrated pair spreads trading strategies. Next, youll generate entry or exit trading signals based on rolling spread normalized time series or z-score crossing certain bands thresholds. Later, youll produce long or short trading positions based on previously generated trading signals.Finally, youll measure trading strategies performance against individual paired stock indexes buy and hold benchmarks through annualized return, annualized standard deviation, annualized Sharpe ratio and cumulative returns, maximum drawdown charts."
Price: 49.99 |
| "Pairs Trading Analysis with Python" |
| "Full Course Content Last Update 12/2018Learn pairs trading analysis through a practical course with Python programming language using MSCI countries indexes ETFs 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 a Pairs Trading Analysis Expert in this Practical Course with PythonRead or download MSCI Countries Indexes ETF prices data and perform pairs trading analysis operations by installing related packages and running code on Python IDE.Identify pairs of international countries stock indexes prices with similar behavior based on fundamental factors of countries with comparable economies which have relevant commodities sector and countries from specific region.Test pairs short term statistical relationship through their price returns correlation coefficient.Assess single pairs spread co-integration or long term statistical relationship through Engle-Granger test.Evaluate if paired assets prices spread is stationary after testing individual price time series are non-stationary and individual price time series differences are stationary through augmented Dickey-Fuller and Phillips-Perron tests.Calculate trading strategies for co-integrated pairs spreads.Generate entry or exit trading signals based on rolling spread normalized time series or z-score crossing certain bands thresholds.Produce long or short trading positions associated to trading signals.Assess trading strategies performance against buy and hold benchmarks using annualized return, annualized standard deviation, annualized Sharpe ratio metrics and cumulative returns chart.Become a Pairs Trading Analysis Expert and Put Your Knowledge in PracticeLearning pairs trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors quantitative trading research and development.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using MSCI Countries Indexes ETF prices historical data for back-testing to achieve greater effectiveness. Content and OverviewThis practical course contains 45 lectures and 6 hours of content. Its designed for all pairs trading 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 MSCI Countries Indexes ETF prices historical data to perform pairs trading analysis operations by installing related packages and running code on Python IDE.Then, youll identify pairs for international countries stock indexes prices with similar behavior based on fundamental factors of countries with comparable economies which have relevant commodities sector and from specific region. After that, youll test pairs short term statistical relationship through their price returns correlation coefficient.Next, youll asses single pairs spread co-integration or long term statistical relationship through Engle-Granger test. Later, youll evaluate if paired asset prices spread is stationary after testing individual price time series are non-stationary and individual price time series differences are stationary through augmented Dickey-Fuller and Phillips-Perron tests.After that, youll calculate co-integrated pair spreads trading strategies. Next, youll generate entry or exit trading signals based on rolling spread normalized time series or z-score crossing certain bands thresholds. Later, youll produce long or short trading positions based on previously generated trading signals.Finally, youll measure trading strategies performance against individual paired stock indexes buy and hold benchmarks through annualized return, annualized standard deviation, annualized Sharpe ratio and cumulative returns chart."
Price: 49.99 |
| "Volatility Trading Analysis with Python" |
| "Full Course Content Last Update 11/2018Learn volatility trading analysis through a practical course with Python programming language using CBOE and S&P 500 volatility strategies benchmark indexes and replicating ETFs or ETNs historical data for risk adjusted performance back-testing. It explores main concepts from advanced 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 sophisticated investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.Become a Volatility Trading Analysis Expert in this Practical Course with PythonRead or download CBOE and S&P 500 volatility strategies benchmark indexes and replicating funds data to perform historical volatility trading analysis by installing related packages and running code on Python IDE.Estimate historical or realized volatility through close to close, Parkinson, Garman-Klass, Rogers-Satchell, and Garman-Klass-Yang-Zhang metrics.Calculate forecasted volatility through seasonal random walk, historical mean, simple moving average, exponentially weighted moving average, autoregressive integrated moving average and general autoregressive conditional heteroscedasticity models.Measure market participants implied volatility through related volatility index.Estimate futures prices and explore volatility and asset returns correlation, volatility risk premium, volatility term structure and volatility skew patterns.Assess volatility hedge futures trading strategyhistorical risk adjusted performance using related hedged equity volatility futures strategy benchmark index replicating ETF or ETN.Approximate options call and put prices through Black and Scholes model together with related option Greeks.Evaluate buy write, put write and volatility tail hedge options trading strategies historical risk adjusted performance using related buy write, put write and hedged equity volatility options strategy benchmark indexes and replicating ETFs.Become a Volatility Trading Analysis Expert and Put Your Knowledge in PracticeLearning volatility trading analysis is indispensable for finance careers in areas such as derivatives research, derivatives development, and derivatives trading mainly within investment banks and hedge funds. It is also essential for academic careers in derivatives finance. And it is necessary for experienced sophisticated investors volatility trading strategies research.But as learning curve can become steep as complexity grows, this course helps by leading you step by step using CBOE and S&P 500 volatility strategies benchmark indexes and replicating ETFs or ETNs historical data for risk adjusted performance back-testing to achieve greater effectiveness.Content and OverviewThis practical course contains 44 lectures and 6 hours of content. Its designed for advanced volatility trading analysis knowledge level and a basic understanding of Python programming language is useful but not required.At first, youll learn how to read or download CBOE and S&P 500 volatility strategies benchmark indexes and replicating ETFs or ETNs data to perform historical volatility trading analysis by installing related packages and running code on Python IDE.Then, youll do volatility analysis by estimating historical or realized volatility through close to close, Parkinson, Garman-Klass, Rogers-Satchell and Garman-Klass-Yang-Zhang metrics. After that, youll use these estimations to forecast volatility through seasonal random walk, historical mean, simple moving average, exponentially weighted moving average, autoregressive integrated moving average and general autoregressive conditional heteroscedasticity models. Next, youll measure market participants implied volatility through related volatility index.Later, youll estimate futures prices and compare them with actual historical data. Then, youll explore volatility and asset returns correlation, volatility risk premium, volatility term structure and volatility skew patterns. After that, youll assess volatility risk through historical implied volatility index daily returns probability distribution non-normality. Next, youll evaluate volatility hedge futures trading strategy historical risk adjusted performance using related hedged equity volatility futures strategy benchmark index replicating ETF or ETN.After that, youll estimate option call and put prices through Black and Scholes model together with related option Greeks. Next, youll assess asset returns risk through historical stocks index daily returns probability distribution non-normality. Finally, youll evaluate covered call or buy write, cash secured short put or put write and volatility tail hedge options trading strategies historical risk adjusted performance using related buy write, put write and hedged equity volatility options strategy benchmark indexes and replicating ETFs."
Price: 49.99 |
| "Machine Trading Analysis with R" |
| "Learn machine trading 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 your research as experienced investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.Become a Machine Trading Analysis Expert in this Practical Course with RRead or download S&P 500 Index ETF prices data and perform machine trading analysis operations by installing related packages and running script code on RStudio IDE.Define target and predictor algorithm features for supervised regression machine learning task.Select relevant predictor features subset through univariate filter methods, deterministic wrapper methods and embedded methods.Implement Student t-test, ANOVA F-test for univariate methods, recursive feature elimination for deterministic wrapper methods and least absolute shrinkage and selection operator for embedded methods.Extract predictor features transformations through principal component analysis.Train algorithm for mapping optimal relationship between target and predictor features through ensemble methods, maximum margin methods and multi-layer perceptron methods.Apply extreme gradient boosting machine regression for ensemble methods, radial basis function support vector machine regression for maximum margin methods and artificial neural network regression for multi-layer perceptron methods.Test algorithm for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics.Assess mean absolute error, root mean squared error for scale-dependent metrics and mean absolute percentage error, mean absolute scaled error for scale-independent metrics.Calculate machine trading strategies for algorithms with highest forecasting accuracy.Generate buy or sell trading signals based on target feature prediction crossing centerline cross-over threshold.Produce long-only trading positions associated to trading signals.Evaluate machine trading strategies performance against buy and hold benchmark using annualized return, annualized standard deviation, annualized Sharpe ratio metrics and cumulative returns, maximum drawdown charts.Become a Machine Trading Analysis Expert and Put Your Knowledge in PracticeLearning machine trading analysis is indispensable for finance careers in areas such as computational finance research, computational finance development, and computational finance trading mainly within investment banks and hedge funds. It is also essential for academic careers in computational finance. And it is necessary for experienced investors computational finance 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 5 hours of content. Its designed for all machine trading 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 machine trading analysis operations by installing related packages and running script code on RStudio IDE.Then, youll define target and predictor features for supervised regression machine learning task. After that, youll select relevant predictor features subset through univariate filter methods, deterministic wrapper methods and embedded methods. Next, youll implement Student t-test, ANOVA F-test for univariate methods, recursive feature elimination for deterministic wrapper methods and least absolute shrinkage and selection operator for embedded methods. Later, youll extract predictor features transformations through principal component analysis.Next, youll train algorithm for mapping optimal relationship between target and predictor features through ensemble methods, maximum margin methods and multi-layer perceptron methods. Then, youll apply gradient boosting machine regression for ensemble methods, radial basis function support vector machine regression for maximum margin methods and artificial neural network regression for multi-layer perceptron methods. After that, youll test algorithm for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics. Later, youll assess mean absolute error, root mean squared error for scale-dependent metrics and mean absolute percentage error, mean absolute scaled error for scale-independent metrics.After that, youll calculate machine trading strategies for algorithms with highest forecasting accuracy. Then, youll generate buy or sell trading signals based on target feature prediction crossing centerline cross-over threshold. Next, youll produce long-only trading positions associated to trading signals.Finally, youll measure machine trading strategies performance against buy and hold benchmark through annualized return, annualized standard deviation, annualized Sharpe ration and cumulative returns, maximum drawdown charts"
Price: 49.99 |
| "Advanced Forecasting Models with Python" |
| "Learn advanced forecasting models through a practical course with Python programming language using S&P 500 Index ETF prices historical data. It explores main concepts from proficient to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your advanced investment management or sales forecasting research. All of this while exploring the wisdom of best academics and practitioners in the field.Become an Advanced Forecasting Models Expert in this Practical Course with PythonRead S&P 500 Index ETF prices data and perform advanced forecasting models operations by installing related packages and running code on Python PyCharm IDE.Identify Box-Jenkins autoregressive integrated moving average model integration order through level and differentiated time series first order trend stationary augmented Dickey-Fuller unit root test.Recognize autoregressive integrated moving average model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions.Estimate autoregressive integrated moving average models such as random walk with drift and differentiated first order autoregressive.Identify seasonal autoregressive integrated moving average model seasonal integration order through level and seasonally differentiated time series first order seasonal stationary deterministic test.Estimate seasonal autoregressive integrated moving average models such as seasonal random walk with drift and seasonally differentiated first order autoregressive.Select non-seasonal or seasonal 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.Identify general autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average model squared residuals or forecasting errors second order stationary Engle autoregressive conditional heteroscedasticity test.Recognize non-Gaussian general autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average and general autoregressive conditional heteroscedasticity model with highest forecasting accuracy standardized residuals or forecasting errors multiple order stationary Jarque-Bera normality test.Estimate autoregressive integrated moving average models with residuals or forecasting errors assumed as Gaussian or Student-t distributed and with Bollerslev simple, Nelson exponential or Glosten-Jagannathan-Runkle threshold general autoregressive conditional heteroscedasticity effects such as random walk with drift and differentiated first order autoregressive.Assess autoregressive integrated moving average model with highest forecasting accuracy standardized residuals or forecasting errors strong white noise modelling requirement.Become an Advanced Forecasting Models Expert and Put Your Knowledge in PracticeLearning advanced forecasting models is indispensable for finance careers in areas such as portfolio management and risk management. It is also essential for academic careers in advanced applied statistics, econometrics and quantitative finance. And its necessary for advanced sales 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 advanced forecast modelling to achieve greater effectiveness.Content and OverviewThis practical course contains 46 lectures and 7 hours of content. Its designed for advanced forecasting models knowledge level 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 advanced forecasting models operations by installing related packages and running code on Python PyCharm IDE.Then, youll define Box-Jenkins autoregressive integrated moving average models. Next, youll identify autoregressive integrated moving average models integration order through level and differentiated time series first order trend stationary augmented Dickey-Fuller unit root test. After that, youll identify autoregressive integrated moving average models autoregressive and moving average orders through autocorrelation and partial autocorrelation functions. For autoregressive integrated moving average models, youll define random walk with drift and differentiated first order autoregressive models. Later, youll define seasonal autoregressive integrated moving average models. Then, youll identify seasonal autoregressive integrated moving average models seasonal integration order through level and seasonally differentiated time series first order seasonal stationary deterministic test. Next, youll identify seasonal autoregressive integrated moving average models seasonal autoregressive and seasonal moving average orders through autocorrelation and partial autocorrelation functions. For seasonal autoregressive integrated moving average models, youll define seasonal random walk with drift and seasonally differentiated first order autoregressive. After that, youll select non-seasonal or seasonal autoregressive integrated moving average model with lowest information loss criteria. For information loss criteria, youll define Akaike and Schwarz Bayesian information criteria. Later, youll evaluate autoregressive integrated moving average models forecasting accuracy. For forecasting accuracy metrics, youll define scale-dependent mean absolute error and root mean squared error.Next, youll define general autoregressive conditional heteroscedasticity models. Then, youll identify general autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average model squared residuals or forecasting errors second order stationary Engle autoregressive conditional heteroscedasticity test. After that, youll identify general autoregressive conditional heteroscedasticity model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions. Later, youll define autoregressive integrated moving average models with residuals or forecasting errors assumed as Gaussian or normally distributed and with Bollerslev simple, Nelson exponential or Glosten-Jagannathan-Runkle threshold general autoregressive conditional heteroscedasticity effects. For general autoregressive conditional heteroscedasticity models, youll define random walk with drift and differentiated first order autoregressive. Then, youll evaluate general autoregressive conditional heteroscedasticity models forecasting accuracy.After that, youll define non-Gaussian general autoregressive conditional heteroscedasticity models. Next, youll identify non-Gaussian general autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average and general autoregressive conditional heteroscedasticity model with highest forecasting accuracy standardized residuals or forecasting errors multiple order stationary Jarque-Bera normality test. Then, youll define autoregressive integrated moving average models with residuals or forecasting errors assumed as Student-t distributed and with Bollerslev simple, Nelson exponential or Glosten-Jagannathan-Runkle threshold general autoregressive conditional heteroscedasticity effects. Later, youll evaluate non-Gaussian general autoregressive conditional heteroscedasticity models forecasting accuracy. Finally, youll evaluate autoregressive integrated moving average and non-Gaussian general autoregressive conditional heteroscedasticity model with highest forecasting accuracy standardized residuals or forecasting errors strong white noise modelling requirement"
Price: 49.99 |
| "Business Statistics with R" |
| "Learn business statistics 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 statistics research. All of this while exploring the wisdom of best academics and practitioners in the field.Become a Business Statistics Expert in this Practical Course with RRead S&P 500 Index ETF prices data and perform business statistics operations by installing related packages and running script code on RStudio IDE.Chart absolute frequency, relative frequency, cumulative absolute frequency and cumulative relative frequency histograms.Approximate sample mean, sample median central tendency measures and sample standard deviation, sample variance, sample mean absolute deviation dispersion measures.Estimate sample skewness, sample kurtosis frequency distribution shape measures and samples correlation, samples covariance association measures.Define normal probability distribution, standard normal probability distribution and Students t probability distribution for several degrees of freedom alternatives. Evaluate probability distribution goodness of fit through Kolmogorov-Smirnov, Cramer-von Mises and Anderson Darling tests.Approximate population mean, population proportion and bootstrap population mean point estimations.Estimate population mean, population proportion and bootstrap population mean confidence intervals assuming known or unknown population variance.Calculate population mean sample size assuming known or unknown population variance for specific margin of error.Approximate population mean two tails, right tail and population proportion left tail statistical inference tests probability values.Estimate paired populations means assuming equal population variances two tail statistical inference test probability value.Assess population mean two tails statistical inference test power for several levels of statistical significance or confidence alternatives.Become a Business Statistics Expert and Put Your Knowledge in PracticeLearning business statistics is indispensable for data science applications in areas such as consumer analytics, finance, banking, health care, e-commerce or social media. It is also essential for academic careers in applied statistics or quantitative finance. And it is necessary for business statistics 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 business statistics analysis to achieve greater effectiveness. Content and OverviewThis practical course contains 36 lectures and 4 hours of content. Its designed for all business statistics 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 business statistics operations by installing related packages and running script code on RStudio IDE.Then, youll define descriptive statistics. Next, youll define quantitative data, data population and data sample. After that, youll define absolute frequency distribution and relative frequency distribution or empirical probability. For frequency distributions, youll do frequency, density, cumulative frequency and cumulative density histograms. Later, youll define central tendency measures. For central tendency measures, youll estimate sample mean and sample median. Then, youll define dispersion measures. For dispersion measures, youll estimate sample standard deviation, sample variance and sample mean absolute deviation or sample average deviation. Next, youll define frequency distribution shape measures. For frequency distribution shape measures, youll estimate sample skewness and sample kurtosis. Then, youll define association measures. For association measures, youll estimate samples correlation and samples covariance. Next, youll define probability distributions. Then, youll define theoretical and empirical probability distributions. After that, youll define continuous random variable and continuous probability distribution. Later, youll define normal probability distribution, standard normal probability distribution and Students t probability distribution for several degrees of freedom alternatives. Then, youll define probability distribution goodness of fit testing. For probability distribution goodness of fit testing, youll do Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling evaluations.After that, youll define parameters estimation statistical inference. Next, youll define theoretical and bootstrap mean probability distributions. Then, youll define point estimation. For point estimation, youll do population mean, population proportion and bootstrap population mean point estimations. After that, youll define confidence interval estimation. For confidence interval estimation, youll do population mean, population proportion and bootstrap population mean confidence intervals estimation assuming known and unknown population variance. Later, youll define sample size estimation. For sample size estimation, youll do population mean sample size estimation assuming known and unknown population variance for specific margin of error.Later, youll define parameters hypothesis testing statistical inference. Next, youll define probability value. For probability value, youll do population mean two tails and right tail tests. Also, for probability value, youll do population proportion left tail test. Additionally, for probability value, youll do paired populations means two tails test assuming equal populations variances. Finally, youll define statistical power, type I error, type II error, type I error probability and type II error probability. For statistical power, youll do population mean two tails tests for several statistical significance or confidence levels."
Price: 49.99 |
| "Business Statistics with Python" |
| "Learn business statistics 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 statistics research. All of this while exploring the wisdom of best academics and practitioners in the field.Become a Business Statistics Expert in this Practical Course with PythonRead S&P 500 Index ETF prices data and perform business statistics operations by installing related packages and running code on Python PyCharm IDE.Chart absolute frequency, relative frequency, cumulative absolute frequency and cumulative relative frequency histograms.Approximate sample mean, sample median central tendency measures and sample standard deviation, sample variance, sample mean absolute deviation dispersion measures.Estimate sample skewness, sample kurtosis frequency distribution shape measures and samples correlation, samples covariance association measures.Define normal probability distribution, standard normal probability distribution and Students t probability distribution for several degrees of freedom alternatives. Evaluate probability distribution goodness of fit through Kolmogorov-Smirnov and Anderson Darling tests.Approximate population mean, population proportion and bootstrap population mean point estimations.Estimate population mean, population proportion and bootstrap population mean confidence intervals assuming known or unknown population variance.Calculate population mean sample size assuming known or unknown population variance for specific margin of error.Approximate population mean two tails, right tail and population proportion left tail statistical inference tests probability values.Estimate paired populations means two tails statistical inference test probability value.Assess population mean two tails statistical inference test power for several levels of statistical significance or confidence alternatives.Become a Business Statistics Expert and Put Your Knowledge in PracticeLearning business statistics is indispensable for data science applications in areas such as consumer analytics, finance, banking, health care, e-commerce or social media. It is also essential for academic careers in applied statistics or quantitative finance. And it is necessary for business statistics 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 business statistics analysis to achieve greater effectiveness. Content and OverviewThis practical course contains 38 lectures and 5 hours of content. Its designed for all business statistics 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 business statistics operations by installing related packages and running code on Python PyCharm IDE.Then, youll define descriptive statistics. Next, youll define quantitative data, data population and data sample. After that, youll define absolute frequency distribution and relative frequency distribution or empirical probability. For frequency distributions, youll do frequency, density, cumulative frequency and cumulative density histograms. Later, youll define central tendency measures. For central tendency measures, youll estimate sample mean and sample median. Then, youll define dispersion measures. For dispersion measures, youll estimate sample standard deviation, sample variance and sample mean absolute deviation or sample average deviation. Next, youll define frequency distribution shape measures. For frequency distribution shape measures, youll estimate sample skewness and sample kurtosis. Then, youll define association measures. For association measures, youll estimate samples correlation and samples covariance. Next, youll define probability distributions. Then, youll define theoretical and empirical probability distributions. After that, youll define continuous random variable and continuous probability distribution. Later, youll define normal probability distribution, standard normal probability distribution and Students t probability distribution for several degrees of freedom alternatives. Then, youll define probability distribution goodness of fit testing. For probability distribution goodness of fit testing, youll do Kolmogorov-Smirnov and Anderson-Darling evaluations.After that, youll define parameters estimation statistical inference. Next, youll define theoretical and bootstrap mean probability distributions. Then, youll define point estimation. For point estimation, youll do population mean, population proportion and bootstrap population mean point estimations. After that, youll define confidence interval estimation. For confidence interval estimation, youll do population mean, population proportion and bootstrap population mean confidence intervals estimation assuming known and unknown population variance. Later, youll define sample size estimation. For sample size estimation, youll do population mean sample size estimation assuming known and unknown population variance for specific margin of error.Later, youll define parameters hypothesis testing statistical inference. Next, youll define probability value. For probability value, youll do population mean two tails and right tail tests. Also, for probability value, youll do population proportion left tail test. Additionally, for probability value, youll do paired populations means two tails test. Finally, youll define statistical power, type I error, type II error, type I error probability and type II error probability. For statistical power, youll do population mean two tails tests for several statistical significance or confidence levels."
Price: 49.99 |
| "CCNA R&S 200-125 - Tecnologas LAN Switching y VLANs" |
| "Este curso aborda las tecnologa LAN Switching que son tratadas en el examen Cisco CCNA R&S 200-125.Con un enfoque terico y prctico se tocan los temas relacionados a:Conceptos de switchingFormato de la trama EhternetResolucin de problemas de interfaces y cablesConfiguracin de VLANsConectividad Interswitch El protocolo Spanning Tree (STP)Opciones de STPProtocolos de Capa 2Etherchannel Capa 2 y Capa 3Cada tema se toma desde lo ms bsico a lo ms avanzado para alcanzar los conocimientos y prcticas necesarias para la preparacin del examen CCNA""CCNA R&S 200-125 - Tecnologas LAN Switching y VLANs es un curso de aprendizaje de switching desde 0 hasta avanzado para la preparacin del examen de Cisco CCNA 200-125. A lo largo del curso, trataremos distintos tpicos de aprendizaje que son preguntados en el examen Cisco CCNA 200-125, te guiar por el proceso paso a paso para que aprendas de la forma ms eficiente y eficaz posible, sobre temas de switching y Vlans"".Inscrbete!, y preprate de la mejor manera para la certificacin Cisco."
Price: 69.99 |
| "Configuracin bsica de switches Cisco para principiantes" |
| "Con ganas de aprender comandos bsicos de Cisco?Tienes muy poco o nulo conocimientos de configuracin de Switches Cisco?Quieres aprender o iniciar una carrera Cisco?Este curso est diseado para aprender a configurar switch Cisco desde cero y paso a paso. De una manera secuencial, desde la configuracin del nombre de un switch, hasta la configuracin de una red bsica de switches Cisco.Pensado en estudiantes que se estn iniciando en el mundo de la configuracin de switch Cisco por medio de su CLI (Command Line Interface) y que necesiten armar redes en sus lugares de trabajo, laboratorios de aprendizaje, preparacin para la certificacin Cisco CCNA, iniciar una carrera Cisco, etc.Este curso ejecutar cada una de las configuraciones paso a paso, de tal manera que el estudiante pueda aprender cada una de las configuraciones Cisco, y las pueda practicar en switches reales o simulados por medio de ""Cisco Packet Tracer"" y/o ""GNS3"".Adems de la comprensin de cada comando, se tocarn comandos de diagnstico, de tal manera, que los estudiantes sean capaces de encontrar errores de implementacin y configuracin en sus proyectos de networking.Este curso toca temas bsicos de networking, esenciales para emprender el aprendizaje y el manejo de conceptos bsicos de configuracin de switches Cisco. Todos los temas fueron cuidadosamente seleccionados para que el estudiante aprendan las bases de la configuracin de dispositivos Cisco. Si quiere aprender la base de los switches Cisco, este es TU CURSO.No esperes ms y empecemos a aprender juntos desde hoy mismo!"
Price: 49.99 |
| "How to Learn Smarter, Memorize More & Read Faster" |
| "What if I told you that the way you learn is wrong?...It's asad statementif you think about it.The formal education system forces us to learn tons of information without ever teaching us one ESSENTIAL skill: Learning how to learn.Ever since I began my journey of building a life worth living I was confronted with mountains and mountains of information coming my way and I did not have a SINGLE clue on how I could even get started or let alone memorize everything...This is where the fire sparked. I had to find a way out, a way to memorize more, read faster and do it in less time.At first I thought this was impossible... Guess what, it's not. This is an art. Learning how to Learn is something you can study, something you can learn.It's a skill.If you're looking to take your life to the next level or learn a new skill in record time, look no further. It all starts with knowing how to learn.This is the origin of everything.This course will teach you the 20% of things that are going to give you 80% of the results in everything that you do. No need to overwhelm you with mountains of useless information. Let's deconstruct it to the smallest piece and build up from there...Ask yourself, do you feel empowered when learning new skills or do you feel overwhelmed and don't know where to begin? Most people don't know where to begin.The best investment you can ever make is an investment in your own skills...Every single one of the world's TOP performers are masters at what they do. Masters at their skills. Masters at their craft.Did they get there by luck? Or talent? Maybe. But that doesn't help us any further now does it?What if we were to deconstruct what they do, how they learn, how they retain information and taught it to ourselves?There is one reoccurring theme in almost all of the top performers in the world: They read books. And the cream of the crop read 1 book a day.Now, before you think to yourself that you don't have the time, stick with this, this is the problem we'll be fixing.Let's be honest here for a second, we don't have the time to spend 8 hours+ just to read books in our days like Warren Buffet...Let's focus on the 20% that will get us 80% of the results.What if we were able to get the most ESSENTIAL information out of books and be able to do it in just 15 minutes a day?Sounds crazy, I know. But it sure as hell is possible. And this is what this course is built around."
Price: 119.99 |
| "Split (A/B) Testing 101 - Make Your Messaging Convert" |
| "You've got ad creative A, and ad creative B.Your gut tells you that A is best.But in marketing you should never follow your gut.In this course, you will learn:What split testing actually isHow to avoid getting false positives in your testingHacks for short-circuiting your tests and getting to a more effective result FASTERHow to apply split testing to multiple marketing platforms, such as email, video, thumbnails, buttons, descriptions, and social media like Facebook!Much, much more...Could this course change your life?Well, I'll let you be the judge of that.What I will say is that you will learn how to accurately test all kind of messages for all types of platforms against their alternative messages, and you might just choose to use that skill to work your way towards a happier life in the end."
Price: 199.99 |
| "Ubuntu Linux Fundamentals - Learn Linux Server with Ubuntu" |
| "Ubuntu Linux is consistently among the top 5 Linux Desktop and Server distributions. Learn the basics of Linux Command Line and Server Administration in this course.Updated for Ubuntu 18.04, the latest Long Term Support (LTS) version.If you understand Linux, you understand the operating system that powers much of the Internet. This beginner levelcourse will take you from knowing nothing about Linux to competency.You do NOT have to have an extra computer to load Linux on to take this course.As with all Udemy courses:You have a 30 day, no questions asked, money back guarantee if you're not fully satisfied with the course.You have lifetime full access to the course and all updates and additions.Ubuntu's a great place to start learning Linux. It's a well maintained, full featured, well documented and supported, free operating system.Unleash the power of Ubuntu's command line tools.By the end of this course, you'll know:What Linux isWhat Distributions or Distro's areWhen Ubuntu's a fit and when it's notHow to install Ubuntu Server and DesktopInstallation on VirtualBox is included in the courseVirtualBox is free software that lets you run other Operating Systems with an application on your computerThere's no need to find or buy an old computer to run Ubuntu onVirtualBox lets you try Ubuntu or any other Operating System without riskHow to navigate the Ubuntu Desktop using the Graphical User Interface (GUI)How to keep your system up to dateWhat Shell's areHow to navigate your system at the command line using the BASH shellEssential BASH commandsManipulating files with BASHHow to manage packages with apt package managerHow to add and remove users from the systemManaging FilesFile permissionsChanging permissionsThe letter vs. numeric method of setting permissionsHow to keep contents secretChanging ownership on a fileA simple way to keep versions of system filesManaging UsersAdding and removing users (two methods)Giving a user sudo (run as root) permissionsAdding a user to groupsRemoving usersCleaning up after removalManaging GroupsHow groups are used in LinuxPrimary and secondary groupsChanging a users primary groupAdding a user to a secondary groupChanging the group that owns a fileEditing Text FilesUsing Vi Improved - vimUsing nanoSaving copies of originals for system filesHelp and supportWhat manual, or 'man' pages are and how to use themThe Linux --help system and how it can help youUbuntu online documentationUbuntu forumsLinux File System StructureFilesystem OverviewWhere to Put Things You InstallConfiguring Remote Access with Secure Shell (SSH)Remote Access OverviewPreparing Your ServerConnecting with SSHWindows - PuTTYKey Based Authentication - LInux and MACKey Based Authentication - WindowsSaving Your Key (Pageant for WindowsEditing sshd_configMoving Files to and From Your ServerUsing scp to move filesUsing wget to download from the InternetUsing curl to download or copy a whole siteManaging Your ServerUsing ps to see what's runningChecking system performance, top, htop, nmonChecking drive space, dfChecking memory usage, freeScheduling tasks, cronSecuring Your ServerDisabling unneeded servicesStopping bad guys with Fail2banEnabling your firewall (ufw)Creating a Web Server With nginx and Securing nginxInstalling and configuring nginxAdding encryption with SSL/TLSSecuring nginx's configuration fileBlocking malicious activity with Fail2banAll that and more in this course will have you walking away from the course at the end with the knowledge you need to be comfortable with Linux at home or at work.Along with System Administrators wanting to integrate Linux where it is appropriate within their environments, this course has proven useful to developers learning or working with Python, JavaScript, Web Development (HTML, CSS), Machine Learning, Java, MySQL, WordPress, Node.js, Amazon AWS, PHP, Docker, and to aspiring Ethical Hackers, Cyber Security, and DevOps to name a few."
Price: 109.99 |
