Get yourself trained on Testing Statistical Hypotheses with this Online Training Testing Statistical Hypotheses in Data science with Python 3.
Online Training Testing Statistical Hypotheses in Data science with Python 3
While there are many courses in Python, Machine Learning and other Data science related topics, they tend to be covering several topics in a piece-meal fashion and often superficially. In other words, those courses are not laser-focused on a given topic that will provide instant mastery. This course is EXCLUSIVELY about testing parametric and non-parametric Statistical Hypotheses in Python 3. It is highly recommended for Students, Data scientists, Analysts, Programmers and Statisticians who will be using Python as the main tool for data analysis and therefore need to understand HOW Python 3 powerful scientific libraries can be effectively used to tests hypotheses that they were used to performing using R, SAS, SPSS, Matlab or other tools.Thecourse has several strengths that should not be ignored.It is hands-on, uses real world data and focuses on testing statistical hypotheses using Python 3.It is taught by an Adjunct Professor of Statistics who taught statistics for twelve yearsIt is taught by a Data Scientist with Statistics background and over twenty years of professional experience.it is extensive and cover all aspects of testing statistical hypotheses using PythonIt uses Jupyter notebook and mark-downs to clearly document the codes and make them professionalThe course uses latex to write the statistical hypotheses to help users understand what is being tested/In this course you will learn how to test variousstatistical hypotheses using Python 3. The course covers the most relevant tests about the populationparameters for one, two and many samples. In addition, the course coversANOVA(Analysis of Variance) and many non parametric tests. This course is hands-on with real world datasets to help the students understand how to carry on the various tests.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.