Parametric and nonparametric hypotheses testing using Python 3 advanced statistical libraries with real world data
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.
The course 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 years
It 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 Python
It uses Jupyter notebook and mark-downs to clearly document the codes and make them professional
The 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 various statistical hypotheses using Python 3. The course covers the most relevant tests about the population parameters for one, two and many samples. In addition, the course covers ANOVA (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.
Be able to confidently compute test statistical hypotheses using Python 3
Be able to interpret your tests results and draw conclusions from the data
Leverage Python as a Data scientist tool to solve hypotheses testing problems
3 Lectures
13 Lectures
12 Lectures
1 Lectures
I have over 20 years of work experience in the field of statistics as an Applied Statistician. For the last twelve years, I have also been teaching undergraduate college level statistics courses at St Petersburg College,Florida, USA.As a Data scientist, my interests lie in applying Data science techniques (Exploratory Data Analyses, Statistical analyses and other Machine Learning) to real world...
Thnak you
Very good - lots of good examples, well explained
Interesting and good material. Sometime I miss a bit more explanation about the tests but over all a good course.
is Chi-squared test of independence parametric???
TL;DR: Overall, I'm satisfied with the course as it was the thing that I was looking for, but I wouldn't recommend it to people new to stats, and the presentation of the materials is very lazy.
This course was what I was looking for: a run-through of most of the common statistical tests that are available in Python (scipy, statsmodels). The reason I gave it 4 stars is that it is surprisingly hard to find this type of course around.
However, here are a couple things I should point out:
1) If you don't have a decent background in statistics, this is not the course for you. Statistical concepts are not explained, and the various videos go over each test VERY briefly, most of the time without explaining why you would use a test instead of another.
2) The presentation is very sloppy. For clarity, I don't mean the explanations/videos, Luc is actually pretty nice as a presenter. My comment is referred to the materials:
- The notebooks are filled with inconsistencies in terms of style, as well as typos (even in the names of the tests). Some technical mistakes are present too.
- The datasets used are minimal (which is fair enough, for the sake of explaining the concepts), and used in multiple lectures. The datasets are rarely explained at all, there's no context to *most* of them
- The quizzes are very lazy: 1 question each, on marginally important topics
I had a strong understanding of the statistics going in and cared more about learning Python. I have learned a lot from using this course a good starting point for learning python for stats.
Exceptional. One of the best course I have seen in here .
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Zhengyi S.
It is what I'm looking for to get a quick overview on what tests are available in python. However, it requires a lot of knowledge to figure out what tests should be used and which parameters should I change.