Volver a Inferential Statistical Analysis with Python

3.5

24 calificaciones

•

10 revisiones

In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately.
At the end of each week, learners will apply what they’ve learned using Python within the course environment. During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera....

por RR

•Mar 07, 2019

If you are interested in statistics and statistical analysis, this course gets you grounded in the essential aspects of statistics. Excellent instructors.

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8 revisiones

por Daniel Rechitsky

•Mar 21, 2019

Good lectures but too little practice and quizzes that don't cover all the material. Very little Python.

No lecture slides or "handouts" to summarize procedures or formulae that tend to jumble together for the various scenarios you learn. Some of the lectures told us to find tables needed to do the quizzes online, no more specifications. That was very disappointing.

por Rajesh Raghavan

•Mar 07, 2019

If you are interested in statistics and statistical analysis, this course gets you grounded in the essential aspects of statistics. Excellent instructors.

por Emil Krause

•Feb 27, 2019

Do you do usability tests of your courses? Like you can test a landing page - you pick a random person to perform a certain action on your landing page, and see where they struggle or what is unclear? If you did this with this course before going live, it would benefit everyone. Right now the quality of this course is too low, concepts are not explained enough, and the assignments (especially week 3) contain wrong instructions and errors.

por Tobias Roeschl

•Feb 25, 2019

Alltogether the course was great. I learned so much and understood some principles I did not understand when having read of them before.

However in some notebooks, calculations were wrong or notbooks were missing alltogether (week 4, last jupyter notebook). Furthermore it can be annoying if you cannot trust a result of a statistical analysis in a notebook because there were other mistakes before. That's why I give you "only" 4/5 stars.

por Frank Salvador Ygnacio Rosas

•Feb 14, 2019

I really enjoyed the course.

por Iver Band

•Feb 04, 2019

Very clear and interesting lectures, but quizzes and Jupyter notebooks could benefit from some additional proofreading and pre-release testing. Material in last week is out of order. Spent a few hours some week just figuring out the mistakes with the help of the course forum.

Also, I would have liked to have a bit more background and explanation, e.g. information on why we using a particular distribution or a particular test, not just how. While a complete derivation of all the material would clearly be out of scope, other courses did a better job of introducing the theory behind their methods.

por David Zhao

•Jan 30, 2019

Great lecture content. Poor quiz design.

por Yaron Klein

•Jan 26, 2019

If you want to learn basic and inferential statistics - I would advise checking out the courses with these name from by University of Amsterdam(you can take them without taking the specialization). they are much clearer. And then if you want examples of Python code - take this course. Just check out the forums first. As of jan2019 the Python Notebook used for the week3 assessment had various problems.

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