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This is based on their previous course (Data Management and Visualization). This course is better in terms of explaining content clearly, and I enjoyed the real-life example used when explaining about the Chi Square test. However, the python coding could be more optimized; for example, it suggests doing the Chi Square post-hoc test for each variable one by one... which can be 15 batches of dictionary recodes! Thankfully someone in the forum provided a solution for doing an automated batch testing. Maybe the course lecturers felt that a batch recode would be too complicated, but it doesn't feel like you could use their method effectively for a work environment, either. In any case, it's still a good course to explain the various data tests for quantitative and categorical data if you're new to statistics.

The instructors are pleasant, and the videos helpful. Unlike some classes where it feels like there is gulf between the toy examples covered in the lectures and what's requested in the assignments, the materials available speak directly to the homework.

The virtually non-existent discussion board, lacking much activity from either students or staff, is a real downer.

Thank you very much for creating this course. Basic concepts of Population, Sample, Sampling distribution, Sampling distribution variability, Hypothesis testing and ANOVA was really very helpful. the course is designed in a very nice way and the questions in between are of good standard.

Very clear description of basic statistics without all the jargons and mathematical formulas behind it. Unfortunately, somehow, such a good course lacks students and the discussion forum is like ghost room with virtually zero interaction.

Very informative. Tedious concepts like ANOVA, Chi square test etc taught in very simple and effective manner.

There're two options for analysis, SAS and Phython. I'd recommend readers to read more on PROC ANOVA for better understanding.

This course is very good especially for beginners getting started with SAS or python for data analytics. The lessons are very clear and easy to understand. Learnt a lot of valuable information and also enjoyed it.

This was good module. It covers the basics of inferential statistical techniques along with its application using SAS/Python. I would definitely recommend to take up if you are a beginner.

It is a good course for a complete beginner in statistical inference. It helped me to understand some points I found confusing in "Statistical Inference" Coursera course.

It's one of the best course for understanding all the statistical tools , used for data sciences. Thanks to entire team for making such a wonderful course content

It´s a really good course, I´d like it to goo deeper into the techniques but still is very useful.

Enjoyable and easy to follow along with. good videos and examples. Helped fill in some gaps.

This course is very interesting and every data scientist should take time and digest it.

Lectures are well realized (animation, change in contexts) and peer review process.

Una buena forma de introducirte en las herramientas para el análisis de datos

Videos are good and to the point.

Animation is very user friendly.

Good course, but need more direct feedback from professionals.

Very good from a conceptual point of view.

Covers a good deal of material.

Had important problems with SAS which were due to a bug in the software but did not receive any help from moderators. However the course was good and interesting.

There was no explanation about background mathematics. Grading metrics are unclear because assessment of assignments are only peer grading.

Very basic introduction and not thorough or mathematical enough.

Interesting but very superficial I would say.

We don't have to do the calculus, but some acknowledgement that mathematics, and not syntax, is actually responsible for how these tools operate would be great.

too complicated