Back to Inferential Statistical Analysis with Python

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762 ratings

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137 reviews

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....

RZ

Apr 1, 2020

This is a very great course. Statistics by itself is a very powerful tool for solving real world problems. Combine it with the knowledge of Python, there no limit to what you can achieve.

RS

Jan 21, 2021

Very good course content and mentors & teachers. The course content was very structured. I learnt a lot from the course and gained skills which will definitely gonna help me in future.

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By KOPPARTHI H H

•Mar 2, 2020

good

By Jerrold

•Nov 19, 2020

I really don't see the reason for all the hate for this course and the specialization.

Pros:

Robust syllabus on statistics and mathematics that covers all the important concepts in inferential stats

Ample example python notebook files for students to reference

High quality lectures and content

Manageable assignments and quizzes

Lots of guided examples (week4) and excellent readings written by UoM on statistics and data analysis theory and practices.

Student forum support from lecturers is excellent

Cons (minus 1 star):

While the material in this course is good, we should be given some notes with formulas and diagrams to accompany us at the start of week 2 and 3 (the hardest ones)

A person without a background in python will struggle in this specialization because you need to have programing skill and experience and the introductory practices are not enough.

You need to have some prior experience with stats or a pre-college/college year 1 text book to accompany you if this is your first time learning stats. The start-middle phase content at each chapter is explained and NOT skipped, but it could use more elaboration. I had to source elsewhere on the internet for the gaps in my knowledge (which were easily found). It is just missing a few elementary level explanations (how to calculate P values and what tests to use in different scenarios) to understand the more complex topics. I learned hypothesis testing in high school and had to refer to my textbooks for a few explanations and diagrams.

Summary:

Very satisfied with this course for what I got out of it, I gained multiple skills and a lot of familiarity with theory and examples.

By Matteo L

•Apr 5, 2020

Just like the other two courses of this specialization I believe the content offered here is great and the main methods used for statistical inference are well explained and even possibly more important, the interpretation of results is really hammered home here which is great. A few things that weren't covered thoroughly enough (if at all) in my opinion are QQplots (maybe this is more related to course 1...) and Chi-square tests (what are they and when do we use them?). Also it would have been nice to take a little bit more time to explain the differences in using t-tests and z-tests and why we would choose one over the other. I do believe the structure of the notebooks could be improved, maybe listing all of the possible functions that can be used for statistical inference for each type of scenario (e.g. functions applicable for mean of population proportion). As always, I would have loved for answers to be provided for the "extra practice" notebooks.

By Carlos M V R

•Aug 31, 2020

This course gives a lot of important concepts such as confidences intervals, p-values and hypothesis testing, but I think it is short in terms of using it in real life because the explanations rely on examples that always fulfil the same conditions and in real life it is not possible to have always the same conditions for a problem you want to study. It would be nice if the course could be complemented (in a deep way) with applications of complex samples and non-probability samples, not only single random sample. Also, python codes are not explained in a deep way.

By Wenlei Y

•Dec 17, 2019

The teaching team is great. But the assignments are not very helpful. And yes, this is more a statistics course than a python course. The application with python, which I am more interested in, seems just the supplementary portions to the lectures of concepts of statistics. There is not much introduction to how we use python to perform statistics, how we debug, and how we interpret the outcomes of programs.

By Hwanmun K

•Feb 22, 2020

It would be better to give precise definitions of each test, at least in optional reading material. Also, sometimes different lecturers used different terminologies and sometimes concepts not covered before just popped up in the video (ex. chi-square test). In general, it seems more organization in the material needed.

By Pankaj Z

•May 20, 2020

The course gives details on several stats concepts. Its one of the finest course here on Coursera. You gain a significant amount of knowledge on Statistics.

As the course progressed, I felt the content was squeezed and students were bombarded with the content without giving a real life example on them.

By Asem K

•Dec 9, 2021

Could be made more organized, like the first course in the specialization series. Seems there are some missing gaps (or assumptions of things being covered) that made it a challenge to smoothly proceed in the first 2 weeks of content.

By William O

•Jan 10, 2021

Thank you a lot. For me was an incredible course I learned many things and was very important to my career. Thanks to all the team, They are really masters.

By Yury P

•Jul 8, 2019

Good theoretical foundation, but lacks explanation on python libraries extensively used in the course.

By Felipe B

•Jan 25, 2020

the fundamentals and intuition are greatly explained. The python part feels a little rushed though.

By Harshad S M

•Aug 19, 2020

Great experience, though very helpful and happy working with the real world dataset and problems

By Faroq M M A

•Jul 15, 2021

A very good one, but it would be great if more challenging exercises and examples were added.

By Sam F

•Jan 27, 2020

Overall solid course. Could do without peer review assignment, more of a hassle than anything.

By Khaled S A

•Mar 23, 2020

Perfect Course, It was very useful to understand the basics of inferential statistics

By Kim J

•Oct 16, 2020

Good and accessible introduction to hypothesis testing and confidence intervals ...

By Bill G

•Feb 24, 2020

Need Intermediate - Advanced skill level in Python.

By k v r

•Apr 21, 2020

good examples expected to have more examples

By Kevin K

•Oct 29, 2019

Wish there were more practice problems.

By Pankaj K

•May 21, 2020

Peer Graded Assignments are a joke

By Ricardo W E

•Sep 21, 2020

very very high level statistic

By Frank S Y R

•Feb 14, 2019

I really enjoyed the course.

By Harshvardhan K

•Oct 14, 2020

I had already taken a Statistics course in my College, and took this course less to learn the concepts and more so to understand how to code Inferential Statistic in Python.

I definitely learnt how to do that at the end of the course, Confidence Intervals, Hypothesis testing, Z and T tests, etc. were taught well by the instructors.

However, many of the Lectures don't match the subsequent Quizzes ( quizzes are much easier and sometimes unrelated), and the Jupyter notebooks have you code Normal Multiplication and division of numbers to find the Intervals (for eg), instead of teaching you how to master the Scipy.Stats Module or use other powerful libraries which you will be expected to know if you land a Statistics related role in a Company.

Overall, it was a good course and knowing it's part of a specialization means you still have much to learn, but I hope the course creators make it more challenging for non-beginners and Programmers

By Sidclay J d S

•Sep 17, 2020

Statistics theory is well explained with several examples and additional resources, lectures are very clear, but it is part of a Statistics with Python Specialization, I expected to have more deep instructions about statistical part of Python (packages and strategies), there are lots of questions about Python coding and functions into the forums, I think a lecture explaining the different packages and functions would be a good idea. From my point of view the Python tutorials could also be more explored, it was too much on surface of it for me.

By Lars K

•Jan 16, 2022

Mistake in the course instructions and very redundant material. A better understanding of the concepts rather than a series of walk-throughs for different scenarios, would've been better suited to me. Recommended external resources were good. Overall, an ok course, but definitely not the best in terms of design.

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