Back to Inferential Statistical Analysis with Python

4.4

stars

220 ratings

•

45 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....

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.

Jun 22, 2019

A very in-depth learning material for inferential statistics. Very good explanation of p-value which clarifies some of the prevailing misunderstandings.

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By Emil K

•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.

By Yaron K

•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.

By Jin S

•Mar 31, 2019

This course attempts to cover very useful topics but falls short on several areas. 1. Multiple errors in the assignments. Practice exercises don't have any answers for students to check. 2. Course slides are not provided. 3. Lack of support to questions asked in forum. I learned a lot from the course but a significant amount of time could have been saved if the issues I mentioned were addressed.

By Tobias R

•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.

By David Z

•Jan 30, 2019

Great lecture content. Poor quiz design.

By Daniel R

•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.

By José A G P

•Apr 16, 2019

The course contents are good to an introduction or refreshing in statistics but the assigments are not really well prepared, and contains many unrepaired errors. This drops down the level an educational potential of this course (and the entire specialization) and converts it in a poor educational resource and a waste of time, in my opinion

By ILYA N

•Aug 24, 2019

In this course, they cover making confidence intervals and calculating p-values given a specific test scenario (compare sample proportion to population proportion, sample mean to population mean, two sample means to each other, etc). While they go though each statistical procedure clearly, I feel like a lot of underlying context is missing. What is the different between a z- and t-distribution? Why do we use those distributions? How do the different tests relate to each other? Etc. It feels like this course needed an extra 50-60 minutes of lecture time to tie all these concepts together. A textbook to follow along would have been great too.

By Iver B

•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.

By Aayush G

•Apr 26, 2019

I must say that this is a must take course for ones who are aspiring a career in Data Science. All the concepts were laid out so beautifully and it was explained very clearly with visualisations of each real-life-examples. I enrolled in this specialisation before starting my Machine Learning so that I have all the necessary fundamentals of Statistics. Brady Sir & Brendra Ma'am are simply phenomenal, the way they explain the concepts are incredible. The concepts gets etched in one's memory.

By Sagar T

•Jan 06, 2020

From the introduction, the course is supposed to build the knowledge ground up for a beginner in Statistics. However, it falls short in clearing many concepts and the principals end up being vague in a lot of sense, hence, there is a lack of cohesiveness in the concepts spread across weeks. Fortunately, I took an open course offered by Stanford University of the Inference Concepts explained in this course; before taking up this course.

Overall, this is a good course for someone who is familiar with the Inference concepts. For a beginner, a significant amount effort would be required to catch up to these concepts.

By Michael D

•May 28, 2019

This course is a good statistics course, but a poor Python course. Python is practically an after thought in each week's lesson as the focus in the lecturing learning methods is entirely verbal rather than supported by in lecture use of Python. The Python review at the end of each week before the assessment is not connected enough with the lecture materials and makes for a very disjointed week of learning.

By Vinícius G d O

•Jul 13, 2019

A complete course focused on teaching the details and intuition of experiment design, inferential analysis for decision making through confidence interval ans hypothesis testing and how to state effective questions.

I would recommend this course to everyone who are seeeking for more explainability and improvements in its ability to solve complex problems through data analysis.

By Jafed E

•Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

By Bonnie

•Aug 08, 2019

I really appreciate the course and let me accumulate a lot of knowledge about statistics. And I have developed a good impression of the University of Michigan teaching level.

By Rajesh R

•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.

By JIANG X

•Jun 22, 2019

A very in-depth learning material for inferential statistics. Very good explanation of p-value which clarifies some of the prevailing misunderstandings.

By Yaroslav B

•May 29, 2019

This course is significantly better than the previous one. Nevertheless, if you want to get knowledge about Python, it’s not about this course.

By Gabriel G B

•Dec 05, 2019

It is absolutely great. Instructors are veeeery pasionated with what they do, and the course material is very good.

I really like this course.

By Maria G

•Sep 11, 2019

It was very good course, everything was very well explained and the activities were challenging enough to practice the knowledges obtain.

By Jonas N

•Oct 14, 2019

Good Python tutorials that gives a good paratactical introduction to the theoretical core of the course.

By qiaoshuang

•Feb 24, 2020

This is the first certificate I got in Coursera. I like this course, especially then notebook part

By Parvatharajan K

•Jul 17, 2019

The content explanation is excellent and one of the best I have seen.

By EDILSON S S O J

•Jun 18, 2019

Amazing course! Very useful for all kind of Analysis!

By Varga I K

•Apr 14, 2019

Absolutely great course of inferential statistics!

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