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Learner Reviews & Feedback for Statistics for Data Science with Python by IBM

4.5
stars
366 ratings

About the Course

This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts. At the end of the course, you will complete a project to apply various concepts in the course to a Data Science problem involving a real-life inspired scenario and demonstrate an understanding of the foundational statistical thinking and reasoning. The focus is on developing a clear understanding of the different approaches for different data types, developing an intuitive understanding, making appropriate assessments of the proposed methods, using Python to analyze our data, and interpreting the output accurately. This course is suitable for a variety of professionals and students intending to start their journey in data and statistics-driven roles such as Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers. It does not require any computer science or statistics background. We strongly recommend taking the Python for Data Science course before starting this course to get familiar with the Python programming language, Jupyter notebooks, and libraries. An optional refresher on Python is also provided. After completing this course, a learner will be able to: ✔Calculate and apply measures of central tendency and measures of dispersion to grouped and ungrouped data. ✔Summarize, present, and visualize data in a way that is clear, concise, and provides a practical insight for non-statisticians needing the results. ✔Identify appropriate hypothesis tests to use for common data sets. ✔Conduct hypothesis tests, correlation tests, and regression analysis. ✔Demonstrate proficiency in statistical analysis using Python and Jupyter Notebooks....

Top reviews

JL

Jan 19, 2021

The final assignment is very well designed, I was able to review the entire course material and consolidate the learning. I have now a good understanding of hypothesis testing.

HD

Jan 13, 2021

A well structured course, simple and direct to the point, with a little of exercising you'll come out with a huge understanding of the statistical concepts.

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76 - 92 of 92 Reviews for Statistics for Data Science with Python

By Jevgēnijs I

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Jun 9, 2023

Assignments in week 7 of the course are completely unbalanced. The main questions are at the beginning , and the source data and the necessary libraries are at the end of course. There is no sequence , which increase in the time spent on the work.

By Michel M

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Apr 28, 2022

It was a decent course.

It could be more "learning by doing" oriented, there are some concepts like hypothesis testing that could be presented in other way, It'd be helpful if it had some real world examples of that.

By Akshay K

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Nov 18, 2021

I loved learning here; it was explained so well and all the modules here are too fun to learn <3

By Vishnuvardhan G

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Sep 10, 2023

The course is very very nice. Got great hands-on experience on statistics with this course.

By Moiez I

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Mar 10, 2023

The course is super useful, but I'm not a fan of the peer-reviewed portion for the project.

By Omar A

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Apr 5, 2021

I highly recommend this course for anyone that is having problems with basic statisitcs.

By Thomas S

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Mar 2, 2021

very interesting course, however, IBM Watson Studio was difficult to use

By Monika K

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Sep 11, 2023

good content, annoying work with ibm tools

By KANISHK M (

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May 16, 2023

Very nice and easy course

By STEPHEN E

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Aug 31, 2021

Good introductory course

By Anastasia S

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Jan 30, 2023

The assignments are very well prepared but the theoretical part is not clearly explained, I missed the structure, in particular, a more generic intro to every lecture and connection between lectures/slides would make it easier to follow. Also, additional animation to a table or a list of items and using a pointer to make it clear what exactly is explained at the moment would help.

By Randall H

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Apr 14, 2024

It gives pretty basic information, so there isn't depth into the topics. Also the examples are the most basic ones.

By Subham A

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Oct 12, 2022

Final Exam question on one/two tailed test is bugged. The options are all incorrect

By Mohamed S Y

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Apr 20, 2023

Need to explain in details the statistical elements and uses

By Rajeswar S

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Jun 23, 2022

very basic one , course looks like preface only. seems only want to tell the topic which are there in statatitics. there is no details provided in this course. not usefull at all.

By Paul H

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Nov 10, 2021

None of the tools work and I'm struggling to pick up the practical skills being taught. I've dropped out of this and would like my money back.

By Jason W

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Feb 14, 2021

Has very little to do with Python and all about doing statistics manually.