Chevron Left
Back to Statistics for Data Science with Python

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

263 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


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.


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.

Filter by:

26 - 50 of 61 Reviews for Statistics for Data Science with Python

By S. H M

Dec 2, 2022

By Muhammad F H

Sep 2, 2021

By k b

Feb 7, 2021

By Kalyani A

Jun 10, 2022

By Asif Y

Jan 13, 2021

By Frederico S

Jun 2, 2022

By Khusan T

Mar 30, 2021

By Adedeji Y

Oct 10, 2022

By Vaseekaran V

May 13, 2021


Oct 20, 2022


Dec 15, 2021

By Sunny .

Apr 1, 2021

By 佐藤淳一

Jan 29, 2021

By vijay k A

Jun 23, 2021

By Ankit G

Apr 15, 2022

By Akhas R

Mar 20, 2021

By Ume K

Oct 29, 2022

By Md. A I

Mar 15, 2022


Mar 17, 2022

By Htet A L T

Jul 16, 2021

By Usama G

Jun 13, 2022

By André J A

Jul 22, 2021

By Deleted A

Apr 4, 2022

By George P

Apr 18, 2022

By Klemen V

Apr 23, 2021