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

4.5
stars
364 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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51 - 75 of 91 Reviews for Statistics for Data Science with Python

By 佐藤淳一

Jan 29, 2021

It easy to understand. Not too difficult. Not too easy.

By Kajal L

May 18, 2023

It was very informational and interesting, Thank you!!

By vijay k A

Jun 23, 2021

the course is more useful and cover basic concepts

By JUAN R P G

Jun 19, 2023

Just Great!

Excellent course. I learned a lot.

By Mohammad_Anas

Apr 18, 2023

Amazing course in the journey of data science

By Елена Щ

Apr 20, 2023

Interesting course with real project

By Ankit G

Apr 15, 2022

Well Explained with guided project.

By Akhas R

Mar 20, 2021

Extraordinary. Very interesting.

By Ume K

Oct 29, 2022

very informative and helpful

By Md. A I

Mar 15, 2022

AMAZING EXPERIENCES WITH IBM

By JiSeub L

Feb 28, 2024

easy to understand.

By ALEXANDRE R P

Mar 17, 2022

Outstanding course!

By Irshad A k

Sep 28, 2023

Very useful course

By Htet A L T

Jul 16, 2021

Thank You IBM

By Ekofiongo E

Aug 25, 2023

Super cours

By SAMUEL I R J

Mar 18, 2024

TODO BIEN

By Raúl O M

Mar 12, 2024

excelente

By Александр Ф

Nov 1, 2023

all good

By Aloisius G N

Jan 4, 2023

good

By Usama G

Jun 13, 2022

Good

By André J A

Jul 22, 2021

ok

By Deleted A

Apr 4, 2022

Overall this course provided content to familiarize oneself with statistical analysis in python. I'm particuliarly thankful for the step by step labs and excercises available on IBM. In some cases, the course materials don't seem to cover content that is included in the evaluations. In those cases, I suggest to reference outside sources. Also the experiences with IBM Cloud have been frustrating. Partially becuase the environment is at times unavailable when needed. In addtion the environment has been undergoing upgrades and changes, and the course materials are not up to date with the changes in the cloud environment. Ultimately though, dealing with unstable computing environments and reasearching outside sources to successfully complete projects are skills possibly more valuable than knowing how to compute statistics with Python.

By George P

Apr 18, 2022

This was an absolutely useful course to introduce the student in the topics of normal distribution, calculation of probabilities and hypothesis testing applying Python.

Visualization and statistic charts are covered as well.

Examples were given in a meaningful way, nevertheless I would give 5 stars if teachers could focus more on the theory of inferential statistics.

By Klemen V

Apr 23, 2021

Quick basic statistics with python. Some topics were explained better then others. For example t-test was explained well from statistics point and how to do it in python, meanwhile linear regression was just shown how to do it in python and very quick overview of output data. No background explanation or how to do it by hand.

By Jevgēnijs I

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.