Back to Statistics for Data Science with Python

4.5
stars
293 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 â€“ 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: âœ”Calculate and apply measures of central tendency and measures of dispersion to grouped and ungrouped data. âœ”Summarize, present, and visualize data in a way that is clear, concise, and provides a practical insight for non-statisticians needing the results. âœ”Identify appropriate hypothesis tests to use for common data sets. âœ”Conduct hypothesis tests, correlation tests, and regression analysis. âœ”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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## 1 - 25 of 68 Reviews for Statistics for Data Science with Python

By Brandon B

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Jan 17, 2021

The videos, readings, and labs were not sufficient for me to feel prepared for the assessments. I ended up using outside resources just to understand what was being presented here. There was really no explanation of why you would use certain tools or the underlying statistics principles; the course assumes a lot of the learner (both in statistics and Python) considering it's aimed at beginners. I believe this is a newer course, so hopefully it will continue to be revised, but I was disappointed in the content compared to other IBM courses I've taken through Coursera.

By HÆ°ng V

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May 28, 2021

At first, I find this course to be somewhat challenging at first since I don't have any prior knowledge in statistic, but after a few lecture and some self study later, I have gain a pretty good understanding of statistics and its application in Data Science.

I especially like the final assignment as it give me a feel for what being a Data Scientist is like. It also make me go through all of the previous lab for reference. By doing so, I have a chance to review the things I have learnt and get a deeper understanding of the material. I can't speak for everyone but if you are completely new to statistic like me and planning to break into Data Science field, I think this course might be a good starting point for you.

By cynthia e

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Nov 16, 2020

I enjoyed taking this course and found it was well explained. Having been out of school for a long time and not using stats in my daily job, I found that I had to listen to the videos over and over again to fully understand the concepts introduced. I also struggled initially with python as it was a new concept for me. I recommend it for others, take it slowly and try to revisit the videos and readings and ensure you follow and thoroughly complete the lab exercises as this will help with the project.

By Ofure E

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Nov 3, 2020

This course was seamlessly easy to understand and follow. During my undergraduate studies, I struggled with statistics which made me a bit worried taking the course.

I am glad I pushed passed my fear and took the course , as it has sparked my interest to learn statistics, how it applies to data and making business decisions.

Thanks Aije and Murtaza - I look forward to taking more courses from you both on here.

By Zara U

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Nov 9, 2020

I really enjoyed taking this course. It was really easy to follow and I absolutely loved how the course was put together. I will recommend anyone looking to use Python for Data Science to take this course.

By Nabilla A

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Nov 9, 2020

Amazing course . Very easy to follow . Definitely improved on my python skills . Would 100% recommend .

By Alfred K S

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Dec 29, 2020

Challenging for non statiticians

By Anastasiya K

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Feb 12, 2021

There are mistakes in examples, in assignments, and final project! Creators never respond in Help section.

By Domenic P

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May 18, 2022

I really enjoyed the course. However, one has to keep in mind that the concepts presented are very basic.

for example, regression is a highly complicated topic and interpretation of the results needs more in depth analysis, however, for an introductory look into the topic the course is very well structured

By Ebenezer D

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Nov 20, 2020

Excellent course to help clear doubts for the level of statistics needed for data science. It a great experience. well done IBM!

By Pritesh V

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Aug 25, 2022

Being a Mechanical Engineer, I already knew Normal Distribution but don't know the T-test, ANOVA, etc. This course covers pretty much about doing statistics using Python but you should know statistics before doing this course. The course is great it also help you learn a bit of Seaborn visualization library.

I would recommend to do some course on Statistics first then do this course for implementing statistics in Python. The Quiz, Assignment and Lectures were good. Only thing which was bad was it didn't explain the value we got in the summary of results of statistical tests we performed. Also the explaination was very brief.

By Heinz D

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Feb 7, 2021

Good course, many subjects are covered. But be careful if you're totally new to statistics and hypothesis testing, this course is rather fit as a refresher.

Unfortunately the lecture slides are not available for download and some of the transcripts need serious amendments. In all Jupyter labs the kernel did not connect for a long time and attempts to export notebooks as pdf threw internal server errors. Such things are disturbing and could be prevented with proper monitoring and proper technical setup. The peer review in week 6 must be performed without having the approved solutions; this is not very professional.

By Andreas F

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Feb 21, 2021

Overall, the course gave me a brief but informative look at the basics of statistics with Python. Once again, the many practical exercises were very nice. However, the speed of the p-value and regression was a bit too ambitious for me. Would have appreciated some more details there or a good link to somewhat short and informative. But as said, overall, another very informative course.

By Rob S

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Apr 6, 2021

The videos, readings, and labs were not sufficient for me to feel prepared for the assessments. I ended up using outside resources just to understand what was being presented here.

By Elizabeth T

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Jun 15, 2021

The course felt disjointed at times and there was a lack of clear explanations. The expectations for the final project (formatting, etc.) could have been stated more clearly to reflect the marking rubric. The final project was otherwise nice and quite summative.

By Lucian P

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Jan 18, 2022

Not the greatest course on this platform. The structure of the course is somehow confusing and it's got a bit old, should be updated and offer better knowledge.

By Jaelin L

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Mar 22, 2022

It was an excellent course with fundamentals of statistics. It taught me why of the python library models. The quizes and final assignment is very helpful for learning. And, the final assignment really helped me to turn information into my own skill. Thank you very much for designing this amazing course!

By Jason C

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Sep 12, 2021

Enjoyed course the most from teh IBM Data Science Modules! Being less technical, it was easier to understand with minimal knowledge on the subject and the excersixes and final project were very practical and helpful in understanding

By Himanshu D

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Apr 2, 2022

Its a good cource. I learned about the basics of satistics and how to apply it in python and data that we have. I walked through many cources but in Courcera, this is the best basic cource for the DataScience enthusiast.

By Marcelo d C

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Dec 1, 2021

Excellent Course! Clear and didactical explanations, objectives exercices and very oriented subjects! For those who are interested in data analytics, this the trainning you should take!

By Joao L

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Jan 20, 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.

By Hichem D

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Jan 14, 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.

By Yodefia R

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Jul 27, 2021

Great introduction to basic statistics for data science. Python specialization suits those with no experience in the language.

By Ajay K S

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May 3, 2022

It is few of the Data Science courses in my learning series. This is one of the Best in Series. Thanks to the team.

By Piotr K

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May 30, 2022

Good albeit very general presentation of useful libraries and Python programming language for Data Science.