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Statistics for Data Science with Python に戻る

IBM による Statistics for Data Science with Python の受講者のレビューおよびフィードバック



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....



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.


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.


Statistics for Data Science with Python: 26 - 40 / 40 レビュー

by André J A



by Heinz D


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


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 Klemen V


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 Akshay K


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

by Omar A


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

by Thomas S


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

by Brady E


Good introductory course

by Elizabeth T


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 V P


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 Xiangyue W


Many of the concepts mentioned in the lectures or the quizzes are never clearly defined. Quizzes test concepts never mentioned in class, and one question contradicts what was taught in class.

by Brandon H


A​ll IBM courses need to be removed from Coursera until they can fix them, and Coursera gets a promise that the INSTRUCTORS actually involve themselves in the forums. Anybody who paid for these courses should be refunded their money due to the extreme poor quality. I thought this IBM course would be different than the others, but they went right back into the speed through and not explaining the more complex topics again. The final project asks us to add titles to our statistical graphs, but this was never taught in either the videos or labs. The evaulation metrics are also mismatched with what the actual assignment states. This is 100% unacceptable.

by Paul H


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 Anastasiya K


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

by Jason W


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