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学生对 IBM 提供的 Statistics for Data Science with Python 的评价和反馈

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460 个评分

课程概述

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....

101 - Statistics for Data Science with Python 的 110 个评论（共 110 个）

创建者 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.

创建者 Subham A

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Oct 12, 2022

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

创建者 Mohamed Y

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Apr 20, 2023

Need to explain in details the statistical elements and uses

创建者 Alexander H C

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Aug 9, 2024

Probably the most convoluted, out of touch, amazingly confusing ways of describing ideas and methodologies used in stats I've ever witnessed. It seriously seemed like they had some of their engineers come without being told what for, and asked questions on the spot to answer. Some of it was to the point I have my friend watch parts of videos because I was laughing so hard.

创建者 Yaseen A

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Dec 4, 2024

the videos were not enough to clarify the points of Statistical tests and also the contents of videos for modules 4 and above also were not enough for such subject

创建者 Lanchec E

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Oct 30, 2025

Basic statistics and not very well explain. a lot of question for the quiz are NOT in the course

创建者 Pamela G

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Feb 16, 2026

The course material tries to teach the foundation knowledge in a nutshell, while education on the libraries, hands on training and exercises are missing. It would be better to refer to a basic statistic knowledge training and focus on the python libraries and coding part.

创建者 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.