Statistics for Data Science with Python(으)로 돌아가기

4.6
별점
218개의 평가

## 강좌 소개

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....

## 최상위 리뷰

JL

2021년 1월 19일

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

2021년 1월 13일

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.

필터링 기준:

## Statistics for Data Science with Python의 55개 리뷰 중 51~55

교육 기관: Brandon H

2021년 9월 20일

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.

교육 기관: Rajeswar S

2022년 6월 23일

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.

교육 기관: Paul H

2021년 11월 10일

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.

교육 기관: Anastasiya K

2021년 2월 12일

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

교육 기관: Jason W

2021년 2월 14일

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