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

4.6
154 个评分
34 条评论

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....

热门审阅

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 - Statistics for Data Science with Python 的 25 个评论(共 37 个)

创建者 Vũ V H

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.

创建者 cynthia e

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.

创建者 Ofure E

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.

创建者 Zara U

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.

创建者 Nabilla A

Nov 9, 2020

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

创建者 Alfred K S

Dec 29, 2020

Challenging for non statiticians

创建者 Brandon B

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.

创建者 Ebenezer D

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!

创建者 Robert S

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.

创建者 Jason C

Sep 12, 2021

E​njoyed 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

创建者 Joao L

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.

创建者 Hichem D

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.

创建者 Yodefia R

Jul 27, 2021

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

创建者 Muhammad F H

Sep 2, 2021

A worth-to-try course if you are curious about implementing some statistical tests in Python.

创建者 k b

Feb 7, 2021

Excellent course with a step by step explanation and complete final assignment.

创建者 Asif Y

Jan 13, 2021

One of the best course I have taken online. Way of teaching was outstanding.

创建者 Khusan T

Mar 30, 2021

Understandable and easy to grasp the basics of statistical analysis

创建者 Vaseekaran V

May 13, 2021

A good introduction to those who want a brief taste of statistics

创建者 Sunny .

Apr 1, 2021

Excellent Course...Would be great if add few more examples

创建者 佐藤淳一

Jan 29, 2021

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

创建者 vijay k A

Jun 23, 2021

the course is more useful and cover basic concepts

创建者 Akhas R

Mar 20, 2021

Extraordinary. Very interesting.

创建者 Htet A L T

Jul 16, 2021

Thank You IBM

创建者 André J A

Jul 22, 2021

ok

创建者 Heinz D

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.