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Avis et commentaires pour d'étudiants pour Statistics for Data Science with Python par Réseau de compétences IBM

4.6
étoiles
218 évaluations

À propos du cours

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....

Meilleurs avis

JL

19 janv. 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

13 janv. 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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26 - 50 sur 55 Avis pour Statistics for Data Science with Python

par Vaseekaran V

13 mai 2021

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

par HAFED-EDDINE B

15 déc. 2021

Very interesting course as it included very powerfull tools.

par Sunny .

1 avr. 2021

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

par 佐藤淳一

29 janv. 2021

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

par vijay k A

23 juin 2021

the course is more useful and cover basic concepts

par Ankit G

15 avr. 2022

Well Explained with guided project.

par Akhas R

20 mars 2021

Extraordinary. Very interesting.

par Ashraful I S

15 mars 2022

A​MAZING EXPERIENCES WITH IBM

par ALEXANDRE R P

17 mars 2022

Outstanding course!

par Htet A L T

16 juil. 2021

Thank You IBM

par Usama G

13 juin 2022

Good

par André J A

22 juil. 2021

ok

par Virginia B

4 avr. 2022

Overall this course provided content to familiarize oneself with statistical analysis in python. I'm particuliarly thankful for the step by step labs and excercises available on IBM. In some cases, the course materials don't seem to cover content that is included in the evaluations. In those cases, I suggest to reference outside sources. Also the experiences with IBM Cloud have been frustrating. Partially becuase the environment is at times unavailable when needed. In addtion the environment has been undergoing upgrades and changes, and the course materials are not up to date with the changes in the cloud environment. Ultimately though, dealing with unstable computing environments and reasearching outside sources to successfully complete projects are skills possibly more valuable than knowing how to compute statistics with Python.

par Heinz D

7 févr. 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.

par Andreas F

21 févr. 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.

par George P

18 avr. 2022

This was an absolutely useful course to introduce the student in the topics of normal distribution, calculation of probabilities and hypothesis testing applying Python.

Visualization and statistic charts are covered as well.

Examples were given in a meaningful way, nevertheless I would give 5 stars if teachers could focus more on the theory of inferential statistics.

par Klemen V

23 avr. 2021

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.

par Michel M

28 avr. 2022

It was a decent course.

It could be more "learning by doing" oriented, there are some concepts like hypothesis testing that could be presented in other way, It'd be helpful if it had some real world examples of that.

par Akshay K

18 nov. 2021

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

par Omar A

5 avr. 2021

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

par Thomas S

2 mars 2021

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

par STEPHEN E

31 août 2021

Good introductory course

par Elizabeth T

15 juin 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.

par Lucian P

18 janv. 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.

par Xiangyue W

28 avr. 2021

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.