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Learner Reviews & Feedback for Data Analysis with Python by IBM Skills Network

4.7
stars
16,195 ratings

About the Course

Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models. Topics covered include: - collecting and importing data - cleaning, preparing & formatting data - data frame manipulation - summarizing data - building machine learning regression models - model refinement - creating data pipelines You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them. In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge....

Top reviews

RP

Apr 19, 2019

perfect for beginner level. all the concepts with code and parameter wise have been explained excellently. overall best course in making anyone eager to learn from basics to handle advances with ease.

SC

May 5, 2020

I started this course without any knowledge on Data Analysis with Python, and by the end of the course I was able to understand the basics of Data Analysis, usage of different libraries and functions.

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1876 - 1900 of 2,447 Reviews for Data Analysis with Python

By Krithin K V

Dec 2, 2019

Some of the topics at the end of the video have been rushed to end. I would rather liked to see an elaborate examples for those topics to atleast have an idea of it.

By LEONARDO R

Apr 8, 2020

185/5000

It requires more technical skills and knowledge in other areas not mentioned like python or IBM platform.

However this has done push me to learn more skills.

By Timothy B

May 8, 2020

I could have used a little more explanation when it came to Pipelines and Polynomials, but I figure there will likely be more of that to come in the later courses.

By Siddharth T

May 1, 2020

The course is a good start for beginners. The course contained everything useful form churning of data to regression. Pretty decent explanation with practice labs.

By Jonathan P

Oct 18, 2022

Great coverage of fundamentals. Seems to be missing some content around diagnostic metrics for classification problems (e.g. binary class) using AUC/ROC curves.

By Isis S C

Nov 21, 2020

The course is well organized and the content is presented is an accessible manner. Exercises could be more challenging and cumulative to help increase retention.

By Eliseo B F

Feb 11, 2020

Most of the course is very easy to understand, although the exercises in the notebook can become complex, the exercises do not always run and must be done again.

By Harshit T

Sep 22, 2018

Fun course! Lots of interesting content. It could've been more interesting and challenging with addition of a couple of marked assignments or a capstone project!

By Rebecca L

Jan 3, 2022

The course material is great enough for a beginner. However, some of the presentation method is confusing. The narrator also seems like a laymen for the course.

By Neil A

Jan 30, 2022

Great content, but awkward, untimely popping of questions during video lectures, very annoying. Labs are very useful and productive, but videos are too short.

By Shivam C

Jun 3, 2020

This Course was very informative and beneficial and conceptual too, being newbie i personally feel that this course has taught me alot. Thanks to team Coursera

By Sebastián M

Apr 22, 2020

Muy buen curso, por mejorar: varios errores en los talleres y también no fue posible ingresar a estos durante varios días lo cual atrasó el proceso de estudio.

By Osagie E A

Dec 22, 2020

I love how engaging the course is with its labs and how it is well-packaged in such a manner that encourages beginners to learn... keep up the good work guys.

By Kedharnath A

Apr 15, 2019

I found this module very difficult to understand as it was loaded with high end concepts and coding. Might have to redo this course to understand even better.

By Manoj S

Mar 9, 2019

Course content is very good but I feel it can be more improved if the training is provided at slower pace. Also the examples should be in detail. Overall good

By Andrés P

Jan 30, 2020

I think it would be good if the units had activities to deliver mandatory since that would allow to strengthen the knowledge acquired. Thanks for the course.

By Ricardo R O

Oct 13, 2021

This course is too complete, but have too many questions between videos, its feels like a brake every time, I think is more easer at the end of the videos.

By Faizan A S

Dec 1, 2019

The course content is really great and method of teaching is very specific .Much details very covered during the course and really i gained a lot from this.

By SOUVIK B

Aug 31, 2018

Good course if you are beginning data science. You don't need much of python experience but will be better to have if you want to quickly finish the course.

By Sreelatha V

Jan 5, 2020

Very detailed and guided course that provides an overview of data analysis in Python with short assignments after each video and interesting lab courses.

By Guilherme V

Jul 3, 2020

insufficient statistic, as the name of the course is Data Analysis, i would expect more classes about the different distributions of data, pdf and pmf..

By Katarina S

Mar 22, 2020

One of the best courses in the IBM Data Science Specialisation.

I would like to have more quiz questions and opportunities to practise what was covered.

By Shayan k

Sep 12, 2021

There must be a slightly high level of Quiz, assignment and Project and must have to add some more advanced concepts about statistics and probability.

By Frank

Aug 30, 2019

I would have given it 5 stars but they barely went over polynomial regressions and pipelines and it was a major portion of the end of class assignment.

By Wenyu X

Apr 2, 2019

pros: well organized, clearly explained each step, useful

cons: frequent errors in both videos and the lab, especially on the questions part in the lab