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Learner Reviews & Feedback for Data Analysis with Python by IBM

4.7
stars
17,703 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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2026 - 2050 of 2,715 Reviews for Data Analysis with Python

By Bernardo N B C F

Jul 2, 2019

Really enjoyed the Labs, specially the last ones that were long and covered a lot of material in depth. I think the course would have a better user experience if it wasn't for the many spelling mistakes and small bugs, specially in the Jupyter notebook Labs.

By Zayani M

Dec 11, 2018

Toughest course so far. I liked being able to visually see the statistics behind data analysis, which was much more helpful than the textbooks I had to use to earn my math degree! However, the final week was still a challenge to get through and understand.

By Tracey C

Feb 11, 2021

I liked the structure and pace of this course. The videos and exercises were helpful and the final project was a very good measure of what we had done in the course. I took off a star because there were more typos in this course than some of the others.

By Lakshmi h

Jul 9, 2020

There should be an Handicap assistance in the course as some of the visually impaired people are finding it difficult to read the assignment codes with their screen reader nvda.

The assignment notebooks code settings need to be modified to support this.

By Dean E B

May 28, 2021

Covers lots of materials. Lab is at end of each week, but I did better following along with coding during each lesson, A good framework, but with a lot of jumps and not much depth. With additional studying from other sources, I got a lot of knowledge.

By Crystal Y

Sep 2, 2020

A lot of concepts are packed into this little course. The course materials are a bit too concise for the concepts to be elaborated properly, so I need to search a lot extra online for concepts behind. But in general, they can be used a starting point.

By Antas J

Jan 8, 2020

the course was great and informative, however the pace and information in this course is not sufficient for a person who is new to the python libraries and analytical features, if i may add MSE and R^2 and plots are still not so much understood by me.

By Aylin G

Jan 2, 2020

Some questions in the peer-graded assignment are not clear and answer box of some questions are not visible so I could not get any point from them. You should better check the contents of the tutorial and make sure that there is no technical issues.

By giuseppe t

Mar 31, 2022

It is a well structured and quite valuable course; it could have been a masterpiece, if it had provided more connections, explanations, insights, in other words programming background related to all those different topics touched over the weeks.

By Monalisa p

Nov 4, 2019

This Course is very helpful for the beginners. This course is very detailed, and well explained. You will go through all the important things required for data analysis. This course's Lab is very strong, I must recommend you to do this course.

By ERNESTO C O C

Feb 10, 2022

Bom conteúdo na abordagem das principais funções para análise de dados, porém, carente de fundamentação teórica em relação às análises. Por não haver pre requisito, os fundamento poderiam ter sido abordados, ainda que de forma superficial.

By SACHIN G

May 4, 2020

Very informative course... very well designed... a bit fast-paced but concise and clear

it's just that if the final project could have been little more challenging so that there are more opportunities to apply what we learnt in the course.

By Terry G

May 1, 2020

Great course. I felt like I can run my own models and test them now. There were some strange errors throughout the notebook that were raised in the forums but not addressed in the documents. Aside from that, it was pretty good for a MOOC.

By Adarsh K P

Sep 26, 2019

ton of new stuff to learn from.... super informative course...this course will introduce you to a lot of useful and important stuff and the best part is that each topic is explained first then comes the coding part which is just awesome.

By Chris A B

Sep 15, 2019

This was a challenging course that covered a lot of items. I believe I need more practice in these items (Linear Regression, Polynomials, Ridge, Fit, Predict, etc.) in order to have a much better understanding of the course materials.

By Guilherme P d C

Apr 7, 2019

Model Development and Model Evaluation content requires more intuitive examples, maybe adding some flowchart to explain the reasons of every step in Modeling and Evaluation. I am making this suggestion to make the course even better.

By Joe M

Mar 27, 2020

Interesting class. Clearly designed to cover a lot of ground but not always in the detail some may like. Emphasizes showing some basic analytic work flows, but does not always explain how or why of a particular step in the workflow.

By Logan W

Nov 7, 2019

This was a very comprehensive course, but it could definitely use some revising on the labs that caused output issues. Additionally, some of the peer-graded material couldn't be uploaded due to syntax. Other than that, very helpful!

By João L F C

Apr 16, 2020

It was a good introductory and pocket course for Data Analysis with Python to me. The concepts were given pretty much straight forward, and the assignement didn't diverged much from what had been already seen throughout the course.

By Jhon P

Jun 4, 2021

At the end, the final project link was wrong and nobody from coursera or ibm give me an answer. Fortunately, course partners share the right notebook and thus, I could finish my course. The topics are very well for this course.

By Muhammad F

Apr 19, 2023

this was a good course but for beginner like they show explain little bit more regarding python in the video with coding, they are just showing the slides of the codes which make this very difficult to pick the idea clearly.

By Michael g

Mar 4, 2022

this was one of the better courses in the 10 course package. a bit more focused and less slapped together than the previous courses. also the lab load and have no glittches, aleast for me, unlike other courses. overall good

By Olatoye D S

May 27, 2022

This is course is a great way of understanding Data Analysis, model training and evaluation, as well as a further indepth understanding of Exploratory Data Analysis, using Python. It was a great time learning through it.

By Enrico G

Apr 9, 2022

Very nice and practical course. It gives you the tools to perform a regression analysis on a dataset. Perhaps, I might have focus a little more on the mathematical theory behind some method, like correlation, p-value...

By Rajan G

Jun 16, 2020

This course helped me a lot in solving my basics about data cleaning, Visualization, Techniques for getting better result and most important how we can judge whether a model is good or not. Thanks for this great course.