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Learner Reviews & Feedback for Data Analysis with Python by IBM

4.7
stars
17,670 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

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.

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.

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101 - 125 of 2,711 Reviews for Data Analysis with Python

By Jaime V C S

Feb 22, 2019

Hello,

in this course there were some errors on the slides, and some quite complicated topics (almost every time related to statistics) was given in a very over-viewed way. Also, some of the python codes were not explained very well, with some terms of them seem to be kind of arbitrary for those who are beginners in the language. My impression is that this course should be longer and more detailed.

By arda k

Nov 20, 2018

Overall I benefitted the course material as a beginner in python and data analysis. The questions were too trivial but maybe that helped me remain engaged with the course and complete it in a short time frame. There were some bugs, typos and minor quality issues that did not really effect my overall experience.

By Katarina P

Jun 27, 2019

Many typos in videos, stats explained on a very rudimentary way (and often inaccurate), Watson environment is awful as it takes ages for some simple regression plots to be made, it freezes and the interface is not user-friendly, yet we have to use it.

By Sadanand B

Feb 7, 2019

Seems like there are quite a few errors in the labs that confuse the heck out of a student. The labs need to be fixed else the material becomes useless.

By Ravindra D

May 11, 2020

Course content does not give proper understanding of the different approaches. For the person who is not from mathematics background it is confusing.

By Fiona T

Nov 25, 2022

Definitely not a beginner class. I understood all concepts, but it went way too fast, with not enough practice to really embed them and practice.

By Bhuvaneswari V

Mar 9, 2019

The statistics background needed for the course need to be better explained. or at least reference to related learning materials to be given

By Russell K

Apr 26, 2020

Too many errors in the lab examples can be rather confusing.

Also, the Seaborn code was not working in IBM Watson Studio

By Mariam H

May 2, 2020

Great course but some of the concepts are not explained very well. I got lost towards the end but overall i like it.

By Andre L

Mar 10, 2019

Lot of information, but offered in a very choppy manner. Was hard to follow, will need to review many many times

By Abdulaziz A

Apr 11, 2020

the course content is excellent but some Technical issues occurred in doing the lab exercises

By Chau N N H

Jan 29, 2020

The lesson need more explanations on Polynomial Regression, Pipeline, Ridge Regression.

By Małgorzata D

Aug 29, 2022

Good content, but I wish the assignments and exams were more challenging.

By Bruno C S d A

Sep 10, 2022

The evaluation formats of these courses are very weak.

By Maarten E

Dec 28, 2022

explanations are often very short or missing.

By Fayja H

Jan 19, 2021

too much content all at once

By Alex H

Oct 4, 2019

Begins relatively clear. The practice labs were coherent and straightforward.

Around Week 4, things started to get convoluted. Small things, things that you don't notice at first.

Week 5 was where it really started to fall apart. You could tell whoever made this course lost interest or just did not have the capacity to teach the information effectively.

A great example of the lack of understanding or knowledge of how Coursera works is something you can view yourself.

Week 6 is the Final Project

Week 7 is one statement about your certificate.

Usually in most courses, the final project will be in end of the final week. That week having multiple modules that you have to complete leading up to the final. It was worrying for me as I thought the approach to this was on accident, but it seems likely that it was just due to ignorance.

Just as well, the Final Project was botched, the software and questions were depreciated and even written wrong by the creator. And when you would upload your pictures in the end to show you had worked out the problem, one of the upload buttons was missing in lieu of the letter "Y"....

Y indeed. Y was the ending of this course so terrible? A little more investment in the people you are teaching would go a long way. Very disappointed.

By Aleksandr D

Jul 11, 2023

The quality of the course is quite low - there are mistakes in the code shown in the videos, and some libraries are outdated, causing issues with the code in labs. The way they explain things... I don't like it. I completed this course with a score of 100%, but I still don't have a clear understanding of Data Analysis. I feel like I only gained bits and pieces of knowledge - some from here, another from there, but they didn't provide a solid foundation. Unfortunately, I will have to find other courses... 

So, if you're willing to spend a significant amount of time Googling and chatting with ChatGPT to make the example code work, then you can choose this course. It's not that bad of an option either. 😅

By Sashwat A

Apr 15, 2023

I got lost in the first few steps of how to read a dataset. Instructions need to be streamlined, and why certain steps are being taken need to be explained better. For example, I didn't understand why in Cells 1 and 2 under the Read Data heading, we were working with the autos.csv, when just before it was the imports-85.data file.

By Philip P

Jan 9, 2021

Course lacks thorough rigor or genuine assessment.

Labs are training on copy/paste and using the Shift+Enter command in the Jupyter notebook.

Assessments are multiple choice. No assessments on ability to write scripts to undertake data analysis to seek solutions.

By Brandon S

Jan 7, 2021

Again, the use of the IBM cloud is a useless buffering of site traffic for your own products and does not provide anything for the course. Little to no 'challenge' questions that push the student to go beyond the hand held procedure of the labs.

By Elvijs M

Apr 18, 2020

The course makes you aware of some Data Analysis techniques, but you learn very little. The explanations are very superficial. And since nearly all the code is are already there, you are not forced to think about the concepts and methods.

By Garett M

Mar 24, 2023

Big difference in difficulty between first 3 modules and last 3. Last 3 modules present advance use-cases without providing nearly enough context as when they can/should be used, nor any explanation on what the methods were doing

By Utkarsh S

Jun 25, 2020

The course was quite good until Week 3 but after that it was poorly structured. A lot of concepts were randomly introduced without proper explanation in Week 4 and Week 5, thereby killing the fun of learning.

By Ibrahim A

Apr 27, 2020

This course ranks the least of the wonderful courses I have taken with coursera. There is definitely room for improvement in the delivery of materials.