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Learner Reviews & Feedback for Data Analysis with Python by IBM

4.7
stars
11,220 ratings
1,609 reviews

About the Course

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....

Top reviews

SC

May 06, 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 20, 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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1426 - 1450 of 1,597 Reviews for Data Analysis with Python

By Hemanth S

May 04, 2020

Course is a bit too short and way too fast paced for what it is trying to convey! Of course people will be able to complete the course without problems but, have to re-visit and brush knowledge on these a lot more. Anyways, it is a bit of confidence booster. You feel like you learnt a new course.

By Sisir K

Feb 15, 2019

Highly technical and complex in nature. Difficult for people just starting out with data science. The hands-on labs are more useful than the videos themselves. The quizzes in between videos felt a bit too easy and mostly comprised of examples (as questions) in the videos themselves.

By Raghav N

Sep 14, 2018

This course is definitely very helpful to people who are passionate about Data science and have basic to intermediate understanding of Python but this course can be much better if it includes coding assignments rather than quiz submission. It was a great experience.

By Roberto B

Jul 10, 2019

I'm not convinced that this is a great way to learn, I just feel there needs to be a better way of learning this than the approach this course takes, I kind of learned the python commands but I'm not sure I understand how to apply them in the real world. We'll see

By Toan T L

Oct 23, 2018

Decent videos on Data Analysis techniques.

But the labs are poorly constructed: typos, inconstant question and solution, un-commented code and under-explained lab result.

It's a shame since the labs in other courses in this series are very high-quality.

By Raj K

Jul 06, 2018

It would be great course for beginner to have idea about different steps involve in data science job. I would recommend to go with this course. I just took 3 days to complete this course and you can do in 2 days also. Depending on your speed.

By Damian D

Feb 13, 2019

There are some mistakes in the course (wrong transcryptions, missing cells in LAB).

The material is quite difficult and more explanation / exercises would be needed.

There is no assignment at the end of the course which I consider as minus.

By Filipe S M G

Aug 24, 2019

Good introductory course on Data Analysus with Python. Since the course is short, the functions and concepts are explained very quickly. There are also many mistakes in the slides, notebooks and even in the final assignment.

By Benoit P D

May 04, 2019

The content of the course is very interesting. There are lots of typos in the lab workbooks though. Additionally, i found having to use Watson Studio for the assignment / labs as opposed to plain Jupyter a little annoying.

By Sadanand U

Apr 09, 2019

It would be great if we go in a little more details of when to use which metrics for evaluation. Instead of running through a bunch of concepts you could have spent a little more time in each of them.

By Joseph M

Feb 21, 2019

There were serious problems with this course, not in the instructional material but in the execution. There were multiple typos in the code. The especially grievous ones being in the dictionary names.

By Deren T

Jan 07, 2019

This is the 6th course of the specialization and I gave 5 stars to the previous courses. But this course have many typos in videos and codes. It makes harder to understand some points.

By Kristen P

Aug 19, 2019

The work in this course was incredibly interesting. However, there are many errors and the forums went for over a week without response to questions...It seems hastily put together.

By L V P K M

May 14, 2020

Videos are very fast and dont go into details. Assignment is very easy, it could have been more challenging which can test and make learner to think using several concepts learned.

By Ivan L

Apr 29, 2019

Typos are very unprofessional and spoil impressions of the course. Tests and labs are super-easy and do not make you think, and you only need to repeat commands from the lectures.

By Vladimir K

Feb 24, 2020

So many errors in materials. It's unacceptable for course of such level. Even though people mentioned these errors in discussion forumns noone seems to bother about correct em.

By Naveen B

Jul 13, 2019

Some of the codes shown in the videos had minor errors. Also, a bit more explanation for function (in statistics terms) would have helped in having a better understanding.

By Ying W O

Sep 27, 2019

There are lots of typos in the labs and assignments, which can be frustrating. I expect better quality from IBM. Content is great and easy to understand nevertheless.

By Matteo T

Jan 01, 2020

This course is quite good. The bad thing is that the arguments of the last "lesson week" are treated very superficially, taking for granted some advanced knowledge.

By Marcel V

Jun 28, 2019

A lot (too much maybe) is covered in this coarse

It really helps a lot when you know some statistics. Like linear regression,

Why gridsearch was covered I wonder.

By Dylan H

Apr 03, 2019

While a bit fast and loose with the concepts, does contain a lot of practical code as to how exactly to bring things discussed about, which is appreciated.

By Xuecong L

Feb 16, 2019

Thanks for teaching me the systematic way to do data analysis! However, I found quite a few mistakes in the lectures in this course, hope it will improve!

By Hao Z

Aug 12, 2019

IBM Cloud is difficult to use.

The generated link of notebook will not share the latest version, if you click the share icon before editing the notebook.

By Neo B

Feb 11, 2019

Data visualization was taught in details in course 7 and regression was taught in course 8. With no backgrounds, the codes in this course are scaring pe

By Goh S T

Apr 08, 2020

The section on model development and evaluation is not so clear. It is difficult to understand if you have no prior knowledge of machine learning.