Chevron Left
Back to Data Analysis with Python

Learner Reviews & Feedback for Data Analysis with Python by IBM

4.7
stars
7,884 ratings
994 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

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.

AB

Feb 13, 2020

Great introduction to data manipulation and analysis for common problems that arise in data science. Also allows you to gain a further understanding of Python syntax, specifically the pandas library.

Filter by:

651 - 675 of 988 Reviews for Data Analysis with Python

By Piyush L

Jul 09, 2019

The course like every other course in the specialization is a little too fast for me. The videos are way too short (averaging around 5 minutes) and there is way too much stuff in the videos for you to be able to absorb properly. But, the good thing is you'll still learn a lot from this course. Things got really fast mid of 4th week onwards for me, like explaining ridge regression and very complex topics without any proper introduction has left me kind of clueless. But a course should be rated according to how much you really learned and despite of all of the things above that and some more, I still learned a lot from this course so 4 stars for that.

By Nguyen D H A

Aug 03, 2019

A good starting point.

Some of the concepts could have been explained more clearly, I have decent mathematics understanding and sometimes still felt like I was hopping from this to that (regarding the codes). I understand that they're trying to teach many things in basic level, but total video time was about only 1 hour for the whole course... I wouldn't mind watching a little more or getting additional reading materials to get the context & familiarize myself with the codes (I do additional practices on my own so that's fine, but directed-study is always nice, and easier)

The labs were really helpful though, so I'd say go for it!

By Everett T

Jun 29, 2019

The course is overall very helpful to learn Data Science with Python while it does require foundations for statistics for this module, so it appears difficult to understand some mathmetical concepts for beginners. Thus I suggest some more detail explainations/practices for core parts like model development.

Moreover, there are some mistakes/typos in labs, e.g. Week5's Model Evaluation and Refinement, though most of them are minor. Also some libaries are outdated (discovered thourgh warning outcomes), which may need updating.

By Juan M L F

Jan 23, 2020

This course is good if you already have some experience in Python and its structures, or if you have some knowledge in programming. You will learn some basic data manipulation and exploration techniques and also start with some of the model evaluation metrics in order to assess the (regression) models created. Overall good experience. If you already have some knowledge of Python SciKit Learn and Pandas, you could easily cram this course in 2 days (all-in) without too much sweat.

By Sk. T R

Apr 03, 2019

It has been a fantastic experience to have gone through this course materials. Although I found the lecture videos quite quick to the extent that we fail to understand the concept well. But while going through the labs carefully, I was able to get the concepts right. So only because the lab part was well organized, the course was helpful to me. But had it been the lectures alone, then it would have been difficult to grasp all the concepts clearly.

By DI C

Jul 07, 2018

Great course! More hands on and practice, a bit lack of theories, compared with Andrew Ng's ML course. And there are a few typos or mismatch in the course materials that need more attention. However, I especially like the fact the example, i.e. predicting car price, has been revisit and further developed through the 5-week course. Just finished round 1, guess I need to go over it again (maybe again) to grasp more details. Recommend the course!

By Jianxu S

Sep 07, 2019

Overall the course is well written. There are a few typos including in the instructions for final assignment. I feel that a summary is missing for the overall data analysis process and methods. This course is the longest in the series so it takes a lot of effort to get through. I did not have much Python background so it was a bit challenging at the beginning but the material was very helpful in bringing me up to speed.

By Matthew S

Jun 20, 2019

This course was challenging. I will probably want to come back to it after learning a bit more statistics. But it was cool stuff, and at the right level of depth. (The only criticism I have is that there are some problems with the final assignment, a small discrepancy between the question in the notebook and the question on the assignment submission, and some other formatting issues on the submission form.)

By Ekaterina K

Aug 20, 2019

Very good lectures, but the final project takes way longer to set up than to complete: finding the link to the final assignment and making it work in Watson took me too much time. There should be an option to do it outside Watson environment without loosing points because Watson is very slow. Moreover, the assignment and the link to the dataset should be posted more clearly.

By Celine

Jan 01, 2020

The material are structured very well. The explanation in the video and lab tutorial really help to understand. The discussion forum is active and the teachers are responsive. You will also get a free certificate and IBM badge. Though there are some typos and errors and some things left unexplained, but overall it's good. Hope you guys can increase the course's performance.

By MOUAFO D

Jan 20, 2020

Well design for beginners with a scientific profile. The course starts moderately and covers a large amount of concepts. I advise to take notes and often to deepen certain concepts in dedicated tutorials on google or YouTube and other appropriate platforms. Cleaning mistakes on the slides and the notebooks will be great and make the learning experience more fluent.

By Jess M

Feb 28, 2019

Covers a lot of content very quickly with not enough opportunities to practice using and applying the code. Having lots of quizzes is good for testing passive knowledge, but more active hands-on application in labs would be most welcome. Useful content, but I am going to go take an intro to Python course so that I can actually follow and use what is presented here.

By Eugene T B

Sep 03, 2019

Some of the course skates over pretty difficult information really quick and then gives you challenges that haven't really been that well explained, so some self-research is required. The assignments are also pretty copy/paste + modify a couple of variable names so you have to put in the effort to really get good value out of the course.

By Anuradha B O

Jul 14, 2018

The course is very interesting and concise, it has a very logical flow. The best parts about the course are quiz embedded in the lectures and detailed lab assignments. However, there are few errors in the lab and assignments, which need to be rectified. Otherwise, it would have been 5star from me. Thank You for desiging this course.

By Ming E L

Dec 18, 2019

Easy to understand and grasp for a beginner. Good refresher for those who have some basics of programming down. Typos in the reference codes here and there but no major problems. Other than that the Watson interface is alright to work with however there will be some lagging some times. I enjoyed the process of learning this course.

By Joseph O

Mar 11, 2019

a few discrepancies here and there, please see the comments in the discussions. Other than that, very good! This course was more difficult than the others, and so i guess this is why employers prefer potential employees hold a PhD, or at least maintain a high algebraic/calculus/statistical aptitude

By Sumit C

Jun 06, 2019

The underlying basics of Data Analysis with Python were deeply conveyed. Simple examples and easy to operate commands were greatly described. I would suggest everyone take this course whether or not they know to code. It is always great fun to learn new concepts and Coursera makes it possible.

By John B

Sep 09, 2019

Contained some simple grammatical errors, as well as some syntax typos in some of the modules. The most relevant thing I would criticize is the lack of depth with describing certain topics ion the modules as they can be very complex. I recommend studying the section notebooks thoroughly.

By Michael K

Apr 28, 2019

There is a lot to unpack in this course. If you have a statistics background, this may seem kind of trivial, but for the rest of us it is loaded with ways to view data. My only criticism would be that it sometimes skims across an advanced topic without really giving a general overview.

By Irving B

Oct 11, 2018

This course gives a very clear view of the tools used to find the best way to analyze data when looking for the best model to predict target values. The use of Jupyter Notebooks to run code for the data analysis is very useful and enables the student to experiment on his own for options.

By yimingguo

Mar 24, 2019

I have start this course without knowing any Python code. I made it through but with a lot of rock with all the code. like a For loop or simple Python code. I suggest to study basic Python code then start this course but this course did push me a lot on Python code learning with Youtube

By Lauren J

May 07, 2019

This was a good course, but didn't have as much labwork as I would have liked. There were a lot of labs, but they were mostly already completed by the instructors - more of a read-along than actually doing work yourself. That said, it was a valuable course and don't regret taking it.

By Benjamin S

Jan 17, 2020

The course teaches an incredible amount of information in a relatively short time. The downside to this is that users don't get enough practice within the course on the data analysis methods and functions taught. Additionally, there are a lot of typos that need to be fixed.

By Mukul B

Nov 09, 2018

This module is loaded with concepts. Even though they are introduced in a logical sequence, it gets a little overbearing and tend to lose the relevance in the context of car price prediction. At least, now I am aware of the techniques, methods and python's capability.

By Luis O L E F

Nov 14, 2019

Good introductory course. Even though it is an introduction, the course would benefit a lot from including a bit more of theory, even as optional material. For example, including theory about ridge regression, instead of just mentioning how to implement it in Python.