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Learner Reviews & Feedback for Data Analysis with Python by IBM

4.7
stars
18,618 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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76 - 100 of 2,912 Reviews for Data Analysis with Python

By Opetunde A

Jul 12, 2018

I have been looking for a very non-complicated course on data analysis and I hit the Jackport with this course! Very simplified and explanatory. You should definitely take the course

By Faisal A S

Nov 25, 2023

This course is amazing. This course will give you the direct information you will need. It is based on a hands-on project you will be positive when you watch this course

By Prakash C

Jun 6, 2019

Great course. I had fun having a kick start in the field of data in machine learning. I understood the concepts related to how to improve the model.

Thank you

By Siddhartha S

Jun 4, 2019

Great Course. Amazing Work By the team. Concepts explained clearly, followed by the week end quiz to revise. The Labs Do a great work in helping out

By Aldy P

May 7, 2020

helps me a lot! FYI I was new to data science and programming language but this course helps me to understand business analysis with Python!

By Francisco “ S

Oct 10, 2023

The course is very straightforward, everything to the point, just takes a little bit to get familiar with new concepts.

By Sameen

Jul 19, 2024

This course has been very helpful for me and i have learned a lot in this course...many skills

By Ted H

Jun 6, 2019

Covers a lot of ground but the Python Labs are great at bringing everything together.

By Lily N

Jul 17, 2024

It's a bit hard to understand this content. I would come back to review :)

By Aksana

Nov 27, 2023

One of the most interesting and useful courses for data analysts!

By YASSINE S

Nov 4, 2023

the best course to learn data analysis with python

By Paulo B S

Jun 4, 2019

A very complete course of Data Analysis.

By Mahmood H

Mar 16, 2019

Tough but useful.

By Vincent Z

Mar 10, 2019

The course content is definitely interesting, but the approach is superficial. You will get a broad overview of the keyword to search for, and what is available in popular Python packages. However, the quizzes are way, way too easy. The course needs a final "open" assignment, where you have to use the tools without being guided along the way. This is the only way to truly learn.

By Mahvash N

Mar 4, 2019

Course was great but it had number of errors and typos, that per my experience and other attendees caused some confusion.

I am sharing so it could be improved as it is a dream come true for myself to gain this valuable knowledge as conveniently as possible.

Thank you.

Mahvash Nejad

By Nicola Z

Sep 23, 2024

Great segway to Data Analysis with Python, but some details lack in polishing, some minor errors/typos here and there. Would be nice to have a full tutorial for setting up a nice environment on your computer. Overall a very good experience, I recommend it 100%.

By Abira M

Sep 28, 2024

It was a great course. Some of the statistical terms were new to me and it wasn't easy to understand them even though I had a mathematics background. I will recommend explaining these terms simply so anyone can understand them.

By Nina D

Oct 6, 2022

Well structured lectures and notebooks worked without issues. I just wished a bit more explanations would have been given in how to interprete the output of some of the results, especially when it came to data predicitions.

By Juris U

Jun 24, 2024

The strong side - the emphasis on practice, the weak side - insufficient theory and insufficient didactics. The course can be recommended if you are ready and have the opportunity to learn on your own.

By Muhammad T A

Oct 1, 2024

Beginners must start from this course as it will provide the basic insights regarding Data Analysis and will prepare the learners towards advancement in a well-mannered comprehensive way.

By Den M

Sep 21, 2020

A very comfortably created course - no stress at all. However all that you can get is become familiar with the data analysis tools. May be that's the point.

By Ruchir

Dec 19, 2018

I think few more practical exercises or at least references of the same would help better understand the overall fundamentals.

By Rebecca V

Mar 5, 2019

Material covered is useful, but there are a lot of typos and mistakes in the lecture slides and labs.

By Rene P

Mar 24, 2019

There could be links to functiones libraries in the lab for a fast check of a function if needed.

By Ugur S O

Dec 21, 2020

I think the quizzes can be in the format of programming required questions.