Back to Data Analysis with Python

4.7

stars

10,296 ratings

•

1,454 reviews

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....

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.

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.

Filter by:

By Jaime V

•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

•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 07, 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 Bhuvaneswari V

•Mar 09, 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 02, 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 12, 2020

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

By stijn d b

•Dec 29, 2018

i was following nicely until week 4 but halfway there it got really difficult. To a point in week 5 when all i could do was copying code and adjusting it. I have no idea what i was doing, i totally lost the bigger picture. I'm sure i could never replicate any of it outside the course or explain what i learned.

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 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.

By Mohammad M A

•Apr 22, 2020

I'll be honest this course for a beginner is difficult and incomprehensible as thereare many new things introduced which are not explained properly

By Benjamin J

•Dec 01, 2018

many mistakes throughout

By Laura M

•Jun 25, 2019

Honestly, I'm not sure why this course has such a high rating. I feel like it can't possibly be a reflection of what actual students felt about the course. Reading the other reviews, it's clear some of the issues people had with the course were not the course-designers fault. But, there were some tissues that are simply inexcusable. For example, typos in the lectures (especially towards the final week) show little to no proofreading was done. A lot of the labs involved "Warnings" that the instructors didn't explain to students (and so obviously some students got confused by them, even though they were inconsequential). And the final peer-graded assignment was a complete mess. The first few questions are numbered Question 1, Question 2, Question 3, etc. But the last 4-5 questions are not numbered making it very annoying to upload screenshots for each question. The directions in the assignment were simply wrong. For example, one of the questions didn't even have a prompt, just an empty text box. Someone asked about it in the "Discussions" and a staff member replied but it was never fixed in the assignment.

Trying to put the typos and logistical confusion aside, the course material was oddly organized and students were never really given an explanation as to why the concepts taught were being taught in this way. My least enjoyed course of the whole specialization.

By Hesam R

•Dec 01, 2019

I'm sure a number of people put loads of time into this, so, thank you!

This must be the absolute worst online course I've ever come across ever! First of all, there are so many typos and mistakes in the course material, which is totally unacceptable! Then, there is no logical continuity in the subjects presented. The course is supposed to be intended for beginners, however, there is no background information or reasoning behind what is presented. Many times, one may only copy and paste python commands without knowing why or what it means at all! Unfortunately, This course is a disaster, and hence, NOT RECOMMENDED AT ALL!

By Trina B

•Apr 03, 2020

Avoid this class. Unfortunately, the labs have been broken for several weeks and there has been no helpful response from anyone other than stating that the labs are being migrated to a new platform. The migration has been woefully unsuccessful and the labs continue to be unavailable or non-responsive. I am very disappointed in this class with several weeks wasted trying get through the course.

By Vincent L

•Sep 17, 2018

Ton of errors, both minor and major, in the videos and the quizzes. For example, saying the a difference between two variables is significant because p > 0.05. I report them all and I've stopped counting.

Not professional at all.

By Mykyta S

•Apr 28, 2020

There was 0 statistics, 0 intuition. This course looks good on paper but teaches very little of substance.

By Sevak G

•Jul 02, 2019

HOW IS THIS COURSE BEFORE DATA VISUALIZATION??????????!!!!!!!!!!!!!!!!!!!!!!!???????????

By Kishore B

•May 18, 2020

I read the book 'An Introduction to statistical analysis using R'. To reach to the concept of ridge regression it took about 3 months (as i can only spend an hours a day study hour) and page number > 200 for me to understand the statistical concepts of ridge regression, cross validation etc. And still I was tentative in R. Now, based on this video course and labs, the learning concepts and python implementation could just be done in 2 weeks time (spending 4 hrs on weekends). A lot of effort has been put in this course to make it sound simple. Thank you authors. Wish you continued motivation to design such courses.

By Mengting Z

•Jun 05, 2019

This course gives me a brief understanding of data analysis based in the use of Python. Since I have already had a foundation of the basic knowledge of coding with other programming language, this course started with introducing several basic packages for data science followed with the use of each package. Also, in week 4 and week 5, the course provided me with the idea of generating statistical models to train our data sets. The thinking method of evaluating a model will help me a lot in my future studies in the field of machine learning and deep learning.

By mohith k

•May 06, 2020

It is an excellent course for beginners in Data Analytics. It teaches you all basic concepts required for data analysis which includes data pre-processing, data wrangling, data formatting, data normalization, data binning, Exploratory data analysis and data modelling. It also teaches you descriptive statistics including, Correlation, ANOVA etc., It also helps you with basic data visualization, Linear regression, prediction, decision making, Model evaluation and refinement using Ridge Regression and Grid Search. I find it very useful for beginners.

By Xiaowei Z

•May 01, 2020

To pass this course is really not easy as it doesn't just teach us how to code to fulfill the data analysis but it delivers a lot of relevant knowledge of statistics as well, including linear regression, polynomial regression, ridge regression, MSE, R2, ANOVA, etc. Coding is not difficult but understanding those methods of analysis is hard. so if you have little basis of statistics, you have to work harder. But I feel more confident after the course because I have gained one more skills. Keep on going and embrace the future.

- AI for Everyone
- Introduction to TensorFlow
- Neural Networks and Deep Learning
- Algorithms, Part 1
- Algorithms, Part 2
- Machine Learning
- Machine Learning with Python
- Machine Learning Using Sas Viya
- R Programming
- Intro to Programming with Matlab
- Data Analysis with Python
- AWS Fundamentals: Going Cloud Native
- Google Cloud Platform Fundamentals
- Site Reliability Engineering
- Speak English Professionally
- The Science of Well Being
- Learning How to Learn
- Financial Markets
- Hypothesis Testing in Public Health
- Foundations of Everyday Leadership

- Deep Learning
- Python for Everybody
- Data Science
- Applied Data Science with Python
- Business Foundations
- Architecting with Google Cloud Platform
- Data Engineering on Google Cloud Platform
- Excel to MySQL
- Advanced Machine Learning
- Mathematics for Machine Learning
- Self-Driving Cars
- Blockchain Revolution for the Enterprise
- Business Analytics
- Excel Skills for Business
- Digital Marketing
- Statistical Analysis with R for Public Health
- Fundamentals of Immunology
- Anatomy
- Managing Innovation and Design Thinking
- Foundations of Positive Psychology