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....

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.

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.

筛选依据：

创建者 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.

创建者 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

创建者 Benjamin J

•Dec 01, 2018

many mistakes throughout

创建者 Jennifer R

•Apr 01, 2020

The topic is very interesting, but the execution was poor. Code and numbers were just being read at me, instead of focusing the recorded lectures on teaching concepts and troubleshooting, and leave the code to be read by myself in the labs. Also, the quizzes along the way were nearly useless: only two questions, a "pass with at least 50%", and the questions asked were very superficial. This is the most poorly executed course I have taken on Coursera so far.

创建者 Nizami I

•Oct 06, 2019

The course structure and videos are nice, but THERE ARE SO MANY ERRORS in the videos. I spent so much time to google and fix these errors. It is really terrible and I dont understand how people gave the high grade. I stopped watching videos after Week 3, because I fed up correcting their errors. Although people have mentioned it long time ago, but nothing has changed. Really shame on Coursera and IBM that have such quality!!!

创建者 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.

创建者 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.

创建者 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.

创建者 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.

创建者 Dr_G@ur

•Jul 21, 2020

The course nicely gives you a glimpse of the endless possibilities in the area of Analytics. It showcases how data can be easiely and speedily analyzed using Python if you are clear even with the basics of Python programming. It provides a prefect platform to gain skill sets needed to be a great Analyst.

The course is wonderfully desined, the material within seems self-explanatory and you won't have to struggle to grasp the concepts taught. Labs are awesome and so is the team who made the course what it is. Really loved it!

创建者 Maitha S K ( O - I

•Feb 18, 2020

Honestly it is one of the best courses I've attended in Data Science. All the ambiguous concepts that I read in the internet and couldn't understand were clear in this course and I didn't have to struggle to get them. The way the course is structured, the visual materials, labs, quizzes and assignments ensure that you leave the the course with good theoretical and technical understanding. Thanks for making it easy to learn Data Science and python! I would definitely recommend this course if you want to have a good start.

创建者 Ankur G

•Apr 29, 2020

Loved the course overall. Truly amazing! Professors did a really great job in making and structuring this course session by session.

A good course to learn know-how of Data Analysis using Python language so as to facilitate analysis and visualization of data to make effective decisions. I thank the professors to make this course interesting and worth it. Only thing is, videos can be made in a better way so as to facilitate people with non programming background. Maybe some basics of programming would help.

创建者 Clarence E Y

•Mar 08, 2019

Become a Trustworthy Data Analyst

This course provides the knowledge and skills that form the foundation for data analysis. Students learn how to use Python Packages and gain experience creating dataframes and manipulating data sets for computation and visualization. Extensive work on building and evaluating models is included with explanatory lectures and hand-on labs to work with real data. Students' data analysis work will be supported by applying proper of model optimizations learned in the course.

创建者 Shuyao H

•Jun 02, 2020

A step-by-step and detailed introduction to data analysis using Python. It covers a 0 to 1 understanding from importing data to evaluating models, and offers hand-on labs to run codes. The content also includes all the packages and libraries necessary and essential to do data analysis. The courses are somehow in detail, if not, hard, but the tests and assignments are easier. I am sure I will always review the codes I have learned in the course in the future when I go deeper into data analysis.

创建者 Shripathi K

•Aug 19, 2019

I audited the course. I did not complete the quizzes because my goal was to get a very quick overview of pandas and scikit and pick up on basics. This was at the right level for me and did not go haphazardly. It did not try to convince me that something was simple, hard or not important.

I recommend this as a starting point for most who have little experience with Python but are well-versed in programming otherwise and want to get a look at a little of the ecosystem for ML using Python.

创建者 Elizabeth S

•Jul 03, 2020

I will say an excellent class! You will learn a lot essential data analysis methods, and the concepts.

Ok, it's never easy for someone who never learned such knowledges before, now encounters all those statistics concepts along with python code. But still, this class managed to use an easy way to explain all those abstract concepts. The forum also helps a lot to explain some difficulties. You might feel lost in the models, but once you learn it, you feel good.

创建者 Milan D

•Feb 03, 2019

Really good stuff in terms of outlining what is necessary in order to properly analyze the data. One thing to note is the powerpoint slides are off sometimes. Some of the stuff is not spelled correctly in the code.

Another issue is that x and y axis variables will be assigned, but be on the opposite axes (I.E when x = df['price'] but in the scatterplot it's actually the target variable, and thus on the y-axis.

创建者 SOUMYA G

•Apr 14, 2020

This is an excellent course to begin with analyzing data in python. However, it would have been even more useful and interesting had it contained some more discussions on the topics like logarithmic transformation of features, when to apply it, how to do bi-variate and mutivariate analysis, exercises on topics like manipulation of dataframes using pivot, melt, crosstab etc.

创建者 Rishi S

•Sep 11, 2019

Fantastic introduction to some of main python libraries and functions used in order to do anything related to data analysis, also a good entry point for machine learning, big data and other data science specialisations - highly recommended for anyone comfortable with high level scripting and basic oops concepts - if you don't then best take a basic course in python first...

创建者 BrajKishore P

•Aug 08, 2019

The course material was excellent , quizzes makes this course more efficient and handy, all the lectures are explained well , the most important part of this course providing notebooks of each week for self practicing and to judge our-self . Discussion forums are provided asking queries, Overall the course was excellent both for beginners and intermediate.

创建者 Arindam G

•Dec 20, 2018

No Doubt COURSERA is always best AND MNC like IBM,Google courses associated with coursera are MIND-BLOWING.

The Instructors are so great at Explanation Part that hardly anyone won't Understand All the Topics

I would love to thank all the INSTRUCTORS who created such a Awesome Content for us.

My Personal Ratings For All the Instructors: 100 / 100

创建者 Thierno

•May 28, 2020

Excellent Course, i've learned a lot, i can analyze any data and give a conclusion from it. It's great course with a very clear explanation. If you are not understanding from the videos you can have a full understanding of the course from the Lab Notebook. The best is giving you a chance to access on IBM Cloud, creating new dynamic projects. Thank you

创建者 Ramanathan K

•May 07, 2020

Initial part of course was easy, but the labs proved more and more useful. As I learned the course, I applied the charting skills directly to work, and was able to use Pandas to combine data from 3 databases, evaluate and report on the data to my company. It is already making a difference in our ability to make better data driven decisions every day.

创建者 Mona A

•Jun 17, 2019

Great Course! I got a great insight into multiple steps involved in data analysis using python starting from an initial data set to pre-processing it, exploratory analysis, doing multiple operations to create possible models and ways to evaluate the models. I hope to be able to use them to solve some sample data sets and come up with possible models