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....

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.

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.

筛选依据：

创建者 Luciana M G

•Mar 14, 2019

This course is an excellent continuation of the previous IBM ones. Actually there should be one whole course teaching the basics of statistics so that what is taught in this model makes more sense for those who have never studied statistics before.

创建者 Riccardo B

•Jul 14, 2020

I really enjoyed this course. The content was well described. Quite complex, it required me to explore other external material, probably because I lacked the sufficient statistical knowledge to run through the course smoothly.

Thank you Coursera!

创建者 Manoj M

•Apr 1, 2021

Great Course which gave in detail information and Hands on lab about different visualization libraries available in Python such as Matplotlib, Seaborn and plotly for charts and dashboards. Useful course to understand and learn data visualization

创建者 Guy B

•Sep 24, 2019

Great course. few things to make it perfect:

more mathematic explanations required.

more details and explanations about the code itself - the methods and the opportunities inside it.

make the videos more interesting and not so monotonic and boring.

创建者 mustapha b

•Jan 6, 2020

I thank Mr. Joe Santarcangelo 🙏🏼 who helped me learn how to prepare data for hashtag#analysis, perform simple hashtag#statistical analysis, create meaningful hashtag#data visualizations and hashtag#predict future trends from the data👨🏼💻.

创建者 Ronald A

•Aug 17, 2020

The videos explained carefully the structure of the codes and explained the result. The graded quizzes and particularly the final project is also very helpful because it will let you review your notes and test your understanding of the course.

创建者 Swapna N

•May 13, 2020

It was a wonderful experience in learning the course. It was completely a practical approach. I enjoyed the process of assignment submission and reviewing peers assignments. I would highly recommend the course for everyone. Thanks once again.

创建者 Mike F

•Oct 18, 2019

Outstanding course! Valuable information and methodologies all with clear and concise presentation. The labs are detailed and filled with awesome examples. Coursework is intuitive and easy to understand. I would highly recommend this course.

创建者 Saurabh M

•Aug 31, 2020

Its very nicely designed course .Its designed such that you get brush up your fundamentals and get to know the real taste of statistics and probability applying practically to real world. i really enjoyed and earned a valuable knowledge.

创建者 Ricardo S

•Feb 24, 2020

La calidad de los cursos de coursera es excelente. Obviamente tienen detalles que se deben trabajar como algunas presentaciones que no coinciden con las voces en off. Sin embargo con suspicacia se pueden solventar estos infimos detalles.

创建者 Ritik K

•Jul 2, 2020

I learnt a lot in this course, the teaching way is quite good with animation and real life based example. I must say the course is designed very carefully. I want to thanks all the course creators to gave us such a great opportunity..

创建者 Jafed E G

•Jul 6, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

创建者 V.S.S. T

•May 15, 2020

Very helpful and useful course especially for beginners who are willing to gain knowledge on Data Analysis. I would recommend starting with this course for those who are interested in mastering their skills in Data Science later on.

创建者 Sayak P

•May 13, 2020

This course is based mostly on very basic concepts , it's good for those lacking the slightest knowledge in the field of statistics , but yes for beginners it's pretty fine.I loved the presentation of those labs,quite useful

创建者 Phil L

•May 31, 2020

Really good course. More challenging than I expected. I expect it will take a few applications of the concepts to get them down 100%. Very good presentation of material. The labs were critical to learning the concepts.

创建者 QUAN Z

•Feb 25, 2019

The course perfectly fits those who has some knowledge on python and want to do data analysis with it. It explains how professionals would process data, build model with the data, and use the model to solve a real problem.

创建者 Min T A

•Mar 18, 2021

This course covers exploratory data analysis and even furthers onto machine learning with some key statistical introduction such as Linear Regression, Correlation, P-value, F-score, etc, explained in its most clear form.

创建者 Pranav K J

•May 10, 2020

Very good course and well designed , so that a new person also can understand it very well. They way it is taught is admirable. I will recommend, the aspiring data science Engineer, must take this course specialisation!

创建者 Jonathan I O

•Jul 14, 2019

This course provides a robust walk-through in the use of python for data analysis. The labs ensure the theories taught are put into practice through hands-on projects that further reinforces skills learned. I loved it!

创建者 Aman S

•Mar 26, 2020

A very detailed course. The hands-on exercises were really good and I got to learn a lot of things from this wonderful course. Thanks to all the instructors for their hard work in putting together such a course for us.

创建者 Paul A

•Sep 24, 2020

I think this course is the highlight of the Applied Data Science specialization. I learned so much and gave me the tools to learn more on my own. It was really engaging and I never had a dull moment in this Course.

创建者 Roseline A

•Jul 9, 2020

This is a great course. I went away with so much knowledge on modelling and model testing. The labs are also very well structured and not just a repetition of the class presentation. I recommend this course highly.

创建者 Ferenc F P

•Feb 26, 2019

The beginning of the course helps you understanding how you can manage your data with python. In the end linear regression, and ridge regression is also introduced. Good course for those not familiar in this field.

创建者 Wilfredo A

•Aug 30, 2018

Excellente content and very didactic laboratory. There is a lot of information in the course and at the same time it encourages me to investigate and further develop, particularly in Model Evaluation and Refinement

创建者 Brian B

•Dec 5, 2020

Very "meaty" hands-on work with doing some data wrangling, exploratory analysis and models with single linear, multiple linear, and polynomial regression fits. I took several hundreds of lines of code in my notes.

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