Pandas Python Library for Beginners in Data Science
Learn how to clean data using pandas.
Learn how to do basic data preprocessing.
Learn how to handle quantitative data (numeric data) and qualitative data (text data) with pandas.
Note 1: As a beginner you are not yet ready to work with real world data. So real world data is not used in this project. Note2 : If you are already familiar with pandas and want to work with real world data, check out the intermediate course here: https://www.coursera.org/projects/intermediate-pandas-python-library-data-science This guided project is for college students or those who have not heard of pandas before and want to learn about the syntax in pandas, one of the most important python libraries for data analysis. By the end of this project, you will master the basics of pandas. While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning. Special Features: 1) This project provides plenty of challenges with solutions to encourage you to practice using pandas. 2) Libraries are automatically imported each time you begin a new session. Just open the project and start learning! 3) The real world applications of each function is explained. 4) After you complete this project, you get a jupyter notebook of all the work you covered (including gifs). It acts as a useful learning tool that you can refer to at any time in the future. 5) Best practices and tips are provided to ensure that you learn how to use pandas efficiently. 6) Animated gifs are used to aid in the learning process. 7) Important terminology and definitions are explained. 8) Simple language is used throughout the project, so that you can focus on coding. (Eg: Quantitative data is referred to simply as numeric data.) Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Skills you will develop
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Three methods of creating a series.
Two methods of creating data frames.
Importing/exporting different types of data files and viewing rows.
Get a summary of the data & view column names and data types.
Calculate mean & cumulative sum. Determine minimum & maximum values.
String operations such as converting to uppercase letters , lowercase letters, swap case, finding the length of a string, splitting strings and detecting unique values.
Repeating strings.
How Guided Projects work
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Reviews
TOP REVIEWS FROM PANDAS PYTHON LIBRARY FOR BEGINNERS IN DATA SCIENCE
it really such an amazing course for begineers to learn pandas. what excited me the most is the the mentor did solve all the challenges to make a learner to the best.
For an hour of python coding, I think this is perfect, but it could have been a bit longer and introduced to some complex topics.
The content was good but the interface was a bit irritating. It probably would have been better if my screen were bigger
Great start for learning basic pandas functions. It would be great if proper explanations are provided for every step.
Frequently asked questions
Frequently Asked Questions
What will I get if I purchase a Guided Project?
Are Guided Projects available on desktop and mobile?
Who are the instructors for Guided Projects?
Can I download the work from my Guided Project after I complete it?
What is the refund policy?
Is financial aid available?
Can I audit a Guided Project and watch the video portion for free?
How much experience do I need to do this Guided Project?
Can I complete this Guided Project right through my web browser, instead of installing special software?
What is the learning experience like with Guided Projects?
More questions? Visit the Learner Help Center.
Coursera Footer
