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Learn Pandas for Data Science (Course IV)

Learn Pandas for Data Science (Course IV)

Welcome to Course IV!

Welcome to this course on Pandas for Data Science!

Pandas is a fast, powerful, flexible, high-level data analysis and manipulation tool, that is built on top of the Python programming language. It is one of the most widely used tools for data analysis and Machine Learning. In this course, you will learn how to use the Pandas library to perform data analysis, data manipulation and visualization using pandas.

Objectives of the course

The learning objectives of the course are set out as follows:

  • Learn about the two fundamental data structures used in Pandas: Series and DataFrame.
  • Learn how to load data from different data sources.
  • Learn about various data manipulation and analysis operations in Pandas.
  • Learn how to create plots using Pandas.

You can expect to have all of these objectives met by the time you reach the end of this course.

Pre-requisites for the course

If this is your first time working on Python, it may be hard for you to effectively grasp all the concepts. Therefore, the following pre-requisites are necessary for you to get the best out of the course:

  • Solid understanding of the Python programming language
  • Familiar with Numpy and Matplotlib libraries.

If you do not have the above pre-requisites, don’t worry! You can always come back later to this course once you are ready.

Best way to work through the course

The course is not long but requires a good amount of attention from your end.

Before moving to the next lecture, we suggest you to set up your coding environment and open up your Jupyter Notebook. If you are a more advanced user of Python and have your own preferences, please feel free to choose an IDE that you prefer. However, all of the coding examples will be written for execution on Jupyter Notebook cells.

If you come across any problem, please check to see if your code matches exactly with the course or not. If you still are facing errors or have some doubts, please provide your question through the comment section of the specific chapter you are stuck on.

We also recommend you join our community and get connected to our vibrant network of data science aspirants. Once you are in the community, you can share your learnings, form a study group or even get help building a project around Pandas.

All good? Let’s get started.

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