Data Visualisation with Seaborn


Welcome to the session on data visualisation with seaborn library.

In the previous session, you understood the importance of data visualisation and were introduced to the various types of visual aids available to us for portraying insights. After that, you have started the visualisation journey with a case study: analysing the Google Play Store Apps rating data set. Here, you learnt a couple of key data-handling and cleaning tasks that need to be performed before you can start analysing your data and communicate insights with charts and graphs. You also learnt that box plots and histograms are quite useful not only for communicating insights but also for some basic data-cleaning procedures like outlier analysis.

In this session

You will learn about another library, Seaborn, which is used predominantly to create beautiful and aesthetic statistical plots in Python. Let’s listen to Rahim as he introduces this library and explains its various features.

You can go through the official Seaborn documentation to gain further understanding of this library. You will learn about the importance of each visualisation as you go through the case study and derive the insights. You will have to continue working with the same notebook that you had used earlier for the case study part and this needs to be worked on along with the videos to ensure that you are answering the questions along with videos.

Guidelines for in-module questions

The in-video and in-content questions for this module are not graded. There are no graded questions for this session.

People you will hear from in this session

Analytics Lead, Flipkart

Rahim, a BITS Pilani graduate, has around 9 years of experience in advanced analytics and machine learning He is currently the Analytics Lead at Flipkart Pvt Ltd., an Indian electronic commerce company based in Bengaluru, India.