Statistics for Data Science 1 - W2_L4: Describing categorical data - best practices while graphing data - 2

Welcome to Week 2 Lecture 4 of the course "Statistics for Data Science - I" by Prof. Andrew Thangaraj. Full Course: https://study.iitm.ac.in/ds/course_pages/BSMA1002.html Video Overview In this lecture, we continue our discussion on the best practices for visualizing categorical data. You will learn how to choose the right type of graphical representation—bar chart, pie chart, or frequency table—based on the purpose of your analysis and the nature of your dataset. The session explains when a pie chart effectively compares proportions of a whole, when a bar chart is more suitable for comparing distinct categories, and when frequency tables provide a clearer picture for larger or more complex data. The lecture also highlights techniques for labeling and customizing charts in Google Sheets to enhance clarity, readability, and communication of insights. By the end of this session, you will be able to make informed choices about the most appropriate visualization methods for categorical data. About IIT Madras' online Bachelor of Science programme IIT Madras offers four-year BS programmes that aim to provide quality education to all, irrespective of age, educational background, or location. The BS programme has multiple levels, which provide flexibility to students to exit at any of these levels. Depending on the courses completed and credits earned, the learner can receive a Foundation Certificate from IITM CODE (Centre for Outreach and Digital Education), Diploma(s) from IIT Madras, or BSc/BS Degrees from IIT Madras. For more details, Visit: https://www.iitm.ac.in/academics/study-at-iitm/non-campus-bs-programmes #DataVisualization #BarChart #PieChart #FrequencyTable #DataAnalysis #DataInterpretation #GoogleSheets #ChartLabeling #ChartCustomization #DataStorytelling #DataPresentation #Analytics #DataSkills #VisualizationTechniques #DataCommunication #IITMadras #OnlineLearning #BSProgramme #Education #StatisticsForDataScience #DataScience