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W2_L3: Describing categorical data - best practices while graphing data - 1
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Statistics for Data Science 1 - W2_L3: Describing categorical data - best practices while graphing data - 1

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  • 51.3 hours of video
  • Certificate of completion
  • Access on mobile and TV

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Welcome to Week 2 Lecture 3 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 This lecture focuses on best practices for visualizing categorical data and choosing the most effective way to represent your information. You will learn how to decide between using a bar chart, pie chart, or frequency table based on the purpose of your data analysis. The session explains when to use a pie chart to compare parts of a whole, when a bar chart is better suited for comparing discrete quantities, and when a frequency table provides the clearest summary, especially with larger category sets. Additionally, you will learn how to label and customize charts in Google Sheets to ensure that your visualizations communicate insights accurately and effectively. 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

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