Statistics for Data Science 1 - W8_L1: Random variables - introduction

Welcome to Week 8 Lecture 1 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 introduces the foundational concept of Random Variables, a key idea in probability and statistics. Building on the understanding of random experiments, sample spaces, events, and probability, we define what random variables are and how they connect possible outcomes to numerical values. The lecture explores discrete and continuous random variables, introduces the probability mass function (PMF) and cumulative distribution function (CDF), and explains expectation and variance with clear examples and real world interpretations. By the end, learners will understand how probabilities are assigned to the values of a random variable and how these concepts form the basis for further statistical analysis. 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 #RandomVariables #Probability #Statistics #RandomExperiment #SampleSpace #Events #ProbabilityMassFunction #CumulativeDistributionFunction #Expectation #Variance #DiscreteRandomVariable #ContinuousRandomVariable #DataScience #QuantitativeMethods #StatisticalLearning #DataScienceEducation