It is hard to live without generating and using data. Bayesian Data Analysis builds upon basic probability theory and gives us the necessary tools to deal with realistic data. In this session, I will make no assumptions on your knowledge of probability or random processes. Using the very popular case of coin tosses, I introduce the concepts of Prior, Likelihood and Posteriors. This gives me the platform to discuss Bayes Rule and Bernoulli Distribution. Further, the algebraic limitation of such an analysis leads us to appreciate the purpose and properties of Beta Distributions. If you have heard of "Weak Priors", "Strong Priors" and "Conjugates", but never quite got a chance to study them, you will find this talk very interesting. Ever since my joining PhD@IIIT, it has been a journey through Randomized Algorithms, Information Retrieval, Intelligent Systems and Statistical Computation - all of which deal with related perspectives on probability theory. General wisdom claims that discussing fundamentals is relatively harder than discussing advanced stuff. I look forward for this discussion and hope that you will find value in it.