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I share my experience in the course, first of the Micromaster in Statistics and Data Science, and provide my recommendations to succeed in it. Contents: Unit 1: Probability models and axioms Lecture 1: Probability models and axioms Unit 2: Conditioning and independence Lecture 2: Conditioning and Bayes rule Lecture 3: Independence Unit 3: Counting Lecture 4: Counting Unit 4: Discrete random variables Lecture 5: Probability mass functions and expectations Lecture 6: Variance, conditioning on an event, multiple random variables Lecture 7: Conditioning on a random variable, independence of random variables Unit 5: Continuous random variables Lecture 8: Probability density functions Lecture 9: Conditioning on an event, multiple random variables Lecture 10: Conditioning on a random variable, independence, Bayes rule Unit 6: Further topics on random variables Lecture 11: Derived distributions Lecture 12: Sums of independent random variables, covariance and correlation Lecture 13: Conditional expectation and variance revisited, sum of a random number of independent random variables Unit 7: Bayesian inference Lecture 14: Introduction to bayesian inference Lecture 15: Linear models with normal noise Lecture 16: Least mean squares estimation Lecture 17: Linear least mean squares estimation Unit 8: Limit theorems and classical statistics Lecture 18: Inequalities, convergence, and the weak law of large numbers Lecture 19: The central limit theorem Lecture 20: An introduction to classical statistics Unit 9: Bernoulli and poisson processes Lecture 21: The Bernoulli process Lecture 22: The Poisson process Lecture 23: More on the Poisson process Unit 10: Markov chains not graded Lecture 24: Finite-state Markov chains Lecture 25: Steady-state behaviour of Markov chains Lecture 26: Absorption probabilities and expected time to absorption