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Learn basic machine concepts and how statistics fits in.Īfter completing these 3 steps, you'll be ready to attack more difficult machine learning problems and common real-world applications of data science.
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Here are the 3 steps to learning the statistics and probability required for data science: If you do have a formal math background, this approach will help you translate theory into practice and give you some fun programming challenges. It allows you to think through the logical steps of each calculation. If you do not have formal math training, you'll find this approach much more intuitive than trying to decipher complicated formulas. In fact, we're going to tackle key statistical concepts by programming them with code! Trust us. Mastering statistics for data science is no exception. The Best Way to Learn to Statistics for Data Scienceīy now, you've probably noticed that one common theme in "the self-starter way to learning X" is to skip classroom instruction and learn by "doing sh*t." This will all make sense once you roll up your sleeves and start learning. If those terms sound like mumbo jumbo to you, don't worry. Key concepts include conditional probability, priors and posteriors, and maximum likelihood. Bayesian thinking is the process of updating beliefs as additional data is collected, and it's the engine behind many machine learning models. Key concepts include probability distributions, statistical significance, hypothesis testing, and regression.įurthermore, machine learning requires understanding Bayesian thinking. These concepts will help you make better business decisions from data. For example, data analysis requires descriptive statistics and probability theory, at a minimum.