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. Therefore, it shouldn’t be a surprise that data scientists need to know statistics.įor example, data analysis requires descriptive statistics and probability theory, at a minimum. Wikipedia defines it as the study of the collection, analysis, interpretation, presentation, and organization of data. Statistics is a broad field with applications in many industries. Here’s why… Statistics Needed for Data Science It’s essential to progressing your career as a data scientist. ![]() You see, it can be tempting to jump directly into using machine learning packages once you’ve learned how to program… And you know what? It’s ok if you want to initially get the ball rolling with real projects.īut, you should never, ever completely skip learning statistics and probability theory.
0 Comments
Leave a Reply. |