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This week we will skill up on Probability and Statistics, an essential skill set for data science. While Probability is used for the prediction of future events, statistics is used for the analysis of past events.
The practical knowledge and skills gained from this are applied in A/B testing and Hypothesis/Research frameworks. We will learn about basic probability, probability distribution, set theory, independence and conditional probability, Bayes theorem, A/B testing theory, Null and Alternative hypothesis, and their different applications with case studies.
Here are some of the best resources I found:
Useful Resources
There are tons of free materials with practice problems on probability, A/B testing theory, and statistics. These could become overwhelming if trying to master them quickly but consistency is valuable here and the more you practice the more you get used to the terms and concepts. Also, the avalanche of resources out there makes it difficult to know when to draw the line and just be content with what you have learned.
Here is what works for me, I know I have thoroughly understood a concept when I can apply the knowledge to solve practical problems. And because I mostly think and process things in pictures, I would not spend too much time on a concept that I have already understood. The key is to find a learning pattern that works best for you and stick with it. Make consistent baby steps and you will be amazed at how much you have learned and can accomplish.
At the end of this series, we would have gained the practical and theoretical knowledge of probability and statistics, can apply A/B testing, Null and Alternative hypothesis frameworks, and be able to work independently on projects to apply our new skills.
Happy Learning!