Hypothesis testing in Data Science - Part 1 Coding

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The word ‘Hypothesis’ originates from the Greek words ‘hupo’, which means under and ‘thesis’, which means placing. Inferring an idea using limited evidence that can be used as a starting point for further investigation. Hypothesis Testing refers to using a systematic procedure to decide whether data and research study can support our theory/assumption/insight which applies to a population.
In the world of Data Science, there are two parts to consider when putting together a hypothesis. Hypothesis testing is a systematic approach used to evaluate a hypothesis and determine if there is enough evidence to support it. Hypothesis generation, on the other hand, is the process of coming up with a hypothesis based on available information and intuition. Both are important in data science as they help guide the project and provide insights into the relationship between variables. Hence it forms an integral part of exploratory data analysis, it can help us in quantifying the insights which we draw from visualization.
Keeping this in view, I have created a series of videos starting with "Introduction to Probability" which will take you through the basics of probabilistic techniques used in Data Science.

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