![]() ![]() Statistics, together with Probability, can be found literally everywhere in Data Science, from data analysis and visualization to machine learning and statistical modeling. Statistical Analysis often uses probability distributions and theorems, and the two subjects are usually studied together. Statistics and Probability are two related but separate academic disciplines. ![]() It is particularly useful when we need to draw conclusions about a population through analyzing a finite sample from this population. Statistics is the branch of mathematics that deals with the collection, organization, analysis and interpretation of data. In this blog post, we will go over what statistics is, the important role it plays in data science, the fundamental concepts that need to be learned, and lastly, we will provide a curated list of resources to cover them. This profile can be matched with that of a statistician that employs more sophisticated tools and has a vast experience dealing with relatively large and messy datasets. One of these variants, called the data scientist – product analytics, or equivalently, data scientist type A (A for analysis) is by far the most prevalent one. This trend is paving the way for the specialist profile to shine, eclipsing the data science generalist in the way. ![]() As the data job market continues to mature, it has been observed recently that a few key roles or breeds with specific requirements and visions are starting to form, all derived from the original field. ![]() As such, statistics plays a central role here, allowing the practitioner to draw conclusions about the characteristics of a population by analyzing a sample of it, which is usually related to the products and/or clients of the company.ĭata Science today has become a very general term, with each party adopts its own definition of it, according to their context and business objectives. ![]()
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