Fundamentals Of Statistics For Data Scientists and Analysts

Key statistical concepts for your data science or data analysis journey with Python Code

Tatev Karen Aslanyan
LunarTech

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Image Source:Pexels/Anna Nekrashevich

As Karl Pearson, a British mathematician has once stated, Statistics is the grammar of science and this holds especially for Computer and Information Sciences, Physical Science, and Biological Science. When you are getting started with your journey in Data Science or Data Analytics, having statistical knowledge will help you to better leverage data insights.

“Statistics is the grammar of science.” Karl Pearson

The importance of statistics in data science and data analytics cannot be underestimated. Statistics provides tools and methods to find structure and to give deeper data insights. Both Statistics and Mathematics love facts and hate guesses. Knowing the fundamentals of these two important subjects will allow you to think critically, and be creative when using the data to solve business problems and make data-driven decisions. In this article, I will cover the following Statistics topics for data science and data analytics:

- Random variables
- Probability distribution functions (PDFs)
- Mean, Variance, Standard Deviation
- Covariance and Correlation
- Bayes Theorem
- Linear Regression and Ordinary Least Squares (OLS)

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