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Fundamentals Of Statistics For Data Scientists and Analysts
Key statistical concepts for your data science or data analysis journey with Python Code
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)
- Gauss-Markov Theorem
- Parameter properties (Bias, Consistency, Efficiency)
- Confidence intervals
- Hypothesis testing
- Statistical significance
- Type I & Type II Errors
- Statistical tests (Student's t-test, F-test)
- p-value and its limitations
- Inferential Statistics
- Central Limit Theorem & Law of Large Numbers
- Dimensionality reduction techniques (PCA, FA)If you have no prior Statistical knowledge and you want to identify and learn the essential statistical concepts from the scratch, to prepare for your job interviews, then this article is for you. This article will also be a good read for anyone who wants to refresh his/her statistical knowledge.
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