STATISTICS AND PROBABILITY FOR DATA SCIENCE Article Swipe
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.58532/nbennuraith2
· OA: W4411839927
This chapter provides a comprehensive overview of foundational statistical concepts essential for data science. The initial part of statistics focuses on descriptive statistics which defines data description using mean and median and mode and variance alongside the summary of dataset features. The chapter then explores probability theory and common probability distributions-including normal, binomial, and Poisson-which form the basis for modeling uncertainty and real-world phenomena. Expanding on these basics, the material delves into inferential statistics-such as hypothesis testing and confidence intervalswhich equip data scientists to make informed judgments based on sample data. The text also introduces regression analysis, highlighting both linear and logistic approaches as essential tools for understanding variable relationships and forecasting outcomes. Throughout, practical examples and solved problems illustrate how statistical methods are applied to real-world data science scenarios. By mastering these core topics, readers will be well-equipped to analyze data, interpret results, and make informed decisions in a data-driven environment [1].
