Statistics for Data Science

Welcome to the section on "Statistics for Data Science"! This course is designed to provide you with a solid foundation in statistical concepts and techniques that are essential for data analysis and interpretation in the field of data science.

Course Outline

Introduction to Statistics: Understanding the basics of statistics, including measures of central tendency, dispersion, and probability.
Descriptive Statistics: Learning how to summarize and describe data sets using various statistical measures.
Inferential Statistics: Exploring the principles of inferential statistics, including hypothesis testing and confidence intervals.
Probability and Distributions: Delving into the theory of probability and common probability distributions.
Regression Analysis: Applying regression techniques to analyze relationships between variables.
Time Series Analysis: Understanding and analyzing time-based data to identify trends and patterns.
Advanced Topics: Exploring more complex statistical methods and their applications in data science.

Course Materials

Textbooks: "An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
Online Resources: Access to a variety of online resources, including tutorials, articles, and case studies.

Learning Objectives

Understand the fundamental concepts of statistics.
Apply statistical methods to analyze and interpret data.
Develop a strong foundation in inferential statistics.
Learn advanced statistical techniques and their applications in data science.

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