Introduction to Data Science

Module 1: Overview of Data Science

Lesson 1: What is Data Science?

  • Definition of Data Science
  • Importance of Data Science
  • Applications in Various Fields

Lesson 2: The Data Science Workflow

  • Data Collection
  • Data Cleaning
  • Data Analysis
  • Data Visualization
  • Model Building
  • Model Evaluation

Lesson 3: Tools and Technologies

  • Overview of Python Libraries
  • Introduction to Jupyter Notebooks
  • Other Tools (e.g., R, SQL)

Module 2: Python for Data Science

Lesson 1: Introduction to Python

  • Python Basics
  • Setting Up Python Environment
  • Python Syntax and Semantics

Lesson 2: Python Basics

  • Variables and Data Types
  • Control Flow
  • Functions and Modules

Lesson 3: Data Structures

  • Lists, Tuples, and Dictionaries
  • Sets and Strings
  • Data Manipulation

Lesson 4: Libraries for Data Science

  • NumPy Basics
  • Pandas for Data Manipulation
  • Matplotlib for Data Visualization

Module 3: Statistics and Probability

Lesson 1: Descriptive Statistics

  • Measures of Central Tendency
  • Measures of Dispersion
  • Data Distribution

Lesson 2: Inferential Statistics

  • Sampling Techniques
  • Confidence Intervals
  • Hypothesis Testing

Lesson 3: Probability Theory

  • Probability Basics
  • Distributions
  • Bayes' Theorem

Lesson 4: Hypothesis Testing

  • Null and Alternative Hypotheses
  • Types of Errors
  • Statistical Tests (e.g., t-test, chi-square)