Module 1: Introduction to Data Science

• 1.1 What is Data Science?
  • Overview of Data Science and its applications
  • Data Science vs Machine Learning vs AI
  • The role of a Data Scientist
• 1.2 Tools and Libraries for Data Science
  • Python for Data Science
  • Introduction to Jupyter Notebooks
  • Essential Libraries: Pandas, NumPy, Matplotlib, Seaborn

Module 2: Python for Data Science Basics

• 2.1 Python Basics for Data Science
  • Variables, Data Types, Operators
  • Control Flow (if, else, loops)
  • Functions and Modules
  • Working with Python’s standard libraries
• 2.2 Working with Data Structures
  • Lists, Tuples, Sets, and Dictionaries
  • List comprehensions
  • Iterators and Generators
• 2.3 Data Handling and Manipulation with Pandas
  • Introduction to Pandas
  • DataFrames and Series
  • Importing Data (CSV, Excel, SQL, JSON)
  • Handling Missing Data
  • Filtering, Grouping, Sorting Data

Module 3: Data Visualization and Exploration

• 3.1 Introduction to Data Visualization
  • Importance of Visualization in Data Science
  • Basic Plotting with Matplotlib
  • Plotting with Seaborn (Histograms, Scatter plots, Box plots)
• 3.2 Advanced Visualization Techniques
  • Heatmaps, Pairplots, Violin Plots
  • Customizing Plots and Subplots
  • Plotly for Interactive Plots
• 3.3 Exploratory Data Analysis (EDA)
  • Analyzing Distribution of Data
  • Identifying Outliers and Handling Them
  • Correlation and Causation
  • Feature Engineering and Transformation

Module 4: Statistical Analysis

• 4.1 Introduction to Statistics for Data Science
  • Descriptive Statistics (Mean, Median, Mode, Standard Deviation)
  • Probability and Distributions (Normal, Binomial, Poisson)
  • Inferential Statistics (Hypothesis Testing, p-values)
• 4.2 Correlation and Regression
  • Correlation Coefficients
  • Linear Regression
  • Multiple Regression Analysis
  • Assumptions of Regression Models
• 4.3 Statistical Tests
  • T-tests, Chi-Square Tests, ANOVA
  • Confidence Intervals and Significance Tests

Module 5: Machine Learning with Python

• 5.1 Introduction to Machine Learning
  • What is Machine Learning? Types of Machine Learning
  • Overview of Supervised vs Unsupervised Learning
  • Train-Test Split and Model Evaluation
  • Cross-Validation and Hyperparameter Tuning
• 5.2 Supervised Learning Algorithms
  • Linear Regression
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
• 5.3 Unsupervised Learning Algorithms
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • DBSCAN
• 5.4 Model Evaluation and Improvement
  • Accuracy, Precision, Recall, F1 Score
  • ROC Curve and AUC
  • Bias-Variance Tradeoff