2.1 Introduction to Python

• Why Python for Data Science?
• Python installation (Anaconda/Jupyter/VSCode)

2.2 First Program

• print(), understanding code structure

2.3 Comments

• Single-line, multi-line, docstrings

2.4 Data Types

• 2.4.1: Numbers, Strings
• 2.4.2: Lists, Tuples, Sets, Dictionaries

2.5 Operators

• 2.5.1: Arithmetic, Comparison
• 2.5.2: Logical, Bitwise, Membership

2.6 Variables

• 2.6.1: Variable declaration, rules
• 2.6.2: Type casting

2.7 Syntax

• Python indentation, blocks, line continuation

2.8 Working with Numbers

• math module, rounding, random numbers

2.9 Working with Strings

• 2.9.1: Basic operations, slicing
• 2.9.2: String methods, formatting

2.10 Date & Time

• Working with datetime module

2.11 Conditional Statements

• 2.11.1: if, else
• 2.11.2: elif, nested conditions

2.12 For Loop

• 2.12.1: Basic loop, range()
• 2.12.2: Nested loops, break, continue

2.13 While Loop

• Loop conditions and control

2.14 Lists, Tuples, Sets

• 2.14.1: List operations
• 2.14.2: Tuple properties
• 2.14.3: Set operations

2.15 Dictionaries

• 2.15.1: Keys, values
• 2.15.2: Methods, nesting

2.16 Functions

• 2.16.1: Defining functions, parameters, return, scope

2.17 NumPy

• 2.17.1: Arrays, creation, indexing
• 2.17.2: Array operations, broadcasting
• 2.17.3: Statistical & mathematical functions

2.18 Matplotlib

• 2.18.1: Line & bar charts
• 2.18.2: Histograms, pie charts, customization

2.19 Pandas

• 2.19.1: Series, DataFrame creation, indexing
• 2.19.2: Data cleaning, merging, groupby, filtering

3.1 Overview of Math Requirements

• Algebra, statistics, probability, linear algebra

3.2 Introduction to Statistics

• Mean, median, mode

3.4 Measure of Spread

• Variance, standard deviation, IQR, range

3.5 Probability

• Classical, conditional, joint and marginal probability

3.6 Conditional Probability

• Bayes Theorem, independence

3.7 Data Scientist vs Statistician

• Skills, tools, responsibilities

3.8 Data Preprocessing

• Normalization, standardization, handling missing values

4.1 Introduction to ML

• What is ML, differences with AI & DL

4.1.1 Types of ML Algorithms

• Supervised, Unsupervised, Reinforcement

4.2 Steps in Building ML Model

• Define problem → Collect Data → Clean Data → Train → Test → Evaluate

4.3 Linear Regression

• Concept, simple & multiple regression, evaluation metrics

4.4 Logistic Regression

• Binary classification, sigmoid function

4.5 K-Nearest Neighbors (KNN)

• Distance metric, choosing K, overfitting/underfitting

4.6 Naive Bayes

• Bayes theorem, spam classification

4.7 Clustering Overview

• Types and applications

4.8 K-Means Clustering

• Centroid selection, elbow method

4.9 Hierarchical Clustering

• Dendrograms, Agglomerative vs Divisive

4.10 Dimensionality Reduction

• Purpose and techniques

4.10.1 Principal Component Analysis (PCA)

• Variance, eigenvalues, dimensional reduction

4.10.2 Linear Discriminant Analysis (LDA)

4.11 Supervised vs Unsupervised Learning

• Characteristics and examples

4.12 Semi-Supervised Learning

• Real-world examples, pseudo-labeling

4.13 Reinforcement Learning

• 4.13.1: Agents, environment, reward system
• 4.13.2: Q-learning, exploration vs exploitation

4.14 Data Science vs Machine Learning

• Comparison table and use cases

5.1 Deep Learning

• Neural networks, perceptrons, backpropagation

5.2 TensorFlow and Keras

• Building a neural network, training, evaluating

5.3 Data Mining

• Patterns, knowledge discovery, CRISP-DM

5.3.1 Preprocessing in Data Mining

• Feature selection, discretization, transformation

5.4 Natural Language Processing

• Tokenization, stemming, lemmatization

5.4.1 Preprocessing in NLP

• Stopword removal, word embeddings (TF-IDF, Word2Vec)