Data Science Course | ClassNotes.Site

Course Outline

1Introduction to Data Science

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1.1 What is Data Science?

The three pillars of Data Science

Data Science vs Analytics vs ML vs AI

Career opportunities in Data Science

The 6-phase Data Science Lifecycle

Real-world example: Netflix recommendations

1.2 Environment Setup

Installing Python & pip

Setting up VS Code editor

Introduction to Jupyter Notebooks

Working with cells & keyboard shortcuts

Common issues & troubleshooting

Assignment: Your First Data Science Project

2Python Fundamentals

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2.1 Python Basics

Why Python for Data Science

Variables & data types (int, float, str, bool)

Operators (arithmetic, comparison, logical)

Control flow (if/elif/else, for, while)

Functions & lambda expressions

2.2 Data Structures

Lists: indexing, slicing, methods

Tuples: immutability & unpacking

Dictionaries: key-value pairs & iteration

Sets: unique values & operations

Comprehensions (list, dict, set)

2.3 File Handling

Reading & writing text files

CSV file operations

JSON data handling

Context managers (with statement)

Assignment: Python Practice Problems

3NumPy - Numerical Computing

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3.1 NumPy Arrays

Creating arrays (zeros, ones, arange, linspace)

Array attributes (shape, dtype, ndim)

Indexing & slicing arrays

Reshaping & stacking arrays

3.2 Array Operations

Element-wise operations

Broadcasting rules

Universal functions (ufuncs)

Aggregation (sum, mean, std, min, max)

3.3 Linear Algebra

Matrix multiplication (dot, @)

Transpose & inverse

Eigenvalues & eigenvectors

Solving linear equations

Assignment: NumPy Exercises

4Pandas - Data Manipulation

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4.1 Series & DataFrames

Creating Series & DataFrames

Selecting data: loc, iloc, boolean indexing

DataFrame attributes & methods

4.2 Data Loading

Reading CSV, Excel, JSON files

Connecting to SQL databases

Web scraping with read_html()

4.3 Data Cleaning

Handling missing values (fillna, dropna)

Detecting & removing duplicates

Data type conversions

String operations & regex

4.4 Data Transformation

GroupBy aggregations

Merge, join, concat operations

Pivot tables & cross-tabulations

Apply, map, transform functions

Assignment: Data Wrangling Challenge

5Data Visualization

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5.1 Matplotlib Fundamentals

Figure & axes architecture

Line, scatter, bar, histogram plots

Customizing colors, labels, legends

Subplots & figure layouts

5.2 Seaborn Statistical Plots

Distribution plots (histplot, kdeplot, boxplot)

Categorical plots (barplot, countplot, violinplot)

Relationship plots (scatterplot, pairplot, heatmap)

Themes & color palettes

5.3 Interactive Visualizations

Plotly Express quick charts

Interactive hover & zoom

Animated visualizations

Exporting & sharing charts

Assignment: Create a Dashboard

6Statistics for Data Science

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6.1 Descriptive Statistics

Central tendency: mean, median, mode

Dispersion: variance, std, range, IQR

Percentiles & quartiles

Skewness & kurtosis

6.2 Probability Theory

Basic probability rules

Conditional probability & Bayes' theorem

Discrete distributions (binomial, Poisson)

Continuous distributions (normal, uniform)

6.3 Inferential Statistics

Sampling & Central Limit Theorem

Confidence intervals

Hypothesis testing (t-test, z-test)

Chi-square & ANOVA tests

Type I & Type II errors, p-values

6.4 Correlation & Regression

Pearson & Spearman correlation

Simple linear regression

Multiple linear regression

Regression assumptions & diagnostics

Assignment: Statistical Analysis Report

7Exploratory Data Analysis

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7.1 EDA Process & Data Profiling

EDA workflow & objectives

Data quality assessment

Automated profiling with pandas-profiling

Summary statistics interpretation

7.2 Univariate Analysis

Numerical variable distributions

Categorical variable frequencies

Outlier detection methods

Data transformation techniques

7.3 Bivariate & Multivariate Analysis

Numerical vs numerical relationships

Numerical vs categorical relationships

Categorical vs categorical relationships

Correlation matrices & heatmaps

Assignment: Complete EDA Report

8Feature Engineering

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8.1 Feature Creation

Domain knowledge features

Datetime features (day, month, hour)

Mathematical transformations

Binning & discretization

8.2 Categorical Encoding

One-hot encoding

Label encoding & ordinal encoding

Target encoding

Frequency & binary encoding

8.3 Feature Scaling

Min-max normalization

Standardization (z-score)

Robust scaling

When to use which scaler

8.4 Feature Selection

Filter methods (correlation, chi-square)

Wrapper methods (RFE)

Embedded methods (Lasso, tree importance)

Handling multicollinearity

Assignment: Feature Engineering Pipeline

9Machine Learning Fundamentals

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9.1 Introduction to ML

What is Machine Learning?

Supervised vs Unsupervised vs Reinforcement

Train-test split & validation

Bias-variance tradeoff

Overfitting & underfitting

9.2 Regression Algorithms

Simple & Multiple Linear Regression

Polynomial Regression

Ridge & Lasso Regularization

Elastic Net

9.3 Classification Algorithms

Logistic Regression

Decision Trees & Random Forests

Support Vector Machines (SVM)

K-Nearest Neighbors (KNN)

Naive Bayes

9.4 Model Evaluation & Tuning

Regression metrics (MSE, RMSE, MAE, R²)

Classification metrics (accuracy, precision, recall, F1)

Confusion matrix & ROC-AUC

Cross-validation techniques

Hyperparameter tuning (GridSearch, RandomSearch)

Assignment: Build & Evaluate ML Models

10Unsupervised Learning

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10.1 Clustering Algorithms

K-Means clustering

Elbow method & silhouette score

Hierarchical clustering (agglomerative)

DBSCAN for density-based clustering

Cluster evaluation & interpretation

10.2 Dimensionality Reduction

Principal Component Analysis (PCA)

Explained variance & scree plot

t-SNE for visualization

UMAP for high-dimensional data

Assignment: Customer Segmentation Analysis

11Real-World Projects & Final Assessment

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Project 1: Sales Analysis Dashboard

Data cleaning & preparation

KPI calculation & trend analysis

Interactive dashboard with Plotly

Project 2: Customer Segmentation

RFM analysis

K-Means clustering implementation

Segment profiling & recommendations

Project 3: House Price Prediction

Feature engineering for real estate

Multiple regression models comparison

Model interpretation & feature importance

Project 4: Sentiment Analysis

Text preprocessing & tokenization

TF-IDF vectorization

Classification model for sentiment

Project 5: Time Series Forecasting

Trend & seasonality decomposition

ARIMA/Prophet modeling

Forecast evaluation

Project 6: End-to-End ML Pipeline

Scikit-learn Pipeline creation

Model serialization (joblib/pickle)

Simple API deployment

Final Capstone Project

What You'll Learn

Master Python programming fundamentals for data science
Work with NumPy, Pandas for data manipulation
Create compelling visualizations with Matplotlib & Seaborn
Apply statistical methods and hypothesis testing
Build ML models with Scikit-learn & TensorFlow
Complete 6 real-world industry projects

Quick Navigation

1. Introduction 2. Python Fundamentals 3. NumPy 4. Pandas 5. Visualization 6. Statistics 7. EDA 8. Feature Engineering 9. ML Fundamentals 10. Unsupervised 11. Projects & Final

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