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Data Science Masterclass

Master data analysis, visualization, statistics, and predictive modeling with Python. From fundamentals to advanced techniques with real-world projects.

12 Modules
60+ Hours
150+ Examples
12 Assignments
Python Pandas NumPy Matplotlib Scikit-learn
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Course Outline

1Introduction to Data Science

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1.1 What is Data Science?
Definition and scope
Data Science vs Data Analytics vs ML
Career paths and opportunities
1.2 Data Science Lifecycle
Problem definition
Data collection & cleaning
Modeling and deployment
1.3 Environment Setup
Python & Jupyter installation
VS Code & virtual environments
Assignment: Setup Your Environment

2Python Fundamentals

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2.1 Python Basics
Variables and data types
Control flow & functions
2.2 Data Structures
Lists, tuples, dictionaries
List comprehensions
2.3 File Handling
CSV and JSON handling
Assignment: Python Practice Problems

3NumPy - Numerical Computing

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3.1 NumPy Arrays
Creating and reshaping arrays
Indexing and slicing
3.2 Array Operations
Broadcasting & ufuncs
3.3 Linear Algebra
Matrix operations
Assignment: NumPy Exercises

4Pandas - Data Manipulation

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4.1 Series & DataFrames
Creating DataFrames
loc and iloc indexing
4.2 Data Loading
CSV, Excel, JSON, SQL
4.3 Data Cleaning
Missing values & duplicates
4.4 Data Transformation
GroupBy, merge, pivot
Assignment: Data Wrangling Challenge

5Data Visualization

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5.1 Matplotlib
Line, scatter, bar charts
5.2 Seaborn
Statistical visualizations
5.3 Interactive Charts (Plotly)
Interactive dashboards
Assignment: Create a Dashboard

6Statistics for Data Science

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6.1 Descriptive Statistics
Mean, median, mode, std
6.2 Probability
Probability distributions
6.3 Inferential Statistics
Hypothesis testing
6.4 Correlation & Regression
Linear regression
Assignment: Statistical Analysis

7Exploratory Data Analysis

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7.1 EDA Process
Data profiling
7.2 Univariate Analysis
Distribution analysis
7.3 Bivariate & Multivariate
Correlation analysis
Assignment: Complete EDA Report

8Feature Engineering

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8.1 Feature Creation
Domain features
8.2 Feature Encoding
One-hot, label, target encoding
8.3 Feature Scaling
Standardization & normalization
8.4 Feature Selection
Filter, wrapper, embedded
Assignment: Feature Engineering Pipeline

9Machine Learning Fundamentals

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9.1 ML Concepts
Supervised vs Unsupervised
9.2 Regression Algorithms
Linear, polynomial, Ridge, Lasso
9.3 Classification Algorithms
Logistic, trees, SVM, KNN
9.4 Model Evaluation
Metrics & cross-validation
Assignment: Build ML Models

10Unsupervised Learning

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10.1 Clustering
K-Means, Hierarchical, DBSCAN
10.2 Dimensionality Reduction
PCA and t-SNE
Assignment: Customer Segmentation
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 12. Career
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