This repository is a complete step-by-step roadmap to master Python for Data Science, starting from basic programming concepts and moving towards advanced Machine Learning and Deep Learning.
It is built to:
- Strengthen Python fundamentals
- Practice Data Science tools hands-on
- Apply concepts to real-world use cases
- Showcase structured learning and consistency
- Master Python from basic to advanced
- Learn how Python is used in real Data Science workflows
- Build a strong portfolio-ready foundation
- Prepare for interviews, projects, and competitions
- One-stop structured learning resource
- Daily practice & revision guide
- Demonstrate discipline and growth in Data Science
This repository follows a progressive learning strategy:
- Understand Concepts
- Practice with Code
- Apply to Real Data
- Build Projects
Each topic includes:
- Concept explanation
- Python implementation
- Practical relevance
What to Learn
- Variables & Data Types
- Input / Output
- Type Casting
- Operators
Objective
- Build logical thinking
- Understand Python syntax clearly
Real-World Use
- Writing automation scripts
- Data preprocessing logic
What to Learn
- List
- Tuple
- Set
- Dictionary
Objective
- Efficient data storage & access
Real-World Use
- Feature storage
- Dataset manipulation
What to Learn
- if / else
- for & while loops
- break & continue
- Comprehensions
Objective
- Control program execution
Real-World Use
- Conditional data filtering
- Iterative processing
What to Learn
- Functions
- Lambda
- Modules & Packages
Objective
- Reusable & clean code
Real-World Use
- Data pipelines
- Utility functions
What to Learn
- Classes & Objects
- Inheritance
- Polymorphism
- Encapsulation
Objective
- Scalable project design
Real-World Use
- ML pipelines
- Custom dataset classes
What to Learn
- Arrays
- Indexing & slicing
- Broadcasting
- Vector operations
Objective
- Fast numerical computation
Real-World Use
- Mathematical modeling
- Feature transformation
What to Learn
- Series & DataFrame
- Filtering & sorting
- GroupBy
- Merge & Join
Objective
- Data analysis mastery
Real-World Use
- Exploratory Data Analysis (EDA)
- Business insights
What to Learn
- Missing value handling
- Outlier detection
- Encoding
- Scaling
Objective
- Prepare high-quality data
Real-World Use
- Industry datasets
- ML-ready data
What to Learn
- Matplotlib
- Seaborn
- Plot types
Objective
- Visual storytelling
Real-World Use
- Dashboards
- Reports & presentations
What to Learn
- Mean, Median, Mode
- Variance & Std Dev
- Probability
- Hypothesis Testing
Objective
- Analytical thinking
Real-World Use
- A/B testing
- Model assumptions
What to Learn
- Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Unsupervised Learning
- Clustering
- PCA
Objective
- Predictive modeling
Real-World Use
- Sales prediction
- Customer segmentation
What to Learn
- Train-Test Split
- Cross Validation
- Metrics
Objective
- Reliable model evaluation
Real-World Use
- Model comparison
- Performance optimization
What to Learn
- Artificial Neural Networks
- CNN
- RNN
Objective
- Advanced AI skills
Real-World Use
- Image classification
- NLP & time series
What to Build
- EDA Projects
- ML Projects
- DL Projects
Objective
- Hands-on experience
- Portfolio building
✔ Covers complete Python Data Science roadmap
✔ Beginner-friendly → Advanced level
✔ Focus on practice + application
✔ Ideal for students, job seekers & professionals
flowchart TD
%% ========== PYTHON CORE ==========
A["🐍 Python Basics<br/>int, float, str<br/>print(), input()"] --> B["📦 Data Structures<br/>list, tuple, set, dict"]
B --> C["🔁 Control Flow<br/>if-else, for, while"]
C --> D["🧩 Functions<br/>def, lambda"]
D --> E["🧱 OOP<br/>class, object"]
%% ========== DATA HANDLING ==========
E --> F["📊 NumPy<br/>arrays, reshape"]
F --> G["🗃 Pandas<br/>DataFrame, Series"]
G --> H["🧹 Data Cleaning<br/>NaN, duplicates"]
%% ========== VISUALIZATION ==========
H --> I["📈 Visualization<br/>bar, line, scatter"]
%% ========== STATISTICS ==========
I --> J["🧮 Statistics<br/>mean, std, probability"]
%% ========== MACHINE LEARNING ==========
J --> K["🤖 Machine Learning<br/>Regression, Classification"]
K --> L["⚙️ Evaluation<br/>accuracy, confusion matrix"]
%% ========== DEEP LEARNING ==========
L --> M["🧠 Deep Learning<br/>ANN, CNN, RNN"]
%% ========== PROJECTS ==========
M --> N["🚀 Projects<br/>EDA, ML, DL"]
%% ========== DARK UI STYLES ==========
classDef core fill:#0D47A1,color:#ffffff,stroke:#42A5F5,stroke-width:3px;
classDef data fill:#1B5E20,color:#ffffff,stroke:#66BB6A,stroke-width:3px;
classDef viz fill:#E65100,color:#ffffff,stroke:#FFB74D,stroke-width:3px;
classDef stats fill:#4A148C,color:#ffffff,stroke:#BA68C8,stroke-width:3px;
classDef ml fill:#B71C1C,color:#ffffff,stroke:#EF5350,stroke-width:3px;
classDef dl fill:#880E4F,color:#ffffff,stroke:#F06292,stroke-width:3px;
classDef proj fill:#3E2723,color:#ffffff,stroke:#A1887F,stroke-width:3px;
class A,B,C,D,E core
class F,G,H data
class I viz
class J stats
class K,L ml
class M dl
class N proj
