🗺️ Data Science Curriculum: Phase-by-Phase

Phase 1: Foundations & Tooling

Week-1. Introduction to Data Science

Workflow, Industry Applications

Week-2. Environment Setup

Python, Jupyter Notebooks, Git, VS Code

Week-3. Python Programming Basics

Data Types, Loops, Functions, OOP

Phase 2: Data Wrangling & Database Management

Week-4. Data Handling

Pandas DataFrames, Series, NumPy Arrays

Week-5. SQL for Data Science

Joins, Aggregations, Subqueries, Window Functions

Week-6. Data Cleaning & Preprocessing

Missing Values, Outliers, Feature Scaling

Phase 3: Exploratory Data Analysis & Mathematics

Week-7. Data Visualization

Matplotlib, Seaborn, Interactive Dashboards

Week-8. Descriptive Statistics

Mean, Median, Variance, Std Dev, Correlation

Week-9. Inferential Statistics

Hypothesis Testing, p-values, Confidence Intervals

Week-10. Probability Distributions

Normal, Poisson, Binomial, Bayesian Analysis

Phase 4: Core Machine Learning

Week-11. Supervised Learning (Regression)

Linear, Ridge, Lasso, Model Evaluation

Week-12. Supervised Learning (Classification)

Logistic Regression, Decision Trees, SVM, Random Forest

Week-13. Unsupervised Learning

K-Means Clustering, PCA, Hierarchical Clustering

Phase 5: Advanced AI & Big Data

Week-14. Intro to Neural Networks

Perceptron, Activation Functions, Backpropagation

Week-15. Deep Learning

TensorFlow & PyTorch, ANNs, CNNs, RNNs

Week-16. Natural Language Processing

Fine-Tuning, Deployment, Optimization

Week-17. Big Data & Distributed Computing

Spark, Hadoop, Data Pipelines

Phase 6: Production & Capstone

Week-18. MLOps & Deployment

Docker, Flask/FastAPI, AWS/GCP

Week-19. Capstone Part 1

Data Collection, Cleaning, EDA

Week-20. Capstone Part 2

Model Training, Deployment, Business Insights