Week-1. Introduction to Data Science

What is Data Science, Industry Applications, Data Science Workflow

Week-2.Setting Up the Environment

Python Installation, Jupyter Notebooks, Git, VS Code

Week-3. Python Basics

Data Types, Loops, Functions, OOP Concepts

Week-4. Data Handling with Pandas & NumPy

DataFrames, Series, NumPy Arrays, Data Manipulation

Week-5. SQL for Data Science

Joins, Aggregations, Subqueries, Window Functions

Week-6.Data Cleaning & Preprocessing

Handling Missing Values, Outliers, Feature Scaling

Week-7. Data Visualization

Matplotlib, Seaborn, Interactive Dashboards

Week-8. Descriptive Statistics

Mean, Median, Variance, Standard Deviation, Correlation

Week-9.Inferential Statistics

Hypothesis Testing, p-values, Confidence Intervals

Week-10. Probability Distributions

Normal, Poisson, Binomial, Bayesian Analysis

Week-11. Supervised Learning - Regression

Linear Regression, 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

Week-14. Introduction to Neural Networks

Perceptron, Activation Functions, Backpropagation

Week-15.Deep Learning with TensorFlow & PyTorch

ANNs, CNNs, RNNs

Week-16. Natural Language Processing (NLP)

Fine-Tuning, Deployment, Optimization

Week-17. Big Data & Spark

Hadoop, PySpark, Data Pipelines

Week-18.MLOps & Model Deployment

Docker, Flask/FastAPI, Cloud Deployment (AWS/GCP)

Week-19. Capstone Project – Part 1

Data Collection, Cleaning, EDA

Week-20. Capstone Project – Part 2

Model Training, Deployment, Business Insights