Week 1: Core Python, Data Handling, and Statistics (Days 1–7)

📍 Day 1 – Intro to Data Science

• What is Data Science?
• Lifecycle: Data Collection → Cleaning → Analysis → Modeling → Deployment
• Tools: Jupyter, Python, Git, Excel

📍 Day 2 – Python for Data Science

• Data types, loops, functions
• List, Dict, Tuple, Set
• File handling

📍 Day 3 – Numpy & Pandas Basics

• Arrays and matrix operations (Numpy)
• Series & DataFrame (Pandas)
• Importing CSV, Excel

📍 Day 4 – Data Cleaning & Preprocessing

• Handling missing values
• Duplicates, nulls
• Renaming, replacing, mapping

📍 Day 5 – Exploratory Data Analysis (EDA)

• Descriptive statistics
• Groupby, sorting, filtering
• Hands-on: Titanic dataset

📍 Day 6 – Data Visualization

• Matplotlib, Seaborn
• Plot types: histogram, bar, scatter, box, heatmap
• Hands-on: Correlation analysis

📍 Day 7 – Statistics for Data Science

• Mean, median, mode, std dev, variance
• Probability basics
• Distributions: normal, binomial

Week 2: Advanced Stats, ML Algorithms, and Model Building (Days 8–14)

📍 Day 8 – Inferential Stats & Hypothesis Testing

• Confidence intervals
• t-test, chi-square, ANOVA
• p-value explained

📍 Day 9 – Linear Regression

• Simple and multiple regression
• R², adjusted R²
• Hands-on: House price prediction

📍 Day 10 – Classification: Logistic Regression

• Binary vs multi-class classification
• Sigmoid function
• Evaluation: Confusion matrix, ROC curve

📍 Day 11 – Decision Trees & Random Forest

• Splitting criteria: Gini, Entropy
• Overfitting, pruning
• Hands-on: Loan approval prediction

📍 Day 12 – KNN & Naive Bayes

• Distance metrics in KNN
• Bayes theorem and Gaussian NB
• Hands-on: Email spam detection

📍 Day 13 – Unsupervised Learning

• K-means clustering
• Elbow method
• PCA for dimensionality reduction

📍 Day 14 – Model Evaluation & Tuning

• Cross-validation
• GridSearchCV, RandomSearchCV
• Bias-variance tradeoff

 
Week 3: Projects, Real-World Tools & Career Prep (Days 15–21)

📍 Day 15 – Time Series Analysis

• Date/time handling
• Rolling mean, autocorrelation
• Forecasting with ARIMA (brief)

📍 Day 16 – Natural Language Processing (NLP)

• Text cleaning (tokenize, stopwords, stemming)
• TF-IDF
• Sentiment analysis mini project

📍 Day 17 – SQL for Data Science

• SELECT, WHERE, JOIN, GROUP BY
• Subqueries
• Practice with sample database (e.g., SQLite or MySQL)

📍 Day 18 – Working with Real Datasets

• Kaggle datasets
• End-to-end EDA + model
• Hands-on: Diabetes prediction / Customer churn

📍 Day 19 – Mini Capstone Project

Choose 1:

• Sales prediction
• Fake news detection
• Movie recommendation system
• Smart city traffic analysis

📍 Day 20 – Model Deployment

• Save model with Pickle/Joblib
• Flask/Streamlit web app
• Deploy to Heroku (or local server)

📍 Day 21 – Career in Data Science

• Resume tips, GitHub portfolio
• Data science roles: Analyst, ML engineer, DS
• Certifications, interview prep (case studies, SQL/ML Qs)

🧰 Tools & Libraries:

• Python (Jupyter Notebook)
• Numpy, Pandas, Matplotlib, Seaborn
• Scikit-learn
• SQL (SQLite / MySQL)
• Streamlit or Flask for deployment
• Kaggle for datasets