1. Introduction to Data Science

• What is Data Science?
• Data Scientist roles & skills
• Data Science lifecycle (CRISP-DM, OSEMN)
• Applications across industries
• Overview of tools: Python, SQL, Jupyter, Git, Cloud

2. Python for Data Science (Foundations)

• Python basics: variables, loops, functions
• Data structures: lists, tuples, sets, dictionaries
• File handling
• Virtual environments & package management
• Libraries:
• NumPy (arrays, broadcasting)
• Pandas (Series, DataFrames, joins, groupby)

3. Exploratory Data Analysis (EDA)

• Data cleaning & preprocessing
• Handling missing values, outliers, duplicates
• Feature types (numeric, categorical, datetime, text)
• Visualization:
• Matplotlib, Seaborn, Plotly
• Descriptive statistics
• Correlation analysis
• Data distributions

4. Statistics & Probability for Data Science

• Descriptive vs inferential statistics
• Probability theory
• Random variables, distributions
• Sampling & estimation
• Hypothesis testing (t-test, chi-square, ANOVA)
• Confidence intervals
• Bayesian thinking (optional)

5. SQL for Data Science

• CRUD operations
• Joins, subqueries
• Window functions
• Aggregations
• Working with databases (MySQL / PostgreSQL)
• Query optimization basics

6. Data Wrangling & Feature Engineering

• Encoding categorical variables
• Scaling & normalization
• Binning
• Feature selection techniques
• Feature extraction
• Date-time features
• Handling imbalanced datasets

7. Machine Learning (Supervised Learning)

• ML workflow & model evaluation
• Linear Regression
• Logistic Regression
• Decision Trees & Random Forest
• Gradient Boosting (XGBoost, LightGBM, CatBoost)
• K-Nearest Neighbours
• Support Vector Machines
• Model tuning & cross-validation
• Evaluation metrics (RMSE, R², Precision, Recall, F1)

8. Unsupervised Learning

• K-Means clustering
• Hierarchical clustering
• PCA & dimensionality reduction
• Anomaly detection
• Association rule mining (Apriori)

9. Time Series Analysis

• Components of time series
• Stationarity & ACF/PACF
• ARIMA, SARIMA
• Prophet, LSTM (optional)
• Forecast evaluation

10. Deep Learning

• Neural network basics
• Activation functions
• Loss functions & optimizers
• Backpropagation
• Introduction to TensorFlow / PyTorch
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNN, LSTM, GRU)
• Transfer learning

11. Natural Language Processing (NLP)

• Text preprocessing (tokenization, stemming, lemmatization)
• TF-IDF, bag-of-words
• Word embeddings (Word2Vec, GloVe)
• Transformers & BERT
• Sentiment analysis
• Chatbot basics

12. Big Data & Cloud

• Hadoop ecosystem: HDFS, MapReduce
• Spark (RDD, DataFrames, MLlib)
• Cloud basics (AWS / Azure / GCP)
• Data pipelines & ETL
• Data lakes & data warehouses (Snowflake, BigQuery)

13. MLOps (Machine Learning Operations)

• Model deployment: Flask/FastAPI
• CI/CD for ML
• Docker & containers
• Model monitoring
• Drift detection
• ML platforms (AWS Sagemaker, Azure ML, GCP Vertex AI)

14. Data Visualization & Storytelling

• Dashboard tools: Power BI / Tableau
• Creating effective insights
• Business problem framing
• Presentation techniques for stakeholders

15. Capstone Projects

Examples:

• Customer churn prediction
• Sales forecasting
• Recommendation system
• Fraud detection
• NLP text classifier
• Computer vision object detection

16. Bonus Modules (Optional)

• Generative AI / LLM basics
• Reinforcement learning
• Graph analytics
• Advanced statistics