PART 1: Data Analytics Foundations
Module 1: MS Excel for Data Analysis
- Core functions, lookups and logic functions (VLOOKUP, IF, IFERROR)
- Data cleaning, conditional formatting, sorting and filtering
- Pivot tables and chart-based visualization
- Lab: Build an interactive Excel dashboard from a raw dataset
Module 2: Python Fundamentals
- Syntax, variables, data types and operators
- Conditional statements and loops
- Functions, recursion and exception handling
- Lab: Build a command-line script with input validation and error handling
Module 3: Python Data Structures
- Lists and tuples: slicing, indexing, comprehension
- Dictionaries and sets
- Lab: Solve data-wrangling problems using core data structures
Module 4: NumPy, Pandas & Data Visualization
- NumPy arrays and Boolean filtering
- Pandas DataFrames and EDA
- Matplotlib and Seaborn for visualization
- Lab: End-to-end exploratory data analysis on a real dataset
Module 5: SQL for Data Analysis
- SELECT, WHERE, ORDER BY and aliases
- Data models and schema design
- Aggregations, GROUP BY and joins
- Lab: Write SQL queries to answer real business questions
Module 6: Statistics & Hypothesis Testing
- Null vs. alternative hypotheses, significance and p-value
- t-tests, correlation and confidence intervals
- z-test, chi-square and ANOVA
- Lab: Full hypothesis-testing workflow on a real dataset
PART 2: Machine Learning & Deep Learning
Module 7: ML Foundations & Regression
- ML types and data preprocessing
- Simple and multiple linear regression
- Evaluation metrics: MAE, MSE, RMSE, R-squared
- Lab: Build and evaluate a regression model on a housing dataset
Module 8: Classification with Logistic Regression & Decision Trees
- Logistic regression and classification metrics
- Decision trees, splitting and pruning
- Random forest, gradient boosting and AdaBoost
- Lab: Compare logistic regression, decision tree and random forest
Module 9: Model Validation & Hyperparameter Tuning
- K-fold cross-validation and grid search
- Precision, recall, F1 and ROC/AUC
- Lab: Tune a model with grid search and cross-validation
Module 10: Clustering & Instance-Based Learning
- K-Means and hierarchical clustering
- SVM: margin and kernel trick
- KNN and distance metrics
- Lab: Cluster with K-Means and hierarchical clustering, classify with SVM and KNN
Module 11: Time-Series Forecasting
- Time-series fundamentals and ARIMA
- Seasonal ARIMA (SARIMA) and tuning
- Lab: Build and evaluate an ARIMA/SARIMA forecasting model
Module 12: Neural Networks & CNNs
- ANN foundations
- Convolutional neural networks
- Lab: Train an ANN and a CNN for image classification
Module 13: Recurrent Networks & Regularization
- RNNs and the vanishing gradient problem
- LSTM and GRU
- Dropout and normalization
- Lab: Train an LSTM/GRU model with regularization
Module 14: NLP Foundations
- Tokenization, stop-word removal, stemming, lemmatization
- Feature extraction
- Lab: Build a text classification model
Module 15: Advanced NLP with Word Embeddings & Sequence Models
- Word embeddings, GloVe and n-grams
- RNN, LSTM and GRU for NLP
- Lab: Apply embeddings and a sequence model to an NLP task
PART 3: Generative AI, Business Intelligence & Career Readiness
Module 16: Power BI for Business Intelligence
- Data transformation and workspace
- Data modeling, relationships and DAX
- Dashboards and publishing
- Lab: Build and publish a Power BI dashboard with DAX measures
Module 17: Generative AI Foundations & Transformers
- GenAI landscape and self-attention
- Transformer architecture and LLMs
- Hugging Face: text generation, summarization, translation
- Lab: Run text generation and summarization with GPT-2 and Flan-T5
Module 18: Fine-Tuning Large Language Models
- Dataset prep and hyperparameter optimization
- Evaluation with BLEU and ROUGE
- Introduction to RAG
- Lab: Fine-tune GPT-2 or Flan-T5 and evaluate with BLEU/ROUGE
Module 19: Advanced Fine-Tuning, PEFT & Chatbot Deployment
- Conditional text generation, PEFT and LoRA
- Text-to-speech and speech-to-text
- Chatbot design and deployment with Gradio
- Lab: Design, build and deploy a chatbot using Gradio
Module 20: End-to-End Capstone Projects
- Project 1: ML pipeline, data to deployment with Streamlit
- Project 2: NLP/deep learning pipeline, data to deployment with Streamlit
- Lab: Deliver two deployed end-to-end projects
Module 21: Career Enhancement & Soft Skills
- Email etiquette and soft skills
- Interview prep and LinkedIn profile building
- Lab: Mock interview with one-on-one feedback
