Module 3: Exploratory Data Analysis

Learn to explore, visualize, and interpret data to uncover patterns, detect anomalies, understand relationships, and generate actionable business insights.

Topics Covered:
Data Profiling & Quality Checks • Missing Values & Outliers • Univariate, Bivariate & Multivariate Analysis • Correlation & Distribution Analysis • Data Visualization • Statistical Plots & Heatmaps • Business Dashboards • Data Storytelling • EDA Case Studies

Module 4: Advanced Regression & Predictive Modeling

Learn how to build, interpret, and evaluate regression models to predict outcomes and solve real-world business problems.

Topics Covered:
Linear & Logistic Regression • Regression Assumptions • Feature Selection • Multicollinearity • Model Interpretation • Residual Analysis • Model Diagnostics • AIC & BIC • Adjusted R² • Model Comparison & Evaluation

Module 5: Advanced Machine Learning

Build practical machine learning models and learn how to select, tune, evaluate, and optimize models for real-world applications.

Topics Covered:
Supervised & Unsupervised Learning • Classification & Regression • Feature Engineering & Selection • Model Evaluation • Cross-Validation • Hyperparameter Tuning • Ensemble Learning • Random Forest • Gradient Boosting • XGBoost • Model Interpretability • Imbalanced Data • Performance Optimization

Discover hidden patterns, groups, and relationships in data without predefined labels.

Topics Covered:
K-Means Clustering • Hierarchical Clustering • Clustering Evaluation • Customer Segmentation • PCA • Dimensionality Reduction • Feature Compression • High-Dimensional Data Visualization

Module 6: Unsupervised Learning & Dimensionality Reduction

Module 7: Resampling, Simulation & Advanced Statistics

Learn advanced statistical techniques to measure uncertainty, validate models, and simulate real-world scenarios.

Topics Covered:
Bootstrap & Jackknife • Monte Carlo Simulation • Random Number Generation • Permutation Tests • Maximum Likelihood Estimation • EM Algorithm • Statistical Simulation • Model Uncertainty • Practical Case Studies

Module 8: Time Series & Forecasting

Learn to analyze time-dependent data and build forecasting solutions for business and operational decision-making.

Topics Covered:
Time Series Fundamentals • Trend & Seasonality • Cyclic Patterns • Stationarity • Autocorrelation • Moving Averages • Forecasting • Model Evaluation • Sales & Demand Forecasting

Module 9: Natural Language Processing (NLP)

Learn how to apply Data Science and Machine Learning techniques to text and language-based data.

Topics Covered:
NLP Fundamentals • Text Preprocessing • Tokenization • Stop Words • Stemming & Lemmatization • Bag of Words • TF-IDF • Text Classification • Sentiment Analysis • Text Similarity • Named Entity Recognition • NLP Projects

Understand the foundations of neural networks and modern deep learning architectures used in AI applications.

Topics Covered:
Neural Networks • Artificial Neurons & Perceptron • Activation Functions • Forward & Backpropagation • Loss Functions • Optimization • Overfitting & Regularization • CNN • RNN • Transformers

Module 10: Deep Learning Fundamentals

Module 11: Generative AI Fundamentals

Understand how modern AI systems generate text, images, code, and other forms of content, and explore their applications in Data Science.

Topics Covered:
Generative AI Fundamentals • Traditional AI vs Generative AI • Foundation Models & LLMs • Tokens & Context Windows • Inference & Model Parameters • AI Limitations & Hallucinations • Responsible AI • Gen AI Applications in Data Science

Module 12: Prompt Engineering

Learn to communicate effectively with Large Language Models and design prompts that produce accurate, consistent, and useful results.

Topics Covered:
Prompt Engineering • Zero-Shot & Few-Shot Prompting • Role & Instruction Prompting • Structured Prompts • Context Engineering • Prompt Templates • Output Formatting & JSON • Prompt Evaluation • Improving AI Responses • Common Prompting Mistakes

Module 13: LLMs & AI Application Development

Go beyond AI tools and learn how to build applications powered by Large Language Models and APIs.

Topics Covered:
LLM Ecosystem • Open-Source & Commercial LLMs • LLM APIs • Model Selection • System & User Prompts • Context Management • Structured Outputs • Function & Tool Calling • LLM Application Architecture • AI-Powered Applications

Understand how AI applications search, retrieve, and compare information based on semantic meaning.

Topics Covered:
Embeddings • Semantic Similarity • Vector Representations • Vector & Similarity Search • Chunking Strategies • Metadata • Vector Databases • ChromaDB • FAISS • Vector Database Technologies

Module 14: Embeddings & Vector Databases

Learn Report Automation and End to End Production Deployment

Learn Production Deployment from scratch

Master the complete analytics lifecycle. We go far beyond basic chart building to teach you professional Report Automation and End-to-End Production Deployment. You will learn to orchestrate fully automated update schedules, configure intelligent threshold alerts, and deploy production-ready dashboards straight to enterprise environments. Turn raw data into self-sustaining analytical systems.

Learning Path

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