Course Outline : Future-Ready Data Science and AI for Industry 4.0
🚀 Learn by Doing | Several Real-World Projects
Mode: Online
Starting: Coming soon
Course Fees:
Module 1: Introduction to Data Science and AI for Industry 4.0
Data Science and AI Landscape
AI in Modern Industries (Manufacturing, Finance, Retail, Healthcare)
Data Science Lifecycle
Types of AI: Applied AI, Generative AI
Overview of Tools: Python, SQL, Power BI, TensorFlow
Skill Integration: Students engage in peer discussion sessions to define real-world business problems and present how AI could solve them. This builds clarity in framing and articulation.
Module 2: Python Programming for Data Science and AI
Python Essentials for AI and Data Applications
Data Manipulation using pandas and NumPy
Data Visualization using matplotlib and seaborn
Introduction to Object-Oriented Programming
Version Control using Git and GitHub
Skill Integration: Students document their code professionally and contribute to a shared GitHub repository in groups, simulating real-world team collaboration.
Module 3: Statistics and Probability for Data Science
Descriptive and Inferential Statistics
Probability Distributions
Hypothesis Testing and Statistical Decision-Making
Bayesian Thinking for AI Applications
Skill Integration: Students analyze datasets and write insight reports in plain language. They also present key findings to peers in short “explain-to-a-non-tech” pitch sessions.
Module 4: Data Wrangling and Exploratory Data Analysis (EDA)
Data Cleaning and Preprocessing
Exploratory Data Analysis Techniques
Feature Engineering
Handling Structured and Unstructured Data
Working with Large Datasets
Skill Integration: Each student creates a formal EDA report with business-ready visuals and presents key insights in a 5-minute storytelling challenge.
Module 5: SQL and Power BI for Data Analytics
SQL Queries for Data Preparation and Analysis
Power BI Dashboards and Reports
Business Intelligence Techniques
Introduction to NoSQL Concepts
Skill Integration: Students design a dashboard for a given business case and write an accompanying insights summary as if presenting to a management team.
Module 6: Machine Learning Foundations
Supervised and Unsupervised Learning
Regression and Classification Models
Clustering Techniques
Evaluation Metrics and Model Interpretability
End-to-End ML Pipeline Design
Skill Integration: Students work in teams to solve an open ML problem and present their findings and models in a mini case competition format.
Module 7: Advanced Machine Learning and Model Deployment
Ensemble Learning: Random Forest, XGBoost
Dimensionality Reduction: PCA, t-SNE
Model Deployment Essentials
Basics of MLOps and Model Maintenance
Introduction to Explainable AI (XAI)
Skill Integration: Students write technical documentation for their models and explain trade-offs in accuracy, complexity, and business impact during review sessions.
Module 9: Deep Learning Foundations
Neural Networks and Deep Learning Basics
CNNs, RNNs, LSTMs
Transfer Learning and Pre-trained Models
AI APIs for Vision and Text Applications
Skill Integration: Students explore and present real-world AI use cases (e.g., vision, voice) with ethical considerations in group discussions.
Module 10: Generative AI and AI-Augmented Workflows
Generative AI Overview (Text, Image, Audio Generation)
Applications of GPT, BERT, and Diffusion Models
Prompt Engineering for Generative AI
AI in Decision-Support and Automation
Skill Integration: Students craft and optimize prompts for real-world GenAI tools, and reflect on their use cases in collaborative feedback sessions.
Module 11: MLOps and Responsible AI
Model Deployment using Flask or FastAPI
Monitoring and Maintenance Basics
Introduction to MLOps Lifecycle
Responsible AI: Ethics, Bias, Privacy
AI Governance and Compliance Essentials
Skill Integration: Students simulate post-deployment reporting and participate in an ethics panel to debate responsible AI scenarios.
Module 12: Professional and Career Readiness
Office Etiquette for AI and Data Professionals
Communication Skills: Verbal, Written, Presentations
Resume Building and LinkedIn Optimization
Mock Interviews (Technical and Behavioral)
Personal Branding and Career Planning
Time Management and Team Collaboration
Skill Integration: Students undergo resume reviews, mock interviews, and career visioning exercises with structured templates and live feedback.
Module 13: Capstone Project
Real-World Project based on AI or Data Science
Data Handling, Modeling, Deployment, and Business Insight
Documentation and Reporting
Final Presentation and Project Showcase
Skill Integration: Students deliver a complete AI/DS solution with a GitHub portfolio, business report, and live presentation simulating a client meeting.