- Introduce students to Generative AI concepts, tools, and applications across different domains.
- Provide hands-on experience with real AI platforms (ChatGPT, Gemini, Copilot, DALL·E, Midjourney, etc.).
- Enhance problem-solving and creativity using AI-assisted workflows.
- Build confidence in integrating AI into academic and professional work, including projects and documentation.
What you'll learn
Day 1 – Introduction to Data Science & Python for Data
• What is Data Science?
o Data, Information, and Insights
o Data Science vs. Traditional Programming
o Real-life applications of Data Science & ML
• Data Science Workflow / Life Cycle (CRISP-DM style overview)
• Introduction to Python for Data Science
o Python basics: variables, data types, conditionals, loops
o Functions and basic input/output
• Introduction to Jupyter Notebook / Google Colab environment
• Hands-on:
o Writing basic Python programs
o Simple data operations and small exercises
Day 2 – Working with Data: NumPy, Pandas & Exploratory Data Analysis (5 hours)
• Introduction to NumPy
o Arrays, basic operations, vectorization
• Introduction to Pandas
o Series, Data Frames
o Importing data (CSV/Excel)
o Selecting, filtering, sorting, grouping data
• Data Cleaning & Preprocessing
o Handling missing values
o Handling duplicates
o Basic feature selection / dropping columns
• Exploratory Data Analysis (EDA)
o Descriptive statistics (mean, median, mode, variance, std dev)
o Correlation and relationships between variables
• Basic Data Visualization
o Using Matplotlib / Seaborn for plots (histogram, bar plot, scatter plot, box plot)
• Hands-on:
o EDA on a real dataset (e.g., sales, students, or simple public dataset)
Day 3 – Statistics for Data Science & Introduction to Machine Learning (5 hours)
• Quick Review of Essential Statistics
o Types of data: categorical vs numerical
o Probability basics (very brief)
o Distributions (normal, skewness idea)
o Outliers and their impact
• Introduction to Machine Learning
o Types of ML: Supervised vs Unsupervised vs Reinforcement (conceptual)
o Terminology: features, labels, training data, testing data, overfitting, underfitting
• Supervised Learning – Regression
o Concept of regression and prediction
o Linear Regression theory (simple & multiple)
o Performance metrics: MAE, MSE, RMSE, R² (basic understanding)
• Hands-on:
o Build a simple linear regression model in Scikit-learn
o Train-test split and evaluation
Day 4 – Supervised Learning: Classification Algorithms (5 hours)
• Recap of Training/Testing, Model Building Steps
• Classification Problems & Use Cases
o Spam detection, disease prediction, churn prediction, etc.
• Algorithms:
o Logistic Regression (for binary classification)
o Decision Trees (concepts and basic intuition)
o Optionally: Random Forest / KNN (overview)
• Model Evaluation Metrics for Classification
o Accuracy, Precision, Recall, F1-score
o Confusion matrix basics
• Handling Imbalanced Data (basic concept only)
• Hands-on:
o Build a classification model (e.g., predict pass/fail, churn, or simple dataset)
o Evaluate model performance, interpret results
Day 5 – Unsupervised Learning, Projects & Career Guidance (5 hours)
• Unsupervised Learning
o Concept & examples (customer segmentation, grouping, anomaly detection)
o K-Means Clustering – idea and implementation
o Visualizing clusters
• End-to-End Mini Project
o Choose a dataset (e.g., students’ performance, sales dataset, social media data, etc.)
o Steps:
Problem definition
Data loading and cleaning
EDA and visualization
Model building (regression/classification or clustering)
Evaluation and conclusion
• Documentation & Presentation of Results
o How to present a data science project (slides / report / GitHub)
• Career Path & Next Steps
o Roles: Data Analyst, Data Scientist, ML Engineer, etc.
o Recommended learning roadmap and resources
• Q&A, Feedback, and Closing Session
- Students will understand the fundamentals of Generative AI, LLMs, image models, and AI tools.
- Students will be able to produce academic content such as reports, presentations, diagrams, project summaries, and documentation.
- Students will build a mini project demonstrating the use of text + image + code generation.
- Students will understand AI ethics, safety, and responsible use.
