Business Analytics & AI
Course Objective
This course introduces students to the core concepts of business analytics and artificial intelligence using Python and modern BI tools. It focuses on solving real business problems using data analysis, machine learning models, and strategic decision-making frameworks.
Learning Outcomes
- Develop digital and data fluency using Python and BI tools
- Build and evaluate ML models to solve business problems
- Communicate insights using visual and strategic storytelling
Business Analytics & AI Syllabus T30
| Session No. | Topics | Tool/Reading/Activity | Skill Focus |
|---|---|---|---|
| 1 | Introduction to Business Analytics | Course Slides | Strategic Thinking |
| 2 | Data Science Lifecycle | CRISP-DM Model | Data Strategy |
| 3 | Data Types & Sources | Structured vs Unstructured Data | Data Awareness |
| 4 | Descriptive Analytics | Excel / Power BI | Insight Generation |
| 5 | Data Visualization | Tableau, matplotlib | Visual Communication |
| 6 | Introduction to Python | Python & Jupyter Setup | Digital Fluency |
| 7 | Exploratory Data Analysis | pandas, seaborn | Pattern Recognition |
| 8 | AI vs ML vs DL | ChatGPT, Midjourney Demo | Technology Differentiation |
| 9 | Supervised Learning | scikit-learn | Predictive Thinking |
| 10 | Linear Regression | LinearModel in Python | Forecasting |
| 11 | Classification Models | Logistic, Decision Trees | Classification Strategy |
| 12 | Unsupervised Learning | k-means Clustering | Segmentation |
| 13 | Model Evaluation | Confusion Matrix, AUC | Model Assessment |
| 14 | Data Ethics & Governance | Class Discussion | Responsible AI |
| 15 | Forecasting Basics | Time Series with statsmodels | Temporal Analysis |
| 16 | Text Analytics | NLTK, spaCy | Sentiment Analysis |
| 17 | Decision Trees & Random Forest | sklearn, RandomForest | Accuracy Boosting |
| 18 | BI Tools Overview | Power BI / Tableau | Executive Dashboards |
| 19 | Capstone Project Briefing | Team Project Prep | Solution Design |
| 20 | Project Presentations | Peer Review | Strategic Communication |
Textbook & Resources
Primary Tools & Libraries:
- Python, Jupyter, pandas, matplotlib, seaborn, scikit-learn, Power BI, Tableau
Recommended Books:
- Data Science for Business – Provost & Fawcett
- Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow – Aurélien Géron