From Excel User → Data Analyst → AI-Enabled Business Professional
An industry-oriented Python course designed for business students to build analytics, automation, visualization and AI-assisted coding skills through real-world Indian datasets and portfolio-ready projects.
Python is now an essential skill in business analytics. This course helps BBA students learn Python fundamentals, work with real datasets, create dashboards, automate repetitive tasks, use AI coding tools responsibly, and build 10 practical notebooks that can be shown on a resume or GitHub.
Many graduates mention Python on their resumes but lack hands-on confidence. This course closes that gap through real business examples, weekly lab outputs and portfolio-ready artefacts.
Students use GitHub Copilot, Cursor and Claude as professional tools while learning to verify, debug and question AI-generated code.
By the end, students can clean data, analyze information, create visualizations and generate business insights independently using Python.
No prior programming knowledge is assumed. The course follows a step-by-step, hands-on approach that enables students from commerce, management, arts, and science backgrounds to confidently learn Python and apply it to business problems.
Every concept is demonstrated live by the faculty. Students code simultaneously in the lab and immediately practice the concepts.
Learn by DoingStudents work in pairs and solve coding problems together. Peer learning reduces fear of programming and increases confidence.
Peer LearningAdditional tutorials and practice exercises are provided for students who are completely new to programming.
Concepts are taught using examples from finance, marketing, sales, operations, stock markets, and business analytics instead of abstract computer science problems.
Business Analytics Finance MarketingThe course is designed assuming that many students have never written code before. By combining live demonstrations, guided practicals, pair programming, self-paced resources, and continuous practice, every student can build industry-ready Python skills by the end of the trimester.
Click any outcome card during presentation to show details, BTL level and PO/PSO mapping.
Each module connects coding concepts with business use cases, tools, AI integration and strategic insight.
Every case has explicit deliverables and evaluation criteria. Click to open each case.
Teams of 3 choose one Indian business question and build a complete Streamlit app, GitHub repo, 5-page write-up and 8-minute demo presentation.
One experiment per week. Every lab creates a working artefact.
Faculty live-codes while students follow on machines. Students predict outputs, debug errors together and submit a variation to LMS.
10 sessionsLive codingDebuggingDriver-navigator pairs solve 60-minute challenges. Roles rotate every 20 minutes and selected teams demo at the end.
Weeks 3, 6, 9, 11CollaborationCode reviewWeek-wise breakdown showing how topics, case studies, capstone milestones, practicals, ALMs and assessments synchronise across the trimester.
| Week | Topics | Case | Capstone | Practical | ALM | Assessment |
|---|---|---|---|---|---|---|
| Week 1 | Module 1 — Python install, syntax, variables, types | Razorpay CSV case introduced; students read the schema | Team formation with 3 students each; problem brainstorming | Practical 1: Anaconda + Jupyter setup; CAGR calculator | ALM-12 in lab: live setup demo | Quiz: Python types + syntax, 5 marks, formative |
| Week 2 | Module 1 — Control flow, lists, dictionaries, list comprehensions, functions | — | Capstone problem chosen and scoped | Practical 2: Lists/dicts to automate Excel task | ALM-12: lists + comprehensions live | — |
| Week 3 | Module 2 — Pandas Series + DataFrame; selection; filtering | Razorpay CSV case begins | Data sources identified | Practical 3: First Pandas DataFrame cleaning | ALM-13 Pair Sprint: clean a 1K-row CSV | — |
| Week 4 | Module 2 — GroupBy, merge, pivot, missing-data handling | Razorpay CSV submission due | Data ingestion + initial cleaning complete | Practical 4: Pandas groupby + merge | ALM-12: groupby + merge live | Razorpay case submission, 25 marks |
| Week 5 | Module 3 — matplotlib fundamentals; chart-choice principles | NSE stock-dashboard case introduced | Cleaning complete; initial exploration starts | Practical 5: matplotlib recreate-the-chart drill | — | — |
| Week 6 | Module 3 — seaborn + Plotly Express; interactive dashboards | NSE case submission due | Core analysis begins; first chart drafted | Practical 6: Plotly stock dashboard | ALM-13 Pair Sprint: build a chart-suite for a CSV | NSE dashboard case, 25 marks; Mid-Term Exam, 40 marks: CO1, CO2, CO3 |
| Week 7 | Module 4 — EDA mindset, univariate + bivariate exploration | Onion EDA case introduced | Analysis 50% complete | Practical 7: Onion data EDA walkthrough | ALM-12: EDA live | — |
| Week 8 | Module 4 — Multivariate insights, time-series EDA, data-quality audit | Onion EDA case submission | Streamlit MVP starts | Practical 8: AI-assisted code drill with Copilot | — | Onion EDA case, 30 marks |
| Week 9 | Module 5 — AI coding assistants: prompting, verification, anti-patterns | AI Audit case introduced | Streamlit MVP locally working | Practical 9: API call to fetch RBI / NSE data | ALM-13 Pair Sprint: AI-assisted analytical task | — |
| Week 10 | Module 5 — AI for refactoring, testing, documentation | AI Audit submission due | Capstone deployed to Streamlit Cloud | Practical 10: First Streamlit app | ALM-12: deploy live to Streamlit Cloud | AI Audit case, 20 marks |
| Week 11 | Module 6 — End-to-end workflows, APIs, reproducibility | Mutual Fund Dashboard case introduced | Capstone user-tested + iterated | Capstone polish in lab | ALM-13 Pair Sprint: end-to-end pipeline | — |
| Week 12 | Module 6 — Streamlit, GitHub, portfolio polish | Mutual Fund Dashboard submission | Final capstone pitch to faculty | Capstone presentations | — | Mutual Fund case, 30 marks; End-Term Exam, 75 marks: all 6 COs |
Tip: use horizontal scrolling on smaller screens to view all columns.
Programming is learned by building and shipping code. The evaluation combines practical artefacts, applied examinations, and an external industry-style coding benchmark. Portfolio quality and practical problem-solving are emphasised throughout the course.
| Component | Weight | Marks | What it evaluates |
|---|---|---|---|
| Mid-Term Exam | 15% | 40 | CO1, CO2, CO3 via 6 short-answer questions and 1 detailed code-comprehension case in Week 6. |
| End-Term Exam | 25% | 75 | All 6 COs via 9 short-answer questions and 2 detailed code cases in Week 12. |
| 5 Case Study Submissions | 15% | 130 | CO1–CO6 application across the trimester using real-world business datasets. |
| 10 Practical Experiments | 10% | 100 | Tool fluency, coding confidence, debugging ability, and code quality. |
| Portfolio-Capstone Project | 15% | 60 | End-to-end integration of all 6 COs on one real business analytics problem. |
| Third-Party AI-Based Coding Assessment | 20% | 100 | Industry-standard adaptive coding assessment covering Python fundamentals, Pandas manipulation tasks, chart-generation tasks, debugging exercises, and AI-assisted code-review exercises. Adaptive difficulty produces national and global percentile ranks, calibrated against entry-level analyst tests at Fractal, Tiger Analytics, ZS Associates, and Mu Sigma. |
No mandatory global certification is mapped for D1 as per the Term-1 policy. Python fluency is established in this course. Motivated students may independently pursue PCEP after the Term-1 break, but it is not mandated.
Students must secure 40% in each End-Sem component and 40% aggregate to successfully complete the course.
KLBS Y26 D1 combines global Python standards with Indian business cases, AI coding-assistant integration and placement-ready portfolios.
The course is built around tools and workflows used by analytics teams and entry-level analyst roles.
Covers CO1, CO2 and CO3.
Part A: 6 short/code questions × 3 marks = 18
Part B: 1 code case study = 22
Covers all 6 Course Outcomes.
Part A: 9 short/code questions = 27
Part B: EDA case = 24
Part C: Streamlit/workflow case = 24
Foundation for business programming, Pandas, dashboards, APIs and AI-assisted coding.
Data Engineering and SQL builds on data manipulation taught here.
Machine Learning uses Pandas, NumPy and scikit-learn foundations established here.
Generative AI and LLMs assumes Python fluency and API comfort.
Business Analytics uses Pandas and visualization as core skills.
Every professional elective track assumes basic Python literacy.
Reference books and online courses for revision and advanced self-learning.