IT · Data and Analytics
Data Science and Business Analytics Internship
Turn business questions into trustworthy analysis, dashboards and evidence-based recommendations.
A practical analytics internship spanning data preparation, statistical reasoning, SQL, visualization and a stakeholder-ready decision project.
Program overview
Eligible branches: Computer Science and Engineering, Information Technology, Industrial and business-related engineering, Other branches interested in evidence-based analysis
Target learner level: Learners with basic spreadsheet familiarity who want to build practical analysis and reporting skills.
Prerequisites: Basic spreadsheet familiarity · No prior analytics job experience required
Technologies and tools
SQL · Python notebooks · spreadsheets · descriptive statistics · data visualisation · dashboard design
Practical assignments
- Write SQL queries to inspect and reconcile a small dataset
- Build a reproducible analysis and visualisation from a business question
- Create a dashboard with documented metric definitions
Capstone project
Analyse a scoped business question and present a transparent dashboard plus a decision brief.
Expected deliverables
- Documented SQL queries and data-quality checks
- Reproducible analysis notebook or workbook
- Dashboard and metric definitions
- Decision brief with assumptions and caveats
- Stakeholder presentation
Learning outcomes
- Query and clean relational data
- Build clear analytical visualizations and dashboards
- Communicate recommendations with assumptions and caveats
Week-by-week curriculum
The program follows a 13-week core pathway. Weeks 14–15 are optional advanced work when the project scope calls for it.
Weeks 1–2
Orientation, fundamentals and setup
- Orientation, business-question framing, data ethics and tool setup
- Spreadsheet analysis, SQL environment, Python notebooks and descriptive statistics
Weeks 3–5
Core concepts and guided learning
- SQL filtering, joins, aggregation, subqueries and data quality checks
- Data cleaning, missingness, outliers and exploratory data analysis
- Chart selection, dashboard principles and communicating uncertainty
Weeks 6–8
Intermediate concepts and practical exercises
- Hypothesis testing, confidence intervals, sampling and experiment basics
- Cohort, funnel, retention and segmentation analysis
- Build a dashboard with documented definitions and reproducible queries
Weeks 9–11
Advanced topics, specialist tools and project development
- Forecasting and regression for business questions; assumptions and validation
- Metric governance, privacy-aware analysis and stakeholder storytelling
- Develop a capstone analysis from a scoped business case to recommendations
Weeks 12–13
Capstone completion, testing, documentation and evaluation
- Complete analysis, dashboard and concise decision brief
- Validate calculations, reconcile metrics and document data lineage
- Present insights, limitations and recommended next actions
Weeks 14–15 · Optional
Advanced extension and final presentation
- Add scenario analysis, a forecast or an automated reporting workflow
- Refine dashboard usability and deliver a stakeholder presentation
Frequently asked questions
Is prior experience required?
The prerequisites listed on this page describe helpful preparation. The opening weeks include a guided refresher; learners should be ready to practise consistently and complete project work.
Does the program require a business background?
No business role experience is required. The guided work introduces question framing, metric definitions and stakeholder communication.