Program Overview
What makes this track serious and market-ready
This program is designed for learners who want more than superficial tool demos. It moves from business problem framing into Excel, SQL, dashboarding, Python workflows, and applied AI usage so that learners can explain what they built, why it matters, and how it supports hiring conversations.
Strong analytics fundamentals before advanced tooling
Dashboard and reporting discipline with business context
Applied AI workflows without hype-heavy shortcuts
Portfolio-ready deliverables reviewed for clarity
Skills and Stack
Tools, workflows, and execution skills you will build
Data cleaning
Business reporting
SQL querying
Dashboard design
Python analysis
Prompt engineering
Stakeholder storytelling
Capstone presentation
Program Syllabus
Detailed modules built for practical depth and role readiness
Review the complete syllabus before enrollment so you can clearly see how the program moves from analytics foundations into dashboards, Python workflows, applied AI usage, and portfolio-ready execution.
- Module-wise progression from business framing to dashboard and analytics delivery
- Hands-on assignments, guided reviews, and practical workflow repetition
- Capstone and presentation practice aligned with analyst and AI-adjacent roles
The syllabus has been rebuilt to feel more competitive and industry-aware. Instead of isolated tools, it now follows the way analysts and applied AI teams actually work: frame the problem, prepare data, build outputs, and communicate decisions.
- How teams define KPIs and reporting questions
- Difference between raw data, information, and decisions
- Data types, quality, and business context
- Analyst thinking for beginners
- Cleaning and structuring messy datasets
- Logical functions, lookups, and pivots
- Operational reporting workflows
- Reusable spreadsheet discipline
- Queries, joins, filtering, and aggregation
- Writing business-ready SQL questions
- Working with normalized datasets
- Debugging common query mistakes
- Data models and KPI selection
- Visual storytelling for stakeholders
- Interactive dashboard design
- Dashboard review and iteration
- Jupyter-based analysis workflow
- Pandas for cleaning and transformation
- Basic automation patterns
- Exploratory analysis with charts
- Descriptive statistics for business use
- Trend analysis and distribution awareness
- Basic experimentation logic
- Common interpretation mistakes to avoid
- Prompt frameworks for analyst productivity
- Using AI to summarize, review, and classify
- Human review and output validation
- AI limitations in business reporting
- End-to-end analytics case build
- Capstone review and narrative polish
- Resume bullets from real work
- Mock presentation for analyst interviews
Career Readiness
Roles, deliverables, and hiring preparation
Target roles
- Data Analyst
- Reporting Analyst
- Business Analyst
- BI Analyst
- AI Operations Associate
Output you build
- Executive dashboard
- SQL assessment workbook
- Python data notebook
- Applied AI workflow case study
- Resume-ready capstone summary