← Back to all majors
Major 05

AI + Data & Decision Science

Replaces → Statistics

80% of analytics engineers now use AI daily — up from 30% one year ago. BLS projects 34% growth for data scientists. Learn Power BI Copilot, Tableau AI, dbt, and the modern data stack. Practical and tool-oriented, not a traditional stats degree.

Two layers. One analyst who delivers.

Every student completes the Foundations plus domain-specific courses. You don't just learn data — you learn to turn it into decisions with AI.

SQL and Data Warehouse Operations

Production-grade SQL, data warehouse navigation, query optimization, dbt fundamentals. The foundation that 80%+ of data analyst job postings require.

  • Production-grade SQL queries and optimization
  • Data warehouse navigation and architecture
  • Query optimization and performance tuning
  • dbt fundamentals for SQL-based transformation

Data Cleaning and Quality Management

Data profiling, source reconciliation, standardization, deduplication, dbt tests. The unglamorous work that makes every downstream analysis trustworthy.

  • Data profiling and source reconciliation
  • Standardization and deduplication techniques
  • dbt tests and data quality frameworks
  • Building trust in data through systematic validation

Python for Data Analysis

pandas, numpy, data visualization, Jupyter notebooks, AI-assisted code. The programming toolkit that extends what SQL alone can do.

  • pandas and numpy for data manipulation
  • Data visualization with Python libraries
  • Jupyter notebooks for reproducible analysis
  • AI-assisted code generation and debugging

Statistical Analysis and Experimental Design

A/B testing, hypothesis testing, confidence intervals, correlation vs. causation. Enough statistics to design experiments and validate results — applied, not abstract.

  • A/B testing design and analysis
  • Hypothesis testing and confidence intervals
  • Correlation vs. causation in business contexts
  • Experimental design for product and marketing teams

Business Intelligence and Dashboard Design

BI tools, dashboard scoping and design, metric documentation, self-serve analytics. Build dashboards executives actually use — not just charts, but decision-support systems.

  • BI tool proficiency across major platforms
  • Dashboard scoping, design, and iteration
  • Metric documentation and definitions
  • Self-serve analytics for business stakeholders

Data Infrastructure and Architecture

Modern data stack, pipeline monitoring, event tracking, building from zero. The infrastructure that makes reliable analytics possible at scale.

  • Modern data stack architecture and components
  • Pipeline monitoring and alerting
  • Event tracking and instrumentation
  • Building data infrastructure from zero

Revenue and Business Metrics

MRR calculation, unit economics, board deck narratives, forecasting. The business context that turns data analysts into trusted advisors.

  • MRR calculation and subscription metrics
  • Unit economics and business model analysis
  • Board deck narratives and executive reporting
  • Forecasting and scenario modeling

Professional Practice in Data Science

Notebook discipline, code review, reproducibility, stakeholder relationships, career paths. The professional skills that separate analysts who deliver from analysts who just query.

  • Notebook discipline and code review practices
  • Reproducibility and documentation standards
  • Stakeholder relationships and communication
  • Career paths and professional development

Where this major takes you.

AI-skilled data professionals command a 28% salary premium (~$18K/year more). 63% job openings growth YoY for AI data analysts. 51% of AI-requiring job postings are now outside IT — every industry needs this.

Data Analyst

The core path: extract insights, build reports, answer business questions. Entry: $60K–$80K. Mid-career: $80K–$115K. Senior: $115K–$160K+. AI Data Analyst average: $130K (Glassdoor).

AI Uni edge You analyze with ChatGPT, Claude, and Julius AI at 10x speed — and communicate findings with Power BI Copilot dashboards that executives actually use.

BI Analyst / Developer

Build and maintain the dashboards and reporting systems companies run on. Entry: $70K–$92K. Mid-career: $92K–$130K. Senior: $130K–$181K. PL-300 certification requested in ~32% of Power BI postings.

AI Uni edge You build across Power BI, Tableau, and Looker — not just one platform. Plus ThoughtSpot and Sigma Computing for next-gen BI that most programs don't teach.

Analytics Engineer

The fastest-growing data role: SQL + dbt + cloud warehouses. Entry: $100K+. Mid-career: $125K–$153K. Senior: $142K–$216K+. 80%+ ICs over $100K; 49% of managers over $200K.

AI Uni edge You learn dbt, Fivetran, and the modern data stack that most programs haven't caught up to. 80% of analytics engineers already use AI daily — you arrive fluent.

Decision Science Analyst

Go beyond reporting to recommendation. Model scenarios, predict outcomes, advise on strategy. Mid-career: $75K–$156K. BLS projects +21% growth for operations research through 2034.

AI Uni edge DataRobot and AutoML tools let you build predictive models that used to require a data science team. You deliver consulting-level insights as an individual contributor.

What you'll build.

Real analysis projects that demonstrate your ability to turn data into decisions — the output that gets you hired.

Project 01

SQL Analysis Project

SQL analysis project with documented queries and data validation. Demonstrate production-grade query writing, optimization, and systematic data quality checks.

Project 02

Python Data Analysis

Python data analysis with visualization and written insights. End-to-end analysis in Jupyter notebooks with pandas, clear visualizations, and narrative interpretation.

Project 03

Business Intelligence Dashboard

Business intelligence dashboard with metric documentation. A self-serve analytics tool with clear metric definitions, stakeholder-ready design, and documented methodology.

Capstone

End-to-End Data Project

Your capstone: end-to-end data project from raw data to executive presentation. The deliverable that proves you can own the full data lifecycle and turn messy data into strategic recommendations.

The curriculum adapts to you.

Intensive

~6 months · Full-time

For students who can commit full-time. Get career-ready as fast as your skills allow.

Months 1–2 Foundations + SQL and Data Warehouse Operations
Months 3–5 Data Cleaning + Python + Statistical Analysis
Months 6–8 BI + Data Infrastructure + Revenue Metrics
Months 9–12 Professional Practice + Capstone

Steady

~12–24 months · Part-time

For people working while learning. Same depth, flexible scheduling.

Months 1–4 Foundations + SQL and Data Warehouse Operations
Months 5–10 Data Cleaning + Python + Statistical Analysis
Months 11–16 BI + Data Infrastructure + Revenue Metrics
Months 17–24 Professional Practice + Capstone

Is this major right for you?

This is for you if

  • You want to turn data into decisions, not prove theorems
  • You're practical — you'd rather build a Power BI dashboard than write a statistics proof
  • You want a career where AI gives you a 28% salary premium (~$18K/year)
  • You're comfortable with numbers but don't need to be a mathematician
  • You want to be the person in any room who can make sense of the data

This is NOT for you if

  • You want a PhD in statistics or pure mathematics research
  • You want to be an ML engineer building foundational models from scratch → Major 01 + graduate school
  • You need an accredited statistics or data science degree
  • You're interested in theoretical computer science or algorithms research
  • You want deep mathematical rigor over practical application

Ready to start analyzing?

Take the free AI-guided assessment. We'll confirm this major is right for you, build a personalized path, and honestly tell you if a statistics degree is the better choice.

Start Your Assessment
Free · 15 minutes · No credit card