Master of Science in Business Analytics · Career Pathway Guide
Use this site as your MSBA career exploration and job-search planning guide, not a one-time “pick a path and be done” assignment. The pathways highlighted here reflect common MSBA outcomes in the WFUSB data, so use them to learn what roles exist, compare options, build questions for your Career Coach, and move gradually from broad exploration to more targeted applications. Your coach may also use this same site with you in 1:1 appointments, workshops, and classes so your target roles, skills, evidence, and search plan stay aligned.
Do not treat the site as a one-time reading assignment. Work through it in a sequence: learn where students land, compare paths, identify employers and job titles, build evidence, and convert what you learn into a focused search plan.
Start with outcomes and pathway pages. Ask: Which functions appear most often? Which pathways match my interests? Which options create questions I should bring to my coach?
Move from broad interest to specific employers and job-title families. Use the site to build a starter list, then validate current openings in 12Twenty, Handshake, LinkedIn, Wake Network, and employer career sites.
Identify the skills your target roles require and connect those skills to class projects, experiential work, certifications, student leadership, internships, and independent practice.
Use your target role, employer list, priority skills, and evidence to create a job-search plan and story you can use in applications, outreach, interviews, and coaching conversations.
| Rank | Function | Count | % of total |
|---|---|---|---|
| 1 | Analytics / Data Analytics | 333 | 54.0% |
| 2 | Consulting | 78 | 12.6% |
| 3 | Finance | 49 | 7.9% |
| — | Other / Unclassified | 49 | 7.9% |
| 4 | Operations / Supply Chain | 31 | 5.0% |
| 5 | Marketing / Sales | 26 | 4.2% |
| 6 | Information Technology | 20 | 3.2% |
| 7 | Accounting | 10 | 1.6% |
| 8 | Education | 6 | 1.0% |
| 9 | General Management | 5 | 0.8% |
| 10 | Logistics | 5 | 0.8% |
| 11 | Advertising/Public Relations | 3 | 0.5% |
| 12 | Finance Analytics | 1 | 0.2% |
| 13 | Human Resources | 1 | 0.2% |
| Function | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | Total |
|---|---|---|---|---|---|---|---|---|---|
| Analytics / Data Analytics | 28 | 56 | 33 | 25 | 67 | 34 | 55 | 35 | 333 |
| Consulting | 8 | 6 | 12 | 10 | 21 | 8 | 8 | 5 | 78 |
| Finance | 2 | 2 | 5 | 2 | 12 | 4 | 14 | 8 | 49 |
| Other / Unclassified | 4 | 2 | 5 | 8 | 9 | 6 | 11 | 4 | 49 |
| Operations / Supply Chain | 3 | 3 | 0 | 4 | 3 | 9 | 5 | 4 | 31 |
| Marketing / Sales | 0 | 4 | 1 | 2 | 5 | 4 | 6 | 4 | 26 |
| Information Technology | 1 | 2 | 0 | 0 | 7 | 2 | 4 | 4 | 20 |
| Accounting | 0 | 0 | 1 | 0 | 2 | 5 | 0 | 2 | 10 |
| Education | 0 | 0 | 0 | 3 | 0 | 1 | 1 | 1 | 6 |
| General Management | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 3 | 5 |
| Logistics | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 5 |
| Advertising/Public Relations | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 3 |
| Finance Analytics | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| Human Resources | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| Total | 47 | 75 | 59 | 55 | 128 | 75 | 105 | 73 | 617 |
| Location | 2023 | 2024 | 2025 | Total | Share |
|---|---|---|---|---|---|
| North Carolina | 25 | 26 | 29 | 80 | 34.0% |
| New York | 3 | 8 | 6 | 17 | 7.2% |
| Ohio | 8 | 6 | 0 | 14 | 6.0% |
| South Carolina | 2 | 6 | 6 | 14 | 6.0% |
| California | 4 | 6 | 2 | 12 | 5.1% |
| Florida | 6 | 4 | 2 | 12 | 5.1% |
| Georgia | 5 | 2 | 4 | 11 | 4.7% |
| Texas | 5 | 4 | 1 | 10 | 4.3% |
| Washington, DC | 2 | 6 | 1 | 9 | 3.8% |
| Illinois | 1 | 4 | 2 | 7 | 3.0% |
| Coded location total | 83 | 87 | 65 | 235 | 100.0% |
| Industry | Adjusted count | % of total | What MSBA grads do there |
|---|---|---|---|
| Financial Services | 159 | 28.8% | Risk, credit, fraud, and customer analytics at banks, lenders, card issuers, and insurers. |
| Retail / CPG | 63 | 11.4% | Merchandising, pricing, demand forecasting, and customer-insights analytics. |
| Consulting | 57 | 10.3% | Analytics advisory, decision science, and data-driven client delivery. |
| Technology | 55 | 10.0% | Product, data-platform, and BI roles at software and internet firms. |
| Healthcare | 51 | 9.2% | Healthcare business/clinical analytics, payer/provider data, pharma analytics. |
| Manufacturing / Logistics | 38 | 6.9% | Supply-chain, operations, and quality analytics; aerospace and energy. |
| Marketing / Advertising / PR | 27 | 4.9% | Campaign, digital, and media analytics at agencies and brands. |
| Government / Education / Nonprofit | 25 | 4.5% | Policy, program, and institutional-research analytics. |
| Media / Sports / Hospitality | 21 | 3.8% | Audience, fan, and guest-experience analytics. |
| Other (residual) | 56 | 10.2% | Real estate, miscellaneous, and self-described industries. |
| Accepted title | Times accepted | Aligns with pathway |
|---|---|---|
| Data Analyst | 40 | Analytics |
| Senior Data Analyst | 27 | Analytics |
| Business Analyst | 19 | Analytics · Consulting |
| Analyst | 15 | Analytics · Finance · Consulting |
| Financial Analyst | 11 | Finance |
| Healthcare Business Analyst | 8 | Healthcare analytics |
| Marketing Analyst | 6 | Marketing analytics |
| Business Intelligence Analyst | 5 | Analytics · IT |
| Consultant | 5 | Consulting |
| Operations Analyst | 5 | Operations |
| Associate Consultant · Risk Modeling Associate | 4 each | Consulting · Finance |
| Technology Analyst · Strategy Analyst · Risk Analyst | 4 each | IT · Strategy · Finance |
| I want to pull, clean, and analyze data to answer business questions | Analytics → Data Analyst, Senior Data Analyst, Business Analyst |
| I want to build dashboards and the reporting layer the business runs on | Analytics / IT → BI Analyst, BI Developer, Analytics Engineer |
| I want to build predictive models and apply machine learning | Analytics / Technical → Data Scientist, Decision Analytics Associate |
| I want to solve client problems with data and present recommendations | Consulting → Analytics Consultant, Associate Consultant, Strategy Analyst |
| I want to apply analytics to credit, fraud, risk, and financial decisions | Finance → Financial Analyst, Risk Analyst, Risk Modeling Associate |
| I want to work with clinical, claims, or payer/provider data | Healthcare analytics → Healthcare Business Analyst, Analytics Associate |
| I want to optimize networks, forecast demand, and improve operations | Operations → Operations Analyst, Supply Chain Analyst, Project Manager |
| I want to measure marketing, run experiments, and segment customers | Marketing analytics → Marketing Analyst, Digital Analytics Analyst |
| I want to build data pipelines and platforms on the engineering side | Technology → Technology Analyst, Data Engineer, Solution Architect |
Select a target job title, choose a skill, then use the curriculum-to-career map to turn coursework into resume evidence and interview stories.
Expand each term to see how its courses translate into job-search value. Course names, credit hours, and structure below reflect the official MSBA Program Curriculum (updated 1/14/2026); the program notes it is subject to minor changes. Because more than half of MSBA graduates land in analytics roles, your technical portfolio — Python, SQL, a machine-learning model, a dashboard, and the three-course Practicum — is the single most important thing the curriculum produces for your search.
| Course | How to use it in your search |
|---|---|
| MSBA Analytics Foundations (Virtual) · optional | Offered virtually before the summer term (contact Admissions for details). If your calculus, statistics, or programming background is rusty, use it to enter Summer 1 ready — the summer is accelerated and assumes you can keep pace from day one. |
| Course (credit hours) | How to use it in your search |
|---|---|
| Statistical Thinking for Business Decision-Making (3) | The quantitative foundation for every analytics, risk, and data-science role. Pull examples of probability, distributions, confidence intervals, hypothesis testing, ANOVA, and linear regression — and be ready to explain how you'd apply them to a business decision under uncertainty. |
| Python Programming for Business Analytics (3) | The working language of modern analytics — and a skill employers test. Treat every assignment as portfolio material: variables, data structures, loops, functions, automation, and the core data libraries. Start a public GitHub now so your code is visible to recruiters. |
| Career Management (1.5) | The anchor course for your search: understanding analytics career paths and building lifelong career-management skills. Use it to build your resume, LinkedIn, target-employer list, and fall-recruiting plan around real analytics roles. |
| Course (credit hours) | How to use it in your search |
|---|---|
| Data Management for Business Analytics (3) | The most-tested skill in analyst interviews. Hands-on SQL, relational databases, database design, cloud services, APIs, data integration, and big-data systems. Position it as the ability to independently pull, join, and manage large datasets — the baseline employers screen for. |
| Machine Learning for Business Analytics (3) | The differentiator for data-scientist and advanced-analyst roles. Covers regression, classification trees, clustering, random forest, gradient boosting, KNN, recommendation engines, neural networks, and explainable ML. Build one model end-to-end and make it a portfolio centerpiece. |
| Data-Driven Storytelling for Impact (3) | Turns analysis into decisions — directly fuels interviews and case presentations. Build a visualization you can screen-share; frame it as communicating complex analysis to executives in a compelling, influential way. |
| Analytics in Society (1.5 · mini) | Increasingly screened for. Reference ethics in big data, privacy, security, and regulatory compliance — the responsible use of analytics and AI. |
| Decision Modeling (1.5 · mini) | Consulting, operations, and strategy-analytics roles. Pull examples of structured problem framing, advanced spreadsheet modeling, optimization, decision theory, and Monte Carlo simulation. |
| Practicum Introduction: Assessing Performance & Managing Projects (1.5 · mini) | Position it as project management, teamwork dynamics, and aligning analytics with organizational strategy — the professional skills that surround the technical work. |
| Business Analytics Practicum I (1.5) or Sports Analytics Practicum I (1.5) | Your strongest emerging resume project: a student team working with a corporate partner (or, in the Sports track, embedded with an athletic team or sports organization) on a real problem under a faculty mentor. Start documenting it now for interviews. |
| Course (credit hours) | How to use it in your search |
|---|---|
| Marketing Analytics (3) | Marketing- and customer-analytics roles. Reference market segmentation, perceptual mapping, A/B testing, web analytics, and online advertising — leveraging digital data to inform marketing decisions. |
| Financial Analytics and Risk Management (3) | The strongest match for the banks and lenders that dominate MSBA outcomes. Pull examples in financial modeling, risk measurement, time value of money, valuation, portfolio theory, capital budgeting, decision trees, and simulation. |
| Operations and Supply Chain Analytics (3) | Operations, supply-chain, and logistics-analytics roles. Emphasize discrete-event simulation, process improvement, inventory management, and supply-chain network design and optimization. |
| Applied AI for Advanced Analytics (3) | A standout differentiator. Reference the architecture of large language models, adapting AI to business applications, using AI to boost analytical productivity, AI development best practices, and enterprise AI integration — current, in-demand language for any analytics interview. |
| Business Analytics Practicum II (3) or Sports Analytics Practicum II (3) | The capstone asset: continue the team-based, corporate-sponsored Practicum, deliver recommendations, and present findings. Treat it like a consulting engagement and make it your single strongest interview story (problem, your role, the analytics, the impact). |
| Course (credit hours) | How to use it in your search |
|---|---|
| Advanced Sports Analytics and AI (3) | For sports-industry analytics roles. Reference time-series data, Bayesian and advanced ML models for player/team performance, injury risk, and game outcomes, plus applications in roster optimization, game strategy, ticket pricing, player salaries, and fan-engagement prediction. |
| Sports Data Engineering and Applied Analytics (3) | For data-engineering and analytics roles in sports. Emphasize end-to-end workflows (ingestion, cleaning, storage, integration, pipeline automation) on messy, high-velocity data (ticketing, fan engagement, player tracking, biometrics, CRM), cloud solutions and APIs, and interactive dashboards with R Shiny, Streamlit, and Plotly. |
| Career goal | Lean on these courses & projects |
|---|---|
| Data / Business Analyst | Data Management (SQL) · Python Programming · Statistical Thinking · Data-Driven Storytelling · the Practicum · a public dashboard & SQL portfolio |
| Data Scientist / ML | Machine Learning · Python Programming · Statistical Thinking · Applied AI for Advanced Analytics · an end-to-end model on GitHub · the Practicum |
| Financial / Risk Analytics | Financial Analytics & Risk Management · Statistical Thinking · Data Management · Decision Modeling · Machine Learning · a credit/fraud-style modeling project |
| Analytics Consulting | Data-Driven Storytelling · the Practicum (client delivery) · Decision Modeling · Applied AI · case + analytics-case prep |
| Healthcare Analytics | Machine Learning · Data Management (SQL) · Statistical Thinking · Analytics in Society (privacy/compliance) · a Practicum on a healthcare dataset |
| Operations / Supply Chain Analytics | Operations & Supply Chain Analytics · Decision Modeling (optimization/simulation) · Statistical Thinking · Data Management · a forecasting/optimization project |
| Marketing Analytics | Marketing Analytics · Data-Driven Storytelling · Python Programming · Machine Learning · a segmentation/A-B-testing project |
| Sports Analytics | Sports Analytics Practicum I & II · the Summer 2 concentration (Advanced Sports Analytics & AI; Sports Data Engineering) · Machine Learning · R Shiny / Streamlit / Plotly dashboards |
Across the updated 2023, 2024, and 2025 calendar-year hiring files, May is the strongest accepted-outcome month. The biggest strategic message for students is that the search should start well before the strongest hiring months: use spring to convert interviews, early summer to keep active opportunities moving, and fall to finish strong if you are still seeking.
| Calendar month | CY 2023 | CY 2024 | CY 2025 | Total |
|---|---|---|---|---|
| January | 2 | 6 | 1 | 9 |
| February | 4 | 6 | 3 | 13 |
| March | 2 | 7 | 4 | 13 |
| April | 3 | 6 | 6 | 15 |
| May | 9 | 21 | 17 | 47 |
| June | 5 | 14 | 10 | 29 |
| July | 13 | 13 | 6 | 32 |
| August | 7 | 7 | 4 | 18 |
| September | 4 | 3 | 5 | 12 |
| October | 9 | 7 | 5 | 21 |
| November | 13 | 9 | 6 | 28 |
| Total | 71 | 99 | 67 | 237 |
This dashboard translates the historical MSBA hiring timeline into a student-facing Career Planning Calendar. The hiring counts represent accepted outcomes during the calendar year after the MSBA Summer Term (July) and Fall Term (August-December) launch period. Use it to understand when to intensify applications, networking, interview preparation, follow-up, and Career Coach conversations. Your Career Coach can reference the same calendar with you during 1:1 appointments, workshops, and class sessions so your next steps stay aligned with the hiring cycle.
3-year average: 16.7 hires per year. Spring progress window before graduation. Students should be applying, interviewing, and using MSBA coursework and Practicum evidence before graduation.
3-year average: 36.0 hires per year. Largest MSBA hiring wave. Keep the search active after graduation and use final Practicum, project, and technical portfolio evidence to convert interviews.
3-year average: 10.0 hires per year. A smaller but meaningful rebound. Recalibrate target titles, widen employer lists, and refresh networking.
3-year average: 16.3 hires per year. Late-cycle opportunity window before year-end. Students still seeking should use intensive coaching, alumni outreach, and expanded search terms.
| Hiring period | Actual following-year data | Primary objective | What to work on with your coach | Success metric |
|---|---|---|---|---|
| 🎯 Wave 1: Pre-Graduation Hiring Jan–Apr |
50 hires 21.1% of all January-November accepted outcomes 3-year average: 16.7 hires/year |
Convert readiness into interviews and offers before graduation | Application strategy, interview practice, employer engagement, and offer negotiation | At-graduation employment momentum |
| 🚀 Wave 2: Graduation Hiring May–Jul |
108 hires 45.6% of all January-November accepted outcomes 3-year average: 36.0 hires/year |
Convert remaining active job seekers after graduation | Follow-up routines, alumni referrals, expanded target lists, and technical/case interview practice | 30- and 60-day post-graduation progress |
| 📈 Wave 3: Market Rebound Aug–Sep |
30 hires 12.7% of all January-November accepted outcomes 3-year average: 10.0 hires/year |
Capture late-summer hiring and newly reopened needs | Revised search terms, adjacent role groups, wider geographies, and employer outreach | Active interviews and stronger pipeline by September |
| 🏁 Wave 4: Outcomes Push Oct–Nov |
49 hires 20.7% of all January-November accepted outcomes 3-year average: 16.3 hires/year |
Reach every remaining opportunity before the six-month outcomes window closes | Intensive coaching, weekly pipeline audit, referral follow-up, and interview progress work | Six-month employment outcome |
Start with a target, pick the skill evidence you need to build, then connect that skill to the curriculum, Practicum, portfolio, and weekly search actions.
After Step 1, this will translate your pathway or role group into a clear search target and adjacent target.
After Step 1, this will show how to build a target list from historical MSBA employers, current postings, and adjacent employers.
After Step 1, this will set the weekly application, networking, skill-building, and interview-prep rhythm.
Tell us about your background and the role you're targeting, and we'll generate personalized talking points for recruiters, alumni, and interviews.
Deepen the technical and stakeholder-facing skills used in data, business intelligence, risk, operations, and analytics consulting.
How these resources fit: Complement and reinforce skills developed through the academic program while building additional capabilities for specific job titles and functional roles.
Choose a target role, identify the most important gap, and complete the course that helps you create credible evidence for that role.
Use these courses to strengthen responsible AI use, judgment, and communication across business functions.
Use AI productively while checking accuracy, protecting data, and preserving your own judgment.
Employers expect you to frame ambiguous problems, test assumptions, and recommend a practical next step.
Strong analysis only creates value when you can explain it clearly to a client, manager, or teammate.
Start with the cluster closest to your target role. Each cluster pairs two relevant courses with a work sample you can create.
SQL remains one of the most common analyst screens. Build from reliable reporting queries toward joins, subqueries, reusable logic, and business-ready outputs.
Strengthen the bridge between analysis, data models, reusable calculations, and decision-ready business intelligence.
Technical depth matters most when you can gather requirements, explain tradeoffs, and lead a stakeholder toward a defensible decision.
The current MSBA collection emphasizes querying, modeling, BI, and stakeholder communication. A/B testing adds causal experimentation; data stewardship adds quality, definitions, lineage, privacy, and governance—the controls that make analytics dependable.
Pair technical depth with the human skills needed to clarify needs, collaborate, earn trust, and move work forward.
Strong analysts connect methods and metrics to an operating decision, stakeholder, and value driver.
Analytics recommendations often surface uncertainty, limitations, and disagreement that must be handled clearly.
The power-skill collection aligns with the NACE Career Readiness Competencies and the World Economic Forum Future of Jobs 2025 skills outlook.
Use these only after reviewing several postings for your target title and confirming that the skill appears repeatedly.
Create one employer-ready portfolio case containing SQL, Python or DAX, a dashboard, documentation, and an executive recommendation. Be prepared to explain every technical choice without AI assistance.
LinkedIn Learning periodically updates course titles and URLs. These links were verified against LinkedIn's public course catalog in July 2026. Course access is provided through Wake Forest University.