Mercer University

What you'll study

The programme combines foundational coursework in statistics and data management with applied modules in predictive analytics, optimisation and data visualisation. Typical core subjects include Probability and Statistical Inference for Business, Database Management and SQL, Predictive Modeling and Machine Learning, Data Visualisation and Communication, and Business Intelligence. Many students also take electives in areas such as marketing analytics, financial analytics, supply chain and operations analytics, and text or social-media analytics.

Instruction balances theory and practice: classes emphasise hands-on work with tools and languages commonly used in industry (for example SQL, Python or R, and visualisation software). The curriculum ordinarily culminates in a capstone project or practicum in which students apply analytical methods to a real-world business problem, often working with an external client or Mercer-affiliated partner.

Entry requirements

• Academic background: A recognised bachelor’s degree in any discipline; applicants with degrees in business, economics, mathematics, statistics, engineering, computer science or related fields are preferred, though applicants from other backgrounds who demonstrate quantitative aptitude may be considered.
• Transcripts and GPA: Official undergraduate transcripts are required. Competitive applicants typically present strong performance in quantitative coursework; specific minimum GPA requirements are set by the university and should be confirmed with admissions.
• Standardised tests: The programme may consider or accept GRE/GMAT scores; whether these are required can depend on the applicant profile. International applicants should check current test policies.
• Work experience and materials: A current résumé, a statement of purpose outlining analytic interests and goals, and letters of recommendation (usually one to three) are typically requested. Relevant work experience can strengthen an application but is not always mandatory.
• English language: For applicants whose first language is not English, proof of English proficiency (TOEFL, IELTS or equivalent) is normally required unless exempted by prior study in English.
• Prerequisites: Coursework or demonstrable competence in college-level statistics and some exposure to programming or data analysis tools is recommended. Bridging or foundation modules may be available for applicants who need to build specific skills.

Career prospects

Graduates are prepared for roles that require turning data into actionable insight across sectors. Typical job titles include Data Analyst, Business Analyst, Analytics Consultant, Business Intelligence Developer, Operations Analyst and Associate Data Scientist. With experience, alumni often progress to roles such as Analytics Manager, Product Analytics Lead, or Director of Business Intelligence.

Employment sectors for graduates include finance, healthcare, retail and consumer goods, consulting, technology, logistics and manufacturing. Mercer’s regional connections and career services support students seeking internships and employer-engaged projects to build practical experience and professional networks.

Why study at Mercer University

• Applied, career-focused instruction: The programme emphasises practical analytics skills and real-world projects that prepare students to contribute to business decision-making from day one.
• Small cohorts and faculty access: Mercer’s class sizes and faculty mentoring offer personalised support; faculty combine academic expertise with professional and industry experience in analytics and business.
• Capstone and industry engagement: Opportunities for practicum or capstone projects connect students with external organisations, giving direct experience of applying analytics to business problems.
• Regional business network: Mercer’s location and alumni network provide access to employers across sectors, including corporate, healthcare and consulting organisations, supporting internship and placement pathways.
• Support services: Career services, workshops on technical tools and communication, and resources for professional development help students build the portfolio and interview skills sought by analytics employers.