Widener University

What you'll study

The programme combines core quantitative methods with applied computing and domain-focused projects. Typical modules cover data management and databases, statistical inference and predictive modelling, machine learning, data mining, data visualisation and communication, and big data technologies. Students work with programming languages and tools commonly used in the industry, such as Python, R and SQL, and encounter libraries and platforms for machine learning and visualisation.

The curriculum usually culminates in a capstone project or practicum with an applied dataset, where students design and deploy end-to-end analytics solutions, or a research project that advances methods in a particular domain. Elective options and special topics can include time series analysis, natural language processing, cloud-based data engineering, and domain-specific analytics for healthcare, finance or operations.

Structure

• Core modules in statistics, machine learning and data engineering.
• Practical lab and programming components to build applied skills.
• Capstone project or practicum integrating technical, analytical and communication skills.
• Flexible study modes: full-time and part-time pathways, with options for on-campus, hybrid or online delivery depending on availability.

Entry requirements

Applicants are expected to hold a bachelor’s degree from an accredited institution. Degrees in computer science, engineering, mathematics, statistics, economics or related fields are typically well aligned; applicants from other disciplines with quantitative coursework or relevant work experience are also considered.

• A completed undergraduate degree (minimum GPA expectations vary by cohort).
• Transcripts from prior higher education institutions.
• A CV or résumé outlining academic and professional experience.
• A personal statement describing goals, relevant experience and reasons for pursuing the degree.
• Some programmes may request letters of recommendation and may consider standardised test scores where applicable; check the programme office for current policy.

Applicants without formal programming or advanced mathematics background may be advised to take preparatory courses in statistics, calculus and introductory programming prior to or during the early part of the programme.

Career prospects

Graduates from a Master’s in Data Analytics pursue roles across public and private sectors. Common job titles include data analyst, data scientist, business intelligence analyst, analytics consultant, machine learning engineer and data engineer. Employers span industries such as healthcare, finance, manufacturing, technology, retail and government.

The programme’s applied focus prepares graduates for roles that require translating data into actionable insight, building predictive models, deploying analytics pipelines and communicating results to non-technical stakeholders. Alumni also use the degree as preparation for doctoral study or specialised certification in areas such as cloud computing and AI.

Why study at Widener University

Widener University emphasises experiential learning and close faculty mentorship. The university offers small class sizes that allow personalised feedback on projects and strong opportunities for collaboration with faculty who undertake applied research. Students benefit from connections to regional employers and internship and practicum placements that provide real-world data challenges.

The programme is oriented to practical skill development—coding, modelling and communicating results—while maintaining a foundation in statistical reasoning and ethical use of data. Flexible delivery options make it possible for working professionals to upskill without pausing their careers, and university career services support recruitment, résumé preparation and employer engagement.