Golden Gate University

What you'll study

The programme combines foundational mathematics and statistics with practical training in programming, databases and visualisation. Study begins with core numeracy and computing modules and progresses to specialised subjects that reflect current industry practice. Typical areas of study include:

• Foundations in statistics and probability covering descriptive and inferential statistics, hypothesis testing and regression analysis.
• Programming for data analytics using languages commonly used in industry (for example Python and R), including data structures, scripting and reproducible workflows.
• Data management and databases with hands-on SQL, data modelling and introduction to data warehousing concepts.
• Data wrangling and engineering techniques for cleaning, transforming and preparing structured and unstructured data for analysis.
• Data visualisation and communication focusing on dashboarding, storytelling with data and tools such as Tableau or equivalent libraries.
• Introduction to machine learning covering supervised and unsupervised methods, model evaluation and practical deployment considerations.
• Big data and cloud technologies examining distributed processing concepts and cloud-based data platforms commonly used in the Bay Area and beyond.
• Ethics, privacy and legal considerations related to data collection, algorithmic bias and governance.
• Business analytics and domain applications where analytics methods are applied to finance, marketing, healthcare or public sector problems.
• Capstone project or practicum integrating technical and communication skills in a real-world or industry-sponsored project.

Course delivery emphasises applied, project-based learning with opportunities for internships and industry projects. The programme can be taken full time or part time, with flexible scheduling and online options available to suit working students.

Entry requirements

Applicants are normally expected to hold a recognised secondary school qualification (high school diploma or equivalent). Admissions typically consider academic transcripts, a personal statement and any relevant work or extracurricular experience. Applicants should demonstrate competence in secondary-level mathematics; some prior exposure to computing or statistics is advantageous but not always required.

Transfer applicants with college credits or an associate degree will have their transcripts evaluated for transfer credit. Mature students and those with relevant professional experience are encouraged to apply; they may be asked to submit additional documentation such as a resume or references. Placement testing or introductory bridging courses can be available for applicants who need to build skills in mathematics or programming before advancing to higher-level modules.

Career prospects

Graduates leave prepared for roles that require analytical thinking, technical skills and the ability to communicate insights. Typical entry-level job titles include data analyst, business analyst, junior data scientist, business intelligence developer and analytics consultant. With experience, alumni progress to specialised positions such as data engineer, machine learning engineer or analytics manager.

The degree supports careers across sectors including technology, finance, healthcare, retail, government and consulting. Many graduates also choose to continue into postgraduate study in data science, analytics, computer science or business (for example an MBA) to deepen technical expertise or move into leadership roles.

Why study at Golden Gate University

Golden Gate University is well placed for students aiming to enter the data and tech ecosystem. The university offers a practitioner-led approach, with faculty who bring industry experience into the classroom and an emphasis on career-relevant skills. Small class sizes and flexible scheduling, including evening and online options, make the programme accessible to working students and career changers.

Being located in the San Francisco Bay Area gives students proximity to a wide range of employers, startups and professional networks, which supports internship and project partnerships. GGU also provides career services, employer engagement events and support for professional development to help students transition from study into analytics roles.