The Bachelor of Science in Data Analytics prepares students for careers in a world increasingly dependent on data, in dynamic fields that require the application of interdisciplinary scientific and statistical methods, processes, and systems to extract knowledge or insights from data. Data Analytics is a technically-oriented program which will help students build a tool-set of data analytics skills. Students will gain real-world, practical training from leading-edge industry professionals who place data analytics within a business and enterprise context, ensuring that students become well-rounded professionals themselves. This program will help the adult undergraduate student acquire an understanding of, and competency in, current trends in data analytics, applying them to generate insights from data in a variety of business and organizational contexts. Students will learn about Big Data, master the technical aspects of data analytics, and understand the relevance of this type of analysis to business and organizations. Students will benefit from a curriculum that leverages critical thinking, information literacy, and effective communication skills to help students increase their professional marketability. These skills will advance students’ ability to analyze business problems, put those problems in perspective, and clearly communicate insights gained from data analyses.
Student Learning Outcomes
Students who successfully complete this program should be able to:
- Understand and apply the fundamentals of data analytics to real-world business problems.
- Leverage familiarity with the appropriate use of key analytic languages/methods/tools, including R, Python, SQL, NOSQL, SAS, and Tableau, to address business problems, and be able to articulate the advantages and limitations of each one in a variety of business and organizational contexts.
- Demonstrate ability to identify, acquire, cleanse and effectively organize data for analysis.
- Demonstrate a critical understanding of the utility of data analytics tools using data visualization methods in extracting value from data sets.
- Recognize the various challenges (social, economic, and political) represented by the Big Data ecosystem and describe the use of supporting technologies to address these challenges.
- Explain the differences between structured and unstructured data and be able to deploy them appropriately, aligning the use of each with relevant business applications.
- Describe the different approaches to machine learning and the implications of each one, demonstrating the application of the most common algorithms.
- Explain the use of Natural Language Processing, identifying and implementing potential applications and appropriate supporting tools.
- Use storytelling with visual outcomes from analytics to communicate effectively to members of the business community and others, both expert and non-expert, in a variety of settings and formats.
- Demonstrate an understanding of the business implications, relevance and applicability of data analytics and statistical inferences.
- Identify opportunities, needs and constraints for data analytics within organizational contexts.
Requirements for the Bachelor of Science in Data Analytics
The degree requires completion of 123 units as follows: 39 units of general education coursework (including 21 units of liberal studies core), 54 units required for the major (including 9 units of foundation courses, 15 units of business courses, and 30 units of data analytics courses), and 30 units of general elective courses. Each course listed carries three semester units of credit, unless otherwise noted.
A cumulative grade-point average of 2.00 (C) or higher is required in all courses taken at Golden Gate University. Course prerequisites, if any, are shown in the course descriptions.
All degree-seeking undergraduate students must complete their English, mathematics and critical thinking requirements within their first 27 units at Golden Gate University. Placement tests must be taken prior to enrolling in ENGL 10A , ENGL 10B , or ENGL 1A and MATH 10 , MATH 20 , or MATH 30 to ensure proper placement in the sequences. (See course descriptions for details).