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Data Analytics is a technically-oriented program that 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.

GGU is excited to offer six new nine-unit data analytics certificates that can be completed in as little as one semester. Learn in-demand skills in with some of the most-used tools: Tableau, HQL, Python, R, SAS, and SQL.

ADMISSION REQUIREMENTS

Requirements for the Bachelor of Science in Data Analytics

The degree requires completion of 120 units as follows: 36 units of general 48 units for the major, and 36 units of elective courses,, including courses taken toward minors. (See Declaring Minors below for more information.) 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.

All degree-seeking undergraduate students must complete their English, mathematics and critical thinking requirements within their first 27 units at Golden Gate University, unless they have already earned credit for the equivalent courses from another institution and have had those courses accepted in transfer by Golden Gate University. If either Math or English requirements for the degree have not been satisfied, newly enrolled students must take placement tests to ensure proper placement in the appropriate Math or English course. Students may also choose to waive the placement tests and enroll in the first course in either series, which are ENGL 10A and MATH 10. (See the course descriptions below to identify courses that have prerequisite course requirements.)

GENERAL EDUCATION - 36 UNITS

Lifelong Learning and Self Development - 3 units

UGP 10
Gateway to Success (to be taken in the first term of the program)

Communication and Critical Thinking - 9 units

CRTH 10
Critical Thinking
ENGL 60
Research Writing
AND
one of the following:
COMM 35
Speech Communication
COMM 40
Understanding Communication

Quantitative Reasoning - 3 units

MATH 20
Applied Intermediate Algebra

Liberal Studies - 21 units

ARTS 50
Contemporary Arts & Culture (or any other ARTS course offered)
HIST 50
Contemporary American Economic History (or any other HIST course)
HUM 50
Examining the Humanities (or any other HUM course)
LIT 50
Principles of Storytelling (or any other LIT course)
PHIL 50
Professional & Personal Ethics (or any other PHIL course)
SCI 50
Science, Technology & Social Change (or any other SCI course)
SOSC 50
American Government in the 21st Century (or any other SOSC course)
OR
 
PSYCH 10
Psychology for Personal & Professional Success (or any other PSYCH course)

MAJOR REQUIREMENTS

FOUNDATION COURSES - 12 UNITS

ENGL 120
Business Writing
MATH 30
College Algebra
MATH 40
Statistics
MATH 104
Quantitative Fluency for Business Managers and Leaders

DATA ANALYTICS - 36 UNITS

DATA 50
Introduction to Business & Data Analytics
DATA 101
Data Visualization for Business
DATA 102
Business Intelligence & Data Mining
DATA 103
Data Analytics Using SAS
DATA 104
Introduction to R Programming for Data Analysis
DATA 105
Introduction to Social Media & Web Analytics
DATA 110
Introduction to Python Programming for Machine Learning
DATA 111
Introduction to Natural Language Processing
ITM 108
Introduction to Relational Databases
DATA 120
Introduction to Big Data
DATA 125
Artificial Intelligence in Business
DATA 190
Capstone

ELECTIVE COURSES - 36 UNITS

Select twelve additional 3-unit upper or lower-division courses from any subject for a total of 36 units. Note: courses used tocomplete minors also count toward this requirement.

Declaring Minors

To be eligible to declare minors, students must have already completed the required coursework, or be able to complete it intheir final terms without requiring waivers, substitutions, or directed study courses, unless they are approved in advance by thedepartment chair, program director, or dean.

Students may declare minors when they have completed the required coursework, or after the “Last Day to Drop Course with-out Tuition Charge” (per the Academic Calendar) for their final terms.

Students may not declare the same minor as their major (i.e., students majoring in accounting may not also declare minors inaccounting.) Students may declare up to two minors in a given degree program. Students seeking to declare more than twominors will be required to appeal to the dean for approval. Students’ diplomas will list the minors that they had successfully completed at the time their degrees were conferred. Students may not declare additional minors after their degrees have beenconferred.

Students may select from the following minors:

  • Accounting Minor
  • Business Minor
  • Data Analytics Minor
  • Finance Minor
  • Human Resource Management Minor
  • Information Technology Minor
  • International Business Minor
  • Law Minor
  • Literature Minor
  • Management Minor
  • Marketing Minor
  • Operations and Supply Chain Management Minor
  • Organizational Leadership and Human Skills Development Minor
  • Psychology Minor
  • Public Administration Minor

Data Analytics Minor

This minor teaches students how to use tools to extract, categorize, and examine large amounts of information in order to draw insights that can help organizations make betterinformed business decisions. Instruction is relevant and applicable to a broad range of industries and disciplines, including marketing, management, finance, financial planning, project management, human resources, information technology, operations, supply chain management, and others. The curriculum covers a breadth of data analytics tools and concepts, including dashboards, R Language, SAS, Data Mining, and SQL, among others.

Required Courses - 15 units

Select five of the following:

DATA 50
Introduction to Business & Data Analytics
DATA 101
Data Visualization for Business
DATA 102
Business Intelligence & Data Mining
DATA 103
Data Analytics Using SAS
DATA 104
Introduction to R Programming for Data Analysis
DATA 110
Introduction to Python Programming for Machine Learning
DATA 115
Introduction to Relational Databases & SQL
DATA 120
Introduction to Big Data
ITM 108
Introduction to Relational Databases
MATH 104
Quantitative Fluency for Business Managers and Leaders

LEARNING OUTCOMES

Students who complete the Bachelor of Science in Data Analytics, including the general education program, will 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.

MANAGEMENT, HUMAN RESOURCES AND INTERNATIONAL BUSINESS DEPARTMENT

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