Curriculum

Degree Requirements

To be eligible for the M.S. in Business Analytics you must have been Advanced to Candidacy (see Graduate Degree Information in Cal State East Bay University catalog) and have completed 30 semester units meeting the following criteria:

  • All have a course grade of "C" or better.

  • Have a combined 3.0 grade point average (minimum) in all units taken to satisfy the requirements of the student's degree program.

  • Have no more than 20 units for extension and/or transfer credit (any extension and/or transfer credit must be approved by the Program Director) and/or coursework taken in "Unclassified Postbaccalaureate" status.

  • All units earned within the five years immediately preceding the completion of the requirements for the degree.

  • Have completed a satisfactory program of study, defined below.

TWO PREREQUISITE COURSES* (6 UNITS):

BAN 601 - Technology Fundamentals for Analytics (3)

    • This course covers technologies for developing analytics applications. Topics include: fundamentals of business programming, sequential file processing, detail and summary reporting, and data validation. A-F grading only. Minimum grade requirement: B-.

BAN 602 - Quantitative Fundamentals for Analytics (3)

    • Fundamental quantitative methods with software. Topics include: programming for data analysis, computational linear algebra, and quantitative methods. A-F grading only. Minimum grade requirement: B-.

REQUIRED COURSES (18 UNITS)

Required courses will be taught with modern data analytics programming languages and tools, e.g., SQL, Hadoop, SAS, R, Python, Excel-based add-ins, etc.

BAN 610 Database Management and Applications (3)

    • Data modeling, database design and implementation, database administration, and database applications. Topics include: database design, incorporating business rules into entity-relationship (ER) models, transformation of an ER model into a relational database design, normalization of database tables, and SQL languages. Prerequisites: BAN 601 and BAN 602 or consent of instructor. A-F grading only.

BAN 612 Data Analytics (3)

    • Data collection, preparation, visualization, and analysis with software applications. Topics include: web scraping, Application Program Interface (API) data collection, data wrangling, visualization, data type and structure, and computational and quantitative methods. Prerequisites: BAN 601 and BAN 602 or consent of instructor. A-F grading only.

BAN 620 Data Mining (3)

    • Course introduces the fundamental concepts of data mining and provides extensive hands-on experience in applying the concepts to real-world applications. Topics include dimension reduction, classification, association analysis, clustering and model evaluation techniques. Business applications of data mining techniques are emphasized. Prerequisites: BAN 602 or consent of instructor. A-F grading only.

BAN 622 Data Warehousing and Business Intelligence (3)

    • Data warehousing and business intelligence concepts, design, implementation, and software tools. Topics include data warehouse architecture, dimensional model design, data integration and visualization, business intelligence reporting and dashboards. Prerequisites: BAN 610 or consent of instructor. A-F grading only.

BAN 630 Optimization Methods for Analytics (3)

    • Determining the best solution among various choices, suggesting decision options, and illustrating the implications of each option. Topics include: optimization methods, queuing models, simulation, and application-based software. Prerequisites: BAN 602 or consent of instructor. A-F grading only.

BAN 632 Big Data Technology and Applications (3)

    • This course covers key technologies and applications for big data analytics. Topics include: distributed file systems, big data input/output, streaming technologies, techniques for parallel processing, and big data application development. Prerequisites: BAN 601 and BAN 602 or consent of instructor. A-F grading only.

ELECTIVE COURSES  (9 UNITS)

Select three courses from the following:

BAN 660 Advanced Topics in Big Data

    • Newest technologies for Big Data analytics. Topics include: new tools, algorithms, and platforms for Big Data storage, processing, analysis, visualization, and application.

BAN 670 Advanced Topics in Analytics

    • Recent development in various topics of analytics, which include, but not limited to, advanced data visualization, machine learning, text mining and web analytics, natural language processing, and industrial applications of data analytics.

BAN 671 Data Analytics with R

    • Focus on applying business analytics techniques using R programming. Emphasis on using R syntax and packages for data manipulation, visualization and advanced analytics in the context of business environment.

BAN 672 Data Analytics with SAS

    • Focus on applying business analytics techniques using SAS programming. Emphasis on using SAS syntax and modules for data manipulation, visualization and advanced analytics to solve real world business problems.

BAN 673 Time Series Analytics

    • Introduces fundamental concepts and methods of time series analytics. Topics include evaluation of data patterns, moving averages, smoothing models, regression-based forecasting, and ARIMA models. Emphasis is on hands-on experience in applying time series analytics in business.

BAN 674 Machine Learning for Business Analytics

    • Covers key topics in machine learning and their applications to business, including but not limited to, supervised and unsupervised learning, regularization, anomaly detection, and recommender systems.

BAN 675 Text Mining and Social Media Analytics

    • Covers basic natural language processing techniques, document representation, text categorization, sentiment analysis, topic modeling, text visualization, and social media analytics.

BAN 676 Deep Learning for Business Applications

    • Concepts, design and implementation of deep learning models for business analytics. Topics include Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks such as Long Short Term Memory and business value of deep learning technology.

MKTG 612 Marketing Analytics

    • Marketing analytics metrics and models. Approach marketing issues analytically; make marketing decisions driven by quantitative evidence. Topics include resource allocation analytics, product analytics, marketing-mix analytics, customer analytics, and digital analytics. Quantitative techniques enforced through hands-on data analysis and case-based learning.

MGMT 616 People Analytics

    • Data analysis of human resource management issues, such as recruitment, compensation, performance management, leadership, and employee engagement. Topics include systematic collection, analysis, and interpretation of data to enable strategic decision making for HR functions.

MGMT 654 Enterprise Planning and Control

    • Techniques used in planning and scheduling resources with Enterprise Resource Planning (ERP) systems. Topics include demand forecasting, aggregate planning, material and capacity requirements planning, supply chain planning, global sourcing, and customer relationship management

MGMT 658 Project Management

    • Processes, tools and techniques required in managing projects such as product development, construction, information systems and new business. Topics include project scope, schedule, cost and resource management, risk management, managing teams and project control.

CAPSTONE EXPERIENCE (3 UNITS)

BAN 693 Business Analytics Capstone Project (3)

    • This course includes a project to solve a practical problem by applying and integrating the knowledge and skills learned in the Business Analytics degree program. The capstone project will use real business data from an identified organization. Prerequisites: All prerequisite and required courses. A-F grading only.