Academics

Student Standing and Progress Toward the Degree

There are three categories of student status which reflect student progress toward the degree: "Conditionally Classified Graduate," Classified Graduate," and "Advancement to Candidacy."

Students achieve "Conditionally Classified Graduate" status when they have been admitted to the Master of Science in Business Analytics (MSBA) degree program, but have not yet completed the prerequisites for "Classified Graduate" status in the MSBA degree program
Students achieve "Classified Graduate" status when they have satisfactorily completed the prerequisites for "Classified Graduate" status in the MSBA degree program. (See Prerequisites for "Classified Graduate" status below)
Students are Advanced to Candidacy when they have completed the 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 6 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.
- To be eligible for the MSBA, a student must have been Advanced to Candidacy (see Graduate Information chapter of the catalog).

NOTE: Students who fail to maintain progress by falling below a 3.0 GPA in their graduate courses for one or more consecutive semesters will be academically disqualified from the university.

Prerequisites for "Classified Graduate" status

Students must satisfactorily complete BAN 601 - Technology Fundamentals for Analytics with a grade of "B-" or better.
Students must satisfactorily complete BAN 602 - Quantitative Fundamentals for Analytics with a grade of "B-" or better.
BAN601 and BAN602 can be waived if students have taken a similar GRADUATE course from an AACSB accredited institution with a grade of B- or better. To apply, students must submit a course substitution form, available from the CBE Graduate Office, together with the course syllabus.

With built-in flexibility, the MS Business Analytics program is designed to meet the various needs of full time students, as well as working professionals. The roadmap below provides a plan for students who wish to graduate as soon as possible. Students who require a slower pace can adjust their plans accordingly.

It is essential to take BAN 601, BAN 602, BAN 610, and BAN 620 as early as possible. Many advanced BAN courses have some of them as a prerequisite. Early completion of these four courses allows students tremendous freedom and flexibility in selecting courses during the remaining period of study.

Please note that flexibility may be limited in some semesters due to course availability and schedules. Please reach out to CBE Graduate Advising at cbe_grad@csueastbay.edu for help customizing your roadmap.

Three-Semester Roadmap

SEMESTER	COURSES
First Semester	BAN 601: Technology Fundamentals for Analytics BAN 602: Quantitative Fundamentals for Analytics BAN 610: Database Management and Applications BAN 674: Machine Learning for Business Analytics
Second Semester	BAN 612: Data Analytics BAN 622: Data Warehousing and Business Intelligence BAN 630: Optimization Methods for Analytics BAN 632: Big Data Technology and Applications
Third Semester	BAN 693: Business Analytics Capstone Project Elective 1 Elective 2 Elective 3

Two-Semester Roadmap

For Students Who Have BAN 601 and 602 Waived

SEMESTER	COURSES
First Semester	BAN 610: Database Management and Applications BAN 612: Data Analytics BAN 674: Machine Learning for Business Analytics BAN 630: Optimization Methods for Analytics Elective 1
Second Semester	BAN 622: Data Warehousing and Business Intelligence BAN 632: Big Data Technology and Applications BAN 693: Business Analytics Capstone Project Elective 2 Elective 3

Resources for Current Students

Graduate Student Forms

Listed below are some of the frequently used forms for graduate students. If you don't see the form you need, please see a full list at the Office of the Registrar or contact msba@csueastbay.edu.

Registration Petition - Late Add
Registration Petition - Late Drop
Request to Declare or Change Option for Graduate Students
Withdrawal Form
Special Registration Petition
Request to Postpone Graduation

Helpful Links for Current Students

Register and check your grades – MYCSUEB

Fees and Financial aid - Tuition Fees, Financial Aid and Pioneer Scholarship

Canvas – The Canvas program provides an electronic community and communication environment for faculty and students Canvas Web Site

Need a tutor? Visit the Student Center for Academic Achievement (SCAA) located at LI 2550 – (510) 885-3674 – to get help in English, Math, and Statistics. They also offer workshops in Time Management, Effective Study Skills, and Preparation for the Writing Skills Test (WST).

Looking toward the future? Explore careers, find internships, get information on job fairs/recruitment events, attend workshops, and more. Visit the Office of Career and Professional Development

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, the transformation of an ER model into a relational database design, normalization of database tables, and SQL languages. Prerequisites: Post-baccalaureate standing. 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. A-F grading only.

BAN 674 Machine Learning for Business Analytics (3)

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. Prerequisites: BAN 601 and BAN 602. 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. 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: Post-baccalaureate standing. 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 610. 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. Prerequisites: BAN 601

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. Prerequisites: BAN 601 and BAN 602

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. Prerequisites: BAN 601 and BAN 602

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. Prerequisites: BAN 601 and BAN 602

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. Prerequisites: BAN 602.

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. Prerequisites: BAN 601, BAN 602, BAN 620.

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 the business value of deep learning technology. BAN 601, BAN 602, BAN 620.

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 are 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. Prerequisites: BUS 602 and post-baccalaureate standing.

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, supply chain planning, global sourcing, and customer relationship management. Prerequisites: BUS 608

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. Post-baccalaureate standing.

MGMT 662 - Operations Analytics

Quantitative analysis of data that can be used to make efficient and profitable decisions in various operations settings. The emphasis is on utilizing methods and software of big data analytics in operations management.

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: Department consent. A-F grading only.