Curriculum

The BS in Business Analytics is a fully online 4-semester program specially designed for students with AS-T in Computer Science or equivalent.

The innovative curriculum consists of rigorous courses that will help build your capabilities in technical, analytical, and operational areas to prepare your career with modern computer analytics and management skills.

This STEM designated 4-semester fully online or hybrid program is transfer-friendly for students with any AS-T degree. Students with an AS-T in computer science will be able transfer all 60 lower division units to this program. For students with an AS-T in other science majors (such as Math, Chemistry, Biology, or Physics), it requires only two (2) Lower Division core courses:

  • MATH 130 Calculus I (or equivalent course from Community College)
  • CS/MATH 211 Discrete Structures (or equivalent course from Community College)

After admission, students may complete the LD core courses online through California Virtual Campus. Special sessions of the above courses will be offered to help admitted students fulfill the prerequisites

Throughout this degree completion program (with 60 upper-division units), students will complete core courses (33 units), analytics concentration courses (18 units), and elective breadth courses (9 units) taught by faculty specialized in computational analytics, information systems, marketing analytics, supply chain analytics, statistics, as well as computer science. The blending of these course types provides students with a holistic skill set and allows them to focus on a wide range of topics including in-depth business applications using emerging computational algorithms such as AI, ML, information systems, marketing intelligence, or management science.

Our online learning tools bring you face-to-face with classmates and professors who expose you to real-world business challenges. You will learn from Silicon Valley professionals who have experience leading analytics teams in real-time during live, online classes and complete engaging, interactive coursework that reflects the latest thinking in analytical tools and methods.

Download Roadmap- Information System and Supply Chain Analytics Concentration 

Download Roadmap- Marketing Analytics Concentration 

Qualified students may complete 30 units MBA core courses, full-time (in 9 months) or part-time (while working), to receive MBA Degree in Analytics for Manager Concentration

Earn an AACSB-accredited MBA Degree after Completing 30 Units of MBA Core Courses

Students are now able to earn an AACSB-accredited MBA degree from the College of Business and Economics at CSU East Bay for 12 units fewer credits. Qualified students may complete 30 units of MBA core courses, full-time (in 9 months) or part-time (while working), to receive an MBA Degree in Analytics for Manager Concentration. For up to five years after graduating, students may take advantage of this opportunity and apply for this online MBA program. This opportunity allows students to develop skills in an additional area of interest can help set them apart in the job market and expose them to a wider variety of career opportunities.

The following 30 units of MBA core courses are required to complete this accelerated MBA program:

  • ACCT 604 Financial Accounting
  • ECON 606 Managerial Economics
  • FIN 605 Corporate Financial Management
  • MGMT/MKTG 601 - Leading People and Organizations; Managerial Communication
  • MGMT 602 Business Analytics for Managers
  • MGMT 603 Managing Complex Issues in Global Context
  • MGMT 608 Operations and Supply Chain Management
  • MGMT 609 Negotiation and Conflict Resolution
  • MKTG 607 Marketing Management
  • MGMT 693 Strategic Management Capstone (Project)


The following four MBA concentration elective courses (12 units) are blended with B.S. in Business
Analytics program:

  • BAN 602 Quantitative Fundamentals for Analytics (replaced with BAN 315 Data Analysis with Python
    II)
  • BAN 610 Database Management and Applications (replaced with BAN 331 Database Management
    and SQL)
  • BAN 630 Optimization Methods for Analytics (replaced with BAN 320 Optimization and Simulation
    for Business Applications)
  • BAN 674 Machine Learning for Business Analytics (replaced with BAN 340 Machine Learning for
    Business Applications)

Faculty teaching B.S. in Business Analytics program are accomplished scholars with extended academic and industrial connections with companies in Silicon Valley and San Francisco Bay Area. Our dedicated faculty are passionate about working with their students. We look forward to getting to know you, mentoring you, and supporting your academic and professional journey.

 

Faculty Name 

Degree and Institute

Area of Interest

Courses Taught in Business Analytics

Bryant Cassidey

Ph.D., University of Alabama

Operations and Supply
Chain Analytics

Decision Science,
Optimization for
Analytics

Varick Erickson

Ph.D., University of California, Merced

Computer Science

Data Structures and Algorithms

Ivan Fedorenko

PhD, Bentley University

Marketing Analytics

Digital Media Analytics
Customer Analytics

Yuanyuan Gao

PhD, University of Utah

Information System

Big Data and Technology
Data Warehousing and BI

Jia Guo

PhD, University of Alabama

Supply Chain Analytics

Data Analytics,
Optimization for
Analytics,
Machine Learning,
Operations Analytics
Business Analytics for
Managers

Yi He

PhD, University of Hawaii

Marketing Analytics

Mobile Marketing and
AI, Consumer Analytics

Matt Johnson

Ph.D., College of William and Mary

Computer Science

Artificial Intelligence

Inkyu Kim

Ph.D, Michigan State University

Information System

Tech fundamental for
Analytics, Data Mining,
Deep Learning

Somak Paul

PhD, Ohio State University

Statistics, Operations
Management, Supply
Chain Analytics

Quantitative
Fundamentals for
Analytics, Optimization
Techniques

Steve Peng

PhD, York University

Operations Analytics

Data Visualization and Reporting

Zinovy Radovilsky

Ph.D., Scientific Research
Institute of Operations
Management

Operations/Supply Chain
Management and
Business Data Analytics

Data Mining
Optimization Methods
for Analytics
Time Series Analytics
Business Analytics for
Managers

Balaraman Rajan

PhD, University of Rochester

Operations Management
and Healthcare Analytics

Data Mining, Healthcare
Analytics

Surendra Sarnikar

PhD, University of Arizona

Information Systems,
Healthcare Analytics

Deep Learning, Data
Engineering and BI

Lan Wang

PhD, University of Florida

Operations Management
and Information System

Data Analytics in Python
Operations Analytics

Chongqi Wu

Supply Chain Analytics

Jiming Wu

PhD, University of Kentucky

Information System, Big
Data Analytics, Artificial
Intelligence

Capstone Project, Big
Data, Database, and Java
and Python Programming

Peng Xie

PhD, Georgia Institute of

Technology

Information System

Blockchain and Smart Contract

 

 

Upper Division Course Descriptions - Total 60 Units

The following eleven (11) core courses are required:

The following eleven (11) core courses are required:

BAN 310 Data Analysis with Python I
This course introduces widely-used Python libraries for exploratory and quantitative data analysis,
including NumPy, Pandas, Statsmodels, and Matplotlib, along with conceptual foundations such as
statistical inference, regression, and matrix for better understanding and mastery of the data analysis
toolkits.

BAN 315 Machine Learning for Business Analytics
This course further develops students' capability to perform quantitative and computational data
analysis with Python libraries such as Pandas, NumPy, SciPy, Scikit-Learn, TensorFlow, and PyTorch.

BAN 320 Optimization and Simulation for Business Applications (GE UD-B, Sustainability Overlay)
Determine the best solution among various choices. Applications include evaluating business decisions
and environmental impacts, experiment design, analyzing the sustainability of outcomes, and illustrating the
business implications of each option. Topics include sampling and hypothesis testing, regression
analysis, optimization methods, queuing models, simulation, and application-based software. Not for
Math credit.

MKTG 305 Business Communication (GE UD-D, Writing Requirement)
Students develop abilities to communicate effectively; write and revise business documents and work to
understand communication in business environments and cross-cultural settings.

BAN 331 Database Management and SQL
Introduces the design of modern databases to collect, organize, and share data for various business
transactional and decision support needs. It covers common database architectures, such as 
relational databases and data warehousing schemas.

BAN 340 Machine Learning for Business Applications
This course applies data mining and machine learning to business data analysis in Python with libraries
such as scikit-learn, TensorFlow, and PyTorch. It covers various supervised and unsupervised learning
models and model evaluation techniques. Not for Computer Science credit.

BAN 350 Data Wrangling and Data Pipeline
This course teaches how to streamline the data workflow with data pipelines. The workflow starts with
data gathering and extraction, and ends with data transformation, which cleans, reduce, expands, or
generate feature representations suitable for downstream machine learning modeling.

BAN 441 Business Data Visualization and Reporting (Social Justice Overlay)
This course applies methods for data visualization and interactive exploration of Big Data. Topics include
interactive maps, elements of visual perception, effective graphs, and charts, visual representations of
complex statistics, and report writing with an emphasis on analyzing social justice issues.

BAN 449 Big Data Technology and Business Applications
Introduces Big Data concepts, technologies, systems, and applications. Topics include Hadoop
distributed file system, input/output, and business application development with big data. Discuss how
companies leverage technologies to analyze Big Data.

BAN 499 Capstone Project
This course requires students to apply business analytics concepts, tools, and modeling skills to deliver
comprehensive analysis and provide recommendations for solving challenging scenarios that will be
encountered in the business environment.

The following two (2) courses are required:

ITM 336 - Info Systems Development and Management
Developing information systems including systems planning, analysis, design, testing, implementation,
and maintenance. Primary emphasis on learning and practicing techniques and processes used by
system analysts at each phase within a system development lifecycle.

MGMT 364 - Global Supply Chain Management (Diversity Overlay)
Concepts and tools necessary to configure and manage supply chains in a global environment. Topics
include network design, strategic planning, supply chain coordination, resource allocation, capacity
design, transportation management, material handling, and information technology.


Select four (4) courses from the list below:

ITM 338 Cloud Computing and E-Commerce
Technological and conceptual aspects of cloud computing and its application for E-Commerce. Primary
emphasis on developing skills in implementing business-oriented systems for e-commerce using cloud
computing and related information technologies.


MGMT 365 Enterprise Resource Planning and Control
The course presents concepts, applications, and information technology of Enterprise Resource Planning
(ERP) systems. Emphasis on methods and software tools used in aggregate planning, master scheduling,
material and distribution requirements planning, short-range capacity planning and scheduling, and
production activity control.


ITM 445 Blockchain and Smart Contract
This course covers essential knowledge about the blockchain ecosystem, including cryptocurrency,
smart contract, and their applications. Students will also learn using solidity and python to interact with
the blockchain.


ITM 446 Information Security
Principles and practices of computer system security including operating system security, network
security, software security, and web security. Topics include computer attacks, protection measures,
security principles, security models, and cryptography.


MGMT 450 Project Management
Processes, tools, and techniques required in managing projects. Topics include project selection and
strategic alignment, project scope, time, cost and resource management, risk management, project
control.


MGMT 455 AI-enabled Supply Chain and Logistics Analytics
This course showcases real-life applications of data analytics techniques in supply chain management
areas such as forecasting and inventory management, sales and operations planning, transportation,
logistics and fulfillment, purchasing and supply management, and supply chain risk management.

MGMT 460 Healthcare Analytics
The course presents concepts, tools, and analytics techniques to improve healthcare operations,
systems, and delivery. Application of analytics approaches in the domain of healthcare. Topics may
include quality management, process analysis, healthcare information technology, healthcare data, and
standards.

CS 461 Artificial Intelligence
Intelligence as computation, rational agent architecture, state spaces, breadth-first and depth-first
search, heuristic search, A* algorithm, game playing, hill-climbing and optimization, neural networks,
knowledge representation, production systems, machine learning techniques, introduction to Lisp and
Scheme. Programming projects.


MGMT 465 Supply Chain Forecasting with Machine Learning
The course presents concepts and methods of time series forecasting in supply chains using machine
learning algorithms and software. The emphasis is on data pattern evaluation, forecast accuracy,
smoothing models, regression with trend and seasonality, ARIMA models, and neural networks.

The following three (3) courses are required:


MKTG 300 Marketing Principles
Overview of marketing concepts and practical applications including considerations in designing
effective marketing programs. Through a mix of lectures, discussions, and/or projects, students will
understand the scope, the process, and the role of marketing in business firms and non-profit
organizations.

MKTG 310 Marketing Research
Marketing research process. Topics include problem formulation, research design, research instrument
development, data collection and analysis, and report writing and presentation. Students implement
marketing research techniques through assignments and/or projects.

MKTG 312 Marketing Analytics
Marketing analytics metrics and models. Understand how to use data to approach marketing issues
analytically. Topics include resource allocation analytics, product analytics, marketing-mix analytics,
customer analytics, and digital analytics. Quantitative techniques are enforced through case-based learning.

 

Select three (3) courses from the list below:

MKTG 420 Product, Service, and Brand Management
Product concept evaluation, optimal product design, test marketing, market segmentation, product
planning, service delivery, failure, and recovery, service quality, branding elements, brand equity, and
strategic branding.

MKTG 427 Digital Marketing and Social Media
Use of digital and social media in the marketing of goods and services. Topics include: the relevance of
emerging digital technologies to marketing activities, the problems, and opportunities that digital
marketing activities pose for the marketing managers.

MKTG 432 Marketing for New Ventures
The role of marketing in new ventures. Focuses on learning a conceptual framework for understanding
customers, designing appropriate marketing activities, making marketing decisions, and addressing
marketing problems in building a new venture.


MKTG 434 Social and Digital Media Analytics
This course provides an introduction to the fundamentals of social and digital media analytics. Topics
include: social and digital media landscape and common metrics, social listening, social and digital media
data analysis, interpretation, and application.


MKTG 435 Consumer Analytics
This course explores the need for customer centricity and examines data ethics, data privacy regulations,
and how to build a compliant analytics program. This course develops conceptual knowledge and
technical skills that enable organizations to understand customers’ actions, perceptions, and
expectations.

MKTG 436 Marketing Data Visualization
This course focuses on how to present data in a visual format (e.g., infographic, chart, diagram, and
picture) and generate powerful reports and dashboards that help managers make decisions and take
actions based on their business data.


MKTG 437 Mobile Marketing and AI
This course provides an overview of concepts, processes, and best practices for utilizing mobile platforms
and artificial intelligence in marketing and how to analyze results from such efforts. Topics include
mobile ecosystem, mobile advertising, Chabot, data-driven AI, and mobile analytics.

The following three (3) elective courses are required:

  • Select one (1) GE UD-C course
  • Select two (2) Upper Division computer science, statistics, or business courses

(Students who complete 2 elective Upper Division computer science courses will receive Computer Science
Minor)

List of Sample Elective Courses:

Students can select any concentration courses from Information Systems and Supply Chain Analytics or Marketing Analytics listed above.

Courses in Computer Science

  • CS 311 - Programming Language Concepts Units: 3
  • CS 321 - Computer Architecture Units: 3
  • CS 351 - Website Development Units: 3
  • CS 370 - Databases for Social and Health Sciences Units: 3
  • CS 400 - Computer Programming for Science Units: 3
  • CS 401 - Software Engineering Units: 3
  • CS 411 - Automata and Computation Units: 3
  • CS 413 - Analysis of Algorithms Units: 3
  • CS 421 - Operating Systems Units: 3
  • CS 431 - Database Architecture Units: 3
  • CS 441 - Computer Networks Units: 3
  • CS 453 - Mobile Programming Units: 3
  • CS 455 - Computer Graphics Units: 3
  • CS 461 - Artificial Intelligence Units: 3
  • CS 471 - Security and Information Assurance Units: 3

 

Courses in Management

  • MGMT 300 - Business and Professional Ethics Units: 3
  • MGMT 310 - Organizational Behavior Units: 3
  • MGMT 311 - Managing Diversity in the 21st Century Workplace Units: 3
  • MGMT 312 - Human Resources Management Units: 3
  • MGMT 314 - Leadership and Management Units: 3
  • MGMT 316 - Training and Development Units: 3
  • MGMT 318 - Employee Relations and Employment Law Units: 3
  • MGMT 320 - Negotiation and Conflict Management Units: 3
  • MGMT 360 - Operations Management Units: 3
  • MGMT 362 - AI-enabled Service Management and Quality Improvement Units: 3
  • MGMT 364 - Global Supply Chain Management Units: 3 (Diversity Overlay)
  • MGMT 365 - Enterprise Resource Planning and Control Units: 3
  • MGMT 369 - Supply Management and E-Commerce Units: 3
  • MGMT 370 - Business, Government and Society Units: 3
  • MGMT 385 - International Business Units: 3
  • MGMT 402 - Managing the Global Workplace Units: 3
  • MGMT 404 - Staffing and Talent Management Units: 3
  • MGMT 406 - Compensation and Reward Systems Units: 3
  • MGMT 408 - HR Analytics Units: 3
  • MGMT 450 - Project Management Units: 3
  • MGMT 455 - Supply Chain Data Analytics Units: 3
  • MGMT 460 - Healthcare Operations Management Units: 3
  • MGMT 485 - Launching and Scaling New Ventures Units: 3
  • MGMT 487 - Entrepreneurship Practicum Units: 3


Courses in Statistics

  • STAT 303 - Statistical Methods in Biology Units: 3
  • STAT 303A - Biostatistics for Health Science Units: 3
  • STAT 306 - Public Health Informatics: The Role of Data Units: 3
  • STAT 310 - Statistical Methods in the Social Sciences Units: 3
  • STAT 316 - Statistics and Probability for Science and Engineering Units: 3
  • STAT 320 - Introduction to Probability Theory I Units: 3
  • STAT 321 - Probability Through Simulation Units: 3
  • STAT 330 - Statistical Inference Units: 3
  • STAT 331 - Introduction to Analysis of Variance Units: 3
  • STAT 351 - Sampling Procedures for Surveys Units: 3
  • STAT 432 - Introduction to Linear Regression and Logistic Regression Units: 3
  • STAT 460 - Advanced Statistical Package Usage Units: 3
  • STAT 473 - Introduction to Nonparametric Statistics Units: 3
  • STAT 474 - Introduction to Time Series and Forecasting Units: 3
  • STAT 475 - Introduction to Stochastic Processes Units: 3
  • STAT 481 - Bayesian Statistics Units: 3
  • STAT 495 - Data Analysis with SAS Units: 3