Online Master’s in Business Analytics

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The world runs on data. Join the race to the top.

Expertly curated by Silicon Valley leaders, the Online Master of Science in Business Analytics (MSBA) program from the Leavey School of Business will help you develop the analytical mindset required to solve modern business challenges. Even better, SCU’s leading-edge Online MSBA is brought to you by a business school ranked #3 in Best Online MBAs, #1 in Academic Excellence and #1 in Career Outcomes by Poets&Quants.¹

Whether you want to help companies expand their capacity for big data, translate and demystify digitized business models or uncover digital revenue opportunities, the standout faculty at SCU Leavey and the invaluable knowledge they’ll impart will more than prepare you for your next steps. And by earning your MSBA online, you can accomplish it all without compromising your career or other priorities.

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Are you looking to take your career to the next level? Earn your master’s degree online with Santa Clara University. Our Online Master of Science in Business Analytics, offered through the Leavey School of Business, was built to meet the demand for professionals who can skillfully wield analytics tools to produce improved business outcomes through a powerful combination of information technology and business coursework.

Complete the form to get a program brochure for SCU’s Online MSBA program. Get more details about the program, including information on the program structure, curriculum, benefits and application requirements.

Wield the Power of Business Analytics

Here, big data defines big opportunities. An Online Master of Science in Business Analytics helps you master the theories and practices needed to become a leader at any point in your career path, in any organization. We believe in harnessing the power and use cases of numerous data types to carry out modern business intelligence. Throughout the program, you can expect to develop a deep understanding of statistics and optimization, business analytics and information technology as you form an enduring foundation rooted in business acumen and data analysis.

The MSBA online program will also train you to use essential programming languages like SQL, Python, and R, database management, econometric tools and machine learning. With a wide range of elective courses to choose from, you can narrow your focus to a business-forward or analytics-driven path. All in all, our cutting-edge curriculum—which was designed by an advisory board of industry leaders—will help you influence decisions that are poised to perform.

Secure Your Spot in Big Data

The future of business will be shaped by data, and the leaders who have a firm grasp of the tools and techniques of business analytics will be the sculptors. Modern firms collect and create volumes of data, but they lack the analytical skills to extract insights and derive value from their unorganized (and sometimes archaic) datasets.

With so much valuable data available, companies are increasingly invested in finding the right people to help them capitalize on it—and they’re paying them well, too. The demand for professionals trained in data science and analytics increased by 50% across a number of sectors in the past few years, and the U.S. Bureau of Labor Statistics has listed data science as one of the top 20 fastest growing occupations.^2,3By 2025, the global market for big data is predicted to grow to $229.4 billion, and the average base salary for data scientists is $97,004, with the high end at $136,000.^4,5

Program Details

42 units of credit
17 courses
Complete in just over 1 year
3-day on-campus residency
Expedited admissions pathway available for SCU alumni
2 opportunities to start per year (fall and spring)
Capstone (faculty-sponsored) or practicum (company-sponsored) learning component

Our MSBA Alumni Excel in Silicon Valley and Beyond:

SCU Leavey’s Online MSBA Student Review

Learn more about what SCU’s Online Master of Science in Business Analytics (MSBA) degree has to offer—from a program alum.

SCU Leavey MSBA StudentTestimonial

SCU Leavey MSBA Student Testimonial

Completing Admission for the Online Master’s in Business Analytics

Completed MSBA online application

$158 application fee
- This fee will be waived for current SCU students and alumni
4-year bachelor’s degree from an accredited college or university
GMAT/GRE scores or waiver
Official transcripts for undergraduate degree and any post-baccalaureate work
Current resume or CV
2 letters of recommendation
3 business essays—collected together in one document—should be uploaded in the “Documents” tab of the Program Materials section (it will be labeled “Personal Statement” there)
Personal admission interview
- Qualified applicants will be invited to schedule a personal admission interview. Interviews can be conducted in person at Santa Clara University, via Zoom, or by telephone
Applicants for whom English is a non-native language must demonstrate English proficiency by submitting TOEFL or IELTS test scores. This requirement will be waived for applicants who score above the 50th percentile on the GMAT or GRE verbal section
All students, faculty, and staff are required to be fully vaccinated. To see full details please visit the COVID-19 Vaccination FAQs.

Alumni Expedited Pathway to Admission

We are proud to offer a preferred an expedited admissions pathway for the Online Master’s in Business Analytics to SCU alumni who qualify. Students are welcome to apply via this pathway if they have graduated with a GPA of 3.0 or above from any of Santa Clara University’s degree programs, or a GPA of 2.5 or higher from the School of Engineering or in any STEM-related discipline.

Expedited alumni applicants need only submit the online application, personal statement and current resume/CV for initial consideration. Leavey alumni will be evaluated solely on this basis, while alumni from other schools and departments at SCU may be contacted if our admissions team deems further documentation necessary to make a decision regarding your application (GMAT/GRE scores, letters of recommendation, personal statement, etc.). Speak to an Admissions Advisor for more information.

GMAT/GRE Waiver

A waiver for the GMAT/GRE requirement is available to applicants who have four or more years of full-time work experience and meet one or more of the following criteria:

A bachelor's degree from an accredited school with a cumulative GPA of 3.4 (on a 4.0 scale) for STEM majors or 3.5 or higher for non-STEM majors
A master's, doctorate or professional degree (JD, MD) with a cumulative GPA of 3.3 or higher (on a 4.0 scale) from an accredited university
A professional certification such as CPA or CFA (Note: Certificate programs do not count toward this requirement)
A conferred degree that is quantitative in nature, e.g., math, engineering, or hard sciences (undergraduate or graduate)
Five or more years of military enlistment
Alumni of the Santa Clara University Leavey School of Business with a GPA of 3.0 or higher

No GMAT/GRE waiver is automatic, and an approved waiver enables consideration for the Online MSBA program but does not guarantee admission.

Master’s in Business Analytics Courses

At the Leavey School of Business we pride ourselves on our reputation for flexibility and customization. The Online MSBA program provides just that, an opportunity to tailor your curriculum to your specific needs with 10 units to be chosen from a selection of up to 14 elective courses. Whether you want to focus on Big Data modeling and reinforcement learning, broaden your understanding of cloud computing and cloud computing architecture or delve into natural language processing and deep learning, our program can be tailored to fit your needs.

Corequisites

Due to the heavily quantitative nature of the Online Master’s in Business Analytics, applicants should provide evidence of successful completion of coursework in the following subject areas during or following their undergraduate education:

Statistics: Content should include topics in statistics, descriptive statistics, regression, probability, random variable and distributions, the central limit theorem, confidence intervals and hypothesis testing for 1 and 2 populations, goodness of fit, and contingency tables
Calculus 1: Content should include differential and integral calculus; key concepts of limit, derivative, and continuity; derivatives in graphing and optimizing functions; and fundamental theorem of calculus

Applicants who have not completed these courses may be considered for provisional admission to the Online MSBA program, contingent upon their completion prior to enrolling in the program

Core Courses (32 units)

FNCE2402/MSIS2402 Math for Finance and Analytics (4 units)

This course is designed to provide a comprehensive background in the mathematical topics required for learning Quantitative Finance (QF) and Business Analytics and Data Science (BADS) for the rest of the MSBA online program. The mathematical topics covered include Calculus, Linear Algebra, and Probability Theory. Applications of these topics in a variety of business contexts will be included.

MSIS 2503 Database Management Systems – Fundamentals of SQL (2 units)

This course presents technical and managerial approaches to the analysis, design, and management of business data, databases, and database management systems. The topics include structured and unstructured data management, a comparison of relational and object-oriented databases, relational database conceptual and logical design, and database implementation and administration.

MSIS 2507/IDIS 3802 Data Analytics with Python (4 units)

Data science involves the application of scientific methodologies to extract understanding from and make predictions based on data sets from a broad range of sources. Data science involves knowledge and skills from three areas: programming, math/statistics and domain specific expertise. The objective of this course is to teach the programming skills relevant to data science. Students will learn the Python programming language, along with a complete set of open source tools for data science in Python, including the IPython Notebook, NumPy, SciPy, Pandas, matplotlib, scikit-learn and many others. Students will learn skills that cover the various phases of exploratory data analysis: importing data (SQL, web, JSON, CSV), cleaning and transforming data, algorithmic thinking, grouping and aggregation, visualization, time series, and statistical modeling/prediction and communication of results. The course will utilize data from a wide range of sources and will culminate with a final project and presentation.

MKTG 2505 Marketing Analytics (4 units)

Prepares managers to identify the competitive advantages that come from leveraged analytics, apply and implement tools, evaluate advantages and limitations, ask relevant business questions and interpret and communicate the output from tools and models to achieve profitable business decisions.

MSIS 2508: Data Science & Machine Learning with Python (4 Units)

This course introduces participants to quantitative techniques and algorithms that are based on big and small data (numerical and textual). We also analyze theoretical models of big systems for prediction and optimization that are currently being used widely in business. It introduces topics that are often qualitative but that are now amenable to quantitative treatment. The course will prepare participants for more rigorous analysis of large datasets later in other Online MSBA classes, as well as introduce machine learning models and data analytics for business intelligence.

ECON 2509 Econometrics with R (4 units)

Covers the basic conceptual foundations and tools of econometrics and apply them to case studies with real-world data. The key statistical technique used in this course is multiple linear regression and R-programming.

MSIS 2510 Prescriptive Analytics (4 units)

Coming soon!

IDIS 3598 Practicum or Capstone (6 units)

Practicum

Practicum projects are defined by external partners who provide a dataset and the question of interest. Starting from a real-world problem and using input from the partner, students will refine the problem to scope the project, apply analytical tools to generate insights, interpret the findings, summarize the findings in a report, and present them to the partner or faculty supervisor. Practicums occur over two quarters and are worth 2 units of course credit each quarter, or 4 total units. The practicum will serve as a culmination of all of your work throughout the Online Master’s in Business Analytics program.

Capstone

Capstone projects use data and examine analysis questions provided by faculty over the course of one quarter. Students refine the problem to scope the project, apply analytical tools to generate insights, interpret the findings, and summarize the findings in a final report. In certain situations, students with full- or part-time jobs or with an internship can approach their employers to find a suitable project if desired. In those instances, the employer and faculty would provide supervision.

Elective Courses (10 units to be selected from the following)

MSIS 2527 Big Data Modeling and Analytics

This course is about Big Data and its role in carrying out modern business intelligence or actionable insight to address new business needs. This course is a lab led and open source software rooted course. Students will learn the fundamentals of Hadoop framework, NoSQL databases and R Language. The class will focus on storage, process analysis and aspects of Big Data. Students will have access to a MapR Hadoop Image. The image is enhanced by Instructor to include MOngoDB and R.

MSIS 2537 Reinforcement Learning

Reinforcement Learning is introduced as a way to do optimal control in cases when a system model is not available and information about the Value Function is obtained by analyzing its sample paths. RL Algorithms, Temporal Difference Learning, Q-Learning, on-policy and off-policy learning, policy exploration vs exploitation, Deep Learning Neural Networks as function approximators for RL systems, The Deep Q Network (DQN) algorithm, Policy Gradient methods such as the REINFORCE algorithm in combination with Value Function and Policy Gradient methods are explored. Applications of these concepts in the areas of Game Playing systems, Finance and Robotics are also discussed.

MSIS 2528 Applied Cloud Computing

Computing is migrating to the cloud. In this course, you will understand as-a-service concepts by using services from major cloud providers and learn how to deploy and manage cloud infrastructure. This course focuses on hands-on skills required to operate on the three prime cloud service platform from Amazon, Google, and Microsoft. This course will offer an applied perspective on the core features of these platforms such as load balance, auto-scaling, serverless computing, and cloud AI.

MSIS 2529 Dashboards

This course enables you to transform data into persuasive dashboards that effectively inform and guide management actions. Dashboards are persuasive if they motivate actions in an intended audience. Dashboards are effective if they offer comprehensive and reliable information. This course introduces and discusses the fundamental design principles and technology of dashboards and allows you to design, implement, and critique dashboards.

MSIS 2539 Data Visualization

This course enables you to explore data, identify insights, and develop evidence-based arguments using data visualization techniques. Completing this course equips you with a moderate level of data literacy, the ability to interpret, construct and convey arguments through the functional and truthful visual presentation of data. You will wrangle data, customize data visualization technologies, and programmatically develop data visualizations.

MSIS 2513 Database Management Systems - Design, Development & Administration

MSIS 2538 Cloud Computing Architectures

Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. The widespread adoption of hardware virtualization and the availability of low-cost computers and storage devices with high-capacity networks together with service-oriented architecture has led to growth in cloud computing. This course will study what technologies make cloud computing possible and how IT leverage these technologies to make the enterprise computing environment more efficient. There are three parts to this course. The first part will study how hardware virtualization is made possible through computer architecture advancement. The second part will discuss the two main solutions in the virtualization layer which are hypervisor-based virtualization and container-based virtualization. The third part of the course will study the microservices and the containers workflow orchestration. This course includes hands-on labs in virtual machines creation based on different technologies like hypervisors (VMware) and container (Docker). We will also explore different workflow orchestration tools like Docker Swarm and/or Google Kubernetes.

MSIS 2534 Natural Language Processing

This course teaches students the fundamentals of Natural Language Processing (NLP), which will be necessary for the remainder of the Online Master’s in Business Analytics program. NLP has recently found several applications in business. There is now a foundation of content that students who wish to work in this field need to know and this course is aimed at providing students with a conceptual understanding of the field and its business applications, and a technical toolkit to implement NLP models.

MSIS 2536 Deep Learning

Introduction to the topic of Deep Learning Neural Networks (DLNs), Linear Learning models using Logistic Regression, and adding hidden layers to create Deep Feed Forward Neural Networks. Detailed algorithms are used to train these networks using Stochastic Gradient Descent and the resulting algorithm called Backprop. Training processes of these networks are used with the Tensor Flow tool and the MNIST and CIFAR-10 image data-sets. Some specialized DLN architectures include the following: (a) Convolutional Neural Networks (ConvNets), (b) Recurrent Neural Networks (RNNs), (c) Reinforcement Learning. Model parameter initialization, underfitting and overfitting are discussed as well as techniques such as Regularization. Issues such as the Vanishing Gradient problem that often cause problems during training are also discussed.

FNCE 2409 Econometrics (2 units)

This course introduces a broad set of econometric tools to analyze large-scale, real-world company data to make data-driven business decisions. Topics include the ordinary least squares (OLS), model selection, generalized least squares (GLS), instrumental-variables regression, quantile regression, count data models, binary outcome models, and selection models.

FNCE 2526/FNCE 2426 FinTech

Coming soon!

FNCE2524/FNCE2404: Time Series Analysis Forecasting

This course is designed to provide a comprehensive introduction to forecasting methods used in time series analysis. The class covers a range of topics in time series forecasting. The class will provide you with a language to describe time series data and ultimately cover modeling techniques such as ARIMA, SARIMA, and GARCH to produce forecasts.

FNCE2525/FNCE2408: Analytics of Finance

This course covers key issues in panel data analysis, with an emphasis on their applications in empirical research, especially empirical corporate finance. The course aims to introduce various econometric methods for analyzing panel data and develop core techniques to identify causal relations in the data. We will begin with the standard linear regressions, and extend to pooled, fixed effect, and random effect regression models; instrumental variables; differences-in-differences; selection models; and regression discontinuity. Students will be exposed to a broad range of applications in finance through reading academic papers and conducting their own empirical analysis.

ECON 3000: Managerial Economics

This course will introduce economic foundations for managerial decisions. The course analyzes the economic behavior of individuals and firms and explores how their interactions in markets affect managerial decisions. Basic concepts of market, price elasticity, theory of consumer, and theory of firm will be studied to incorporate economic theories in managerial decision-making. How key managerial decisions are made in different industrial structures will be discussed.