Online Master of Science in Business Analytics
The Online Master of Science in Business Analytics (MSBA) program offers a robust educational experience that teaches the advanced analytics methodologies needed to take student's business careers to the next level. Created by the world-renowned faculty of the LeaveySchool of Business---and expertly curated by an advisory board of industry leaders---our dynamic Online MSBA curriculum delivers the essential principles, practices, and analytical tools to solve modern business challenges and accelerate student's careers. The online version of the program would maintain identical program learning objectives and content as the on-campus program. The curriculum emphasizes the fundamentals of data-driven decision making and the advanced tools to make sense of a vast amount of data. The program prepares students to be professionals in the fast-paced world of big data and artificial Intelligence.
Admissions
Applicants for admission to the Online MS Business Analytics program must have a U.S. bachelor's degree from an accredited college or university or its foreign equivalent before registering in the Leavey School of Business.
Submitting an Application
Applicants for the Online MS Business Analytics program may apply to begin study in the fall and winter quarters. Online MS Business program information and additional details are available here. Admission correspondence also may be sent via email to onlinemsba@scu.edu. Refer to Chapter 2: Admissions for admissions requirements.
Academic Standing
To qualify for the Online MS Business Analytics degree, a student must maintain an overall grade point average (GPA) of at least 3.0 in all work taken in the Leavey School of Business. A grade of C- is considered a minimum passing grade in each course. A grade of F is considered a failing grade, and the units will not be counted toward graduation requirements.
If a student has a cumulative GPA below a 3.0, he/she will be placed on academic probation. A student then has one quarter to raise the GPA to a cumulative 3.0 or they will be dismissed from the program. Students failing required classes may be dismissed immediately if it is mathematically impossible to return to good standing and remain on track to graduate with his/her class.
The administration will contact faculty midway through the term to acquire a status update on academic performance to ensure students are aware of academic resources and tutoring in an attempt to resolve matters before they affect GPA.
If a student has a cumulative GPA below a 3.0 at the end of his/her final quarter and all course requirements have been satisfied, no degree will be awarded until the cumulative GPA is a 3.0 or better through completion of additional graduate course work in the Leavey School of Business.
Student Responsibility
Students enrolled in the Online MS Business Analytics program are required to follow the same policies and procedures as students in the evening MS Business Analytics program. Each student is personally responsible for knowing all of the academic regulations of the graduate business school. This includes, but is not limited to: grading, honor code, leave of absence, withdrawal, and concurrent enrollment policies. Please refer to the Academic Information section, Chapter 4, for additional information.
Academic Information
The Online Master's in Business Analytics consists of six core courses worth 32 total units of credit, plus a further 10 units of elective courses which can be completed over 3 to 5 courses.
Core Prerequisites
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 variables 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
Curriculum Clusters
1. Core Courses (7 courses)
Gives students an understanding of the context in which information systems operate. Students acquire a basic knowledge of business and organizational requirements that enables them to understand how information systems are designed and successfully implemented. Students must complete all of the following courses:
MSIS 2502 - Math for Finance and Analytics with R (4 units)
ECON 2509 - Econometrics w/ R (4 units)
MKTG 2505 - Data Science & Marketing (4 units)
MSIS 2503 - Database Management Systems - Fundamentals of SQL (2 units)
MSIS 2507 - Data Analytics - Python (4 units)
MSIS 2508 - Data Science & Machine Learning (4 units)
MSIS 2510 - Prescriptive Analytics (4 units)
2. Experiential Learning (1 course, choose one of the following)
There are two ways that Online MS Business Analytics students may fulfill the experiential learning program requirement for the program: Practicum or Capstone experiences. Both of these experiential learning projects require students to use real data and to take their classroom learnings and apply them to real problems.
Experiential learning in the Online MS Business Analytics program offers a unique opportunity to connect directly to leading companies and potential employers. Standout organizations serve as practicum partners for the program, including Cisco, Intuit, Credit Suisse, Oracle, Nuveen, Franklin Templeton Investments, and many more.
IDIS 3598-P - Practicum (6 units
IDIS 3598-C - Capstone (6 units)
3. Specialization/Electives (choice of 10 units)
Offers a variety of electives to help students develop capabilities in a specific area. Students must complete nine of the following courses or course units must equal to 10 units total:
MSIS 2513 - Database Management Systems - Design, Development & Administration (2 units)
FNCE 2524 - Time-Series Analysis (2 units)
FNCE 2525 - Analytics of Finance (2 units)
FNCE 2526 - FinTech (2 units)
MSIS 2527 - Big Data Modeling and Analytics (4 units)
MSIS 2528 - Applied The Business of Cloud Computing (2 units)
MSIS 2529 - Dashboards (2 units)
MSIS 2533 - Mobile Payment & Blockchain (2 units)
MSIS 2534 - Internet of Things (2 units)
MSIS 2536 - Deep Learning (4 units)
MSIS 2527 - Reinforcement Learning (2 units)
MSIS 2538 - Cloud Computing Architecture (2 units)
MSIS 2539 - Data Visualization (2 units)
Online MS Business Analytics Graduation Petition Process
In order to graduate, all Online MS Business Analytics students must complete and submit an online Petition to Graduate. The information provided in the petition is used to order and mail the diploma and list names in the SCU Commencement Book. If this data changes after the petition has been submitted, students must re-submit an amended petition. Students failing to do so could be omitted from the commencement book and ceremony.
In order to be eligible to graduate, Online MS Business Analytics students must complete:
All required coursework specific to the year in which they began the program
The required number of units specified to the year in which they began the program The total program with a cumulative GPA of 3.0 or higher
Not have any I or N grades listed on their transcripts
Deadlines to submit a Petition to Graduate are as follows:
June graduation February 1
September graduation May 1
December graduation August 1
March graduation November 1
Students wishing to participate in the June Commencement Ceremony must complete all degree requirements by the end of the Spring quarter.
To Petition to Graduate, please visit the website.
Online MS Business Analytics Curriculum Core:
MSIS 2502. Math for Finance and Analytics with R
The objective of this course is to provide a comprehensive background in the mathematical topics required for learning quantitative finance (QF) and business analytics and data science (BADS). The mathematical topics covered include calculus, linear algebra, and probability theory. Applications of these topics in a variety of business contexts will be learned with R. (4 units)
ECON 2509. Econometrics with R
Covers the basic conceptual foundations and tools of econometrics and applies them to case studies with real-world data. The key statistical technique used in this course is multiple linear regression and R programming. (2 units)
MKTG 2505. Data Science & Marketing
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. (4 units)
MSIS 2503. Database Management Systems - Fundamentals of SQL
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. (2 units)
MSIS 2507. Data Analytics - Python
Data analytics involves the application of scientific methodologies to extract, understand, and make predictions based on data sets from a broad range of sources. Data analytics requires knowledge and skills from three areas: (i) programming, (ii) math/statistics, and (iii) domain specific expertise. (4 units)
MSIS 2508. Data Science & Machine Learning
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 data sets as well as introduce machine learning models and data analytics for business intelligence. (4 units)
MSIS 2510. Prescriptive Analytics
This course helps participants understand the principles of optimization in business
decisions and prepare computer based models from problem descriptions and determine optimal solutions using software tools. This course also prepares participants to interpret solutions to obtain insights regarding sensitivity to inputs, resource constraints, and their profitability impacts. (4 units)
Experiential Learning:
IDIS 3598-P. 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. (6 units)
IDIS 3598-C. 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. (6 units)
MS Business Analytics Curriculum Electives:
MSIS 2513. Database Management Systems - Design, Development & Administration
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. (2 units)
FNCE 2524. Time-Series Analysis
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 students with a language to describe time series data and ultimately cover modeling techniques such as ARIMA, SARIMA, and GARCH to produce forecasts. (2 units)
FNCE 2525. 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 standar 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. (2 units)
FNCE 2526. FinTech
FinTech has rapidly become a prevalent part of our vernacular, and an understanding of the evolution of traditional finance methods is an important part of a Finance major\'s arsenal. This course covers the evolution of traditional finance methods -- namely, the disruptions and innovations that have transformed: (i) how we access capital; (ii) how we allocate or invest capital; (iii) how we settle or transfer capital; and (iv) how we monitor and maintain the integrity of financial institutions and transactions. Prerequisites: FNCE 2452, 3452, 3000 (Financial Management) (4 units)
MSIS 2527. Big Data Modeling and Analytics
Big Data and its role in carrying out modern business intelligence for 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 and analysis aspects of Big Data. Students will have access to a MapR Hadoop image. The image is enhanced by instructor to include MongoDB and R. Prerequisites: MSIS 2506, 2507 and 2503. (4 units)
MSIS 2528. Applied Cloud Computing
Computing is migrating to cloud. In this course, students 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 platforms 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. (2 units)
MSIS 2529. Dashboards
This course enables students 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 students to design, implement, and critique dashboards. Prerequisites: FNCE 2402 & MSIS 2403. (2 units)
MSIS 2801. Mobile Payment and eCommerce Security
This course reviews the advancements in mobile payments, crypto-currency and on-line transaction security and will prepare students to engage in platform and application development for this emerging new market. They will learn the fundamentals of secure chip-cards processing as mandated by Europay, MasterCard and Visa (EMV). Various mobile payment technologies will be discussed in detail with a special focus on the pros and cons of Near Field Communication (NFC), secure element, Host Card Emulation (HCE), Bluetooth, QR codes, tokens and eWallets. The course covers online transaction security risks such as Heartbleed, and fraud prevention methods including multi-level authentication, biometrics, cloud-based security and Fast Identification Online (FIDO). The course concludes with a discussion on the role of crypto-currency and future trends. (2 units)
MSIS 2803. Internet of Things
This course introduces students to the principles underlying the Internet of Things (IoT). It starts with the history of various technologies that have enabled IoT. It will cover types of IoT architectures, sensor technologies, hardware platforms, communication protocols at various IoT stacks, machine-to- machine communication, IPv6-based solutions, the IEEE 802.15.4 standard that governs and defines IoT protocols, the IoT cloud infrastructure, and security and remote management of IoT devices. This course will learn IoT principles learned. Students who would like to take on leadership or managerial roles will find the principles learned in this course very helpful in implementing a unique and effective IoT-based business strategy for their organization. Students will be required to work in teams to design and build a working IoT system. (2 units)
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 Feedforward 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. (4 units)
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. (2 units)
MSIS 2538. Cloud Computing Architecture
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 leverages 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. (2 units)
MSIS 2539. Data Visualization
This course enables students to explore data, identify insights, and develop evidence-based arguments using data visualization techniques. Completing this course equips them with a moderate level of data literacy, the ability to interpret, construct and convey arguments through the functional and truthful visual presentation of data. Students will wrangle data, customize data visualization technologies, and programmatically develop data visualizations. (2 units)