Online Master of Science in Finance and Analytics
The Online MSFA program helps students develop the in-depth knowledge needed to excel in a management or C-suite role in the financial sector, or in a finance leadership role in any organization. Students will learn the intricacies of corporate finance, investments, and analytical tools as they build a rock-solid foundation of finance acumen.
Submitting an Application
Applicants for the Online MS Finance and Analytics program may apply to begin study in the fall and spring quarters. Online MS Finance and Analytics program information and additional details are available here. Admission correspondence also may be sent via email to onlinemsfa@scu.edu. Refer to the Chapter 2: Admissions for admissions requirements.
Academic Standing
To remain in good academic standing, a student in the Online MS Finance and Analytics program 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 Finance and Analytics program are required to follow the same policies and procedures as students in the evening MS Finance and 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 of Science in Finance and Analytics consists of six core courses worth 24 total units of credit, plus a further 12 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 Finance and 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 (6 courses)
Gives students an understanding of the context in which finance operates. Students acquire a basic knowledge of finance and analytics that enable them to make intelligent business decisions. Students must complete all of the following courses:
FNCE 2400 - Corporate Finance and Financial Analysis (4 units)
MSIS 2402 - Math for Finance and Analytics with R (4 units)
MSIS 2403 - Database Management Systems (2 units)
FNCE 2409 - Econometrics (2 units)
IDIS 3802 (MSIS 2407) - Data Analytics with Python (4 units)
FNCE 3491 (FNCE 2405) - Investments (4 units)
2. Experiential Learning (1 course, choose one of the following)
There are two ways that Online MS Finance and 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-world problems.
Experiential learning in the Online MS Finance and 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 (4 units)
IDIS 3598-C - Capstone (4 units)
3. Specialization/Electives (choice of of 12 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 12 units total:
FNCE 2404 - Introduction to Time Series Forecasting (2 units)
FNCE 2408 - Analytics in Finance (2 units)
FNCE 3460 - Mergers, Acquisitions, and Corporate Restructuring (4 units)
FNCE 3807 - Intro to FinTech (2 units)
FNCE 3484 - Financial Engineering (4 units)
FNCE 3482 - Business Valuation (4 units)
MKTG 3597 - Marketing Analytics (4 units)
MSIS 2431 - Machine Learning (4 units)
Finance Forward
In the Online MSFA program, students will learn more than just a new set of leading-edge finance and analytics skills. Our program theme of "Finance Forward" ensures students develop the clarity to select the correct tool for the task at hand and the confidence to deploy it expertly. In each course, students will be presented with a complex business challenge which they will work to overcome with the use of a new analytical approach or method. At each course's end, students will record a reflection that will be compiled into an ePortfolio to be presented at the end of the program. In doing so, they'll develop a holistic sense of how these tools fit together and the broad strategic sensibility to succeed in Silicon Valley and beyond.
Online MS Finance and Analytics Graduation Petition Process
In order to graduate, all Online MS Finance and 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 Finance and 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 Finance and Analytics Curriculum Core:
FNCE 2400. Corporate Finance and Financial Analysis
The objective of this course is to provide a foundation in the basic concepts of corporate finance, particularly the role of the financial manager and the goal of financial management. For this purpose, the course focuses on agency conflicts, business ethics and corporate governance, capital structure, payout policy, financial distress, options (real and executive), derivatives/hedging, and international issues. The application of these techniques has gone beyond the simple corporate budgeting context and has extended to mergers and acquisitions (M&A), private equity transactions such as leveraged buyouts (LBOs), investment banking, and commercial real estate and infrastructure transactions. (4 units)
MSIS 2402. 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)
MSIS 2403. Database Management Systems
This course introduces database management and database management systems (DBMS). Teaches technical and managerial skills in database planning, analysis, logical design, physical design, implementation, and maintenance. Features hands-on training in database design, development, and implementation using relational DBMS software. Emphasizes designing and developing reliable databases to support organizational management. (2 units)
FNCE 2409. Econometrics
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. (2 units)
IDIS 3802 (MSIS 2407). Data Analytics with 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. The objective of this course is to teach the programming skills relevant to data science. Students will learn to use a complete set of open source tools for data science in Python, including the Jupyter Notebook, NumPy, Pandas, Seaborn, scikit-learn, Colab, and many others. Students will learn skills that cover the various phases of exploratory data analysis: importing data, cleaning and transforming data, algorithmic thinking, grouping, aggregation, reshaping, visualization, time series, statistical modeling, and data exploration and communication of results. The course will utilize data from a wide range of sources and will culminate with a final project and presentation. (4 units)
FNCE 3491 (FNCE 2405). Investments
This course explains the foundation blocks of the investments industry, key stakeholders in the industry and drivers for their actions including any ethical aspects, the evolution of the industry, its growth in the global setting, regulations, the industry's current state, and key trends likely to shape the future. It explains rational and normal behavior, standard and behavioral portfolios, standard and behavioral life-cycles of saving and spending, standard and behavioral asset pricing, and standard and behavioral market efficiency. It combines the theoretical underpinnings of finance with real-world examples. Before taking the course, students should understand the time value of money (discounting), capital budgeting, and evaluation of two-stock portfolios. (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. (4 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. (4 units)
Online MS Finance and Analytics Curriculum Electives:
FNCE 2404. Introduction to Time Series 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 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 2408. Analytics in 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. (2 units)
FNCE 3460. Mergers, Acquisitions, and Corporate Restructuring
Examines corporate governance and corporate restructurings. Emphasizes how corporate ownership, control, and organizational structures affect firm value. Other topics include valuing merger candidates, agency theory, and takeover regulation. Places a heavy emphasis on case projects and/or class presentations. (4 units)
FNCE 3807. Intro to 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. (2 units)
FNCE 3728. Alternative Investments | Partnerships and Venture Capital
Alternative investments contrast to widely-held investments like stocks, bonds, and mutual funds. This course covers how these investments are generally structured along with a closer study of a particular category, venture capital. (2 units)
FNCE 3484. Financial Engineering
Examines the design, valuation, and risk management of derivative securities (futures, options, etc.), including structured products. Includes topics on arbitrage theory, futures, equity options, bond options, credit derivatives, swaps, and currency derivatives. Mathematical modeling of derivatives, including implementation and applications in investments, corporate finance, and risk management. (4 units)
FNCE 3482. Business Valuation
Discusses implementing finance theory for valuation problems. Provides practical valuation tools for valuing a company and its securities. Covers valuation techniques including discounted cash-flow analysis, estimated cost of capital, market multiples, free-cash flow, and pro forma models. (4 units)
MKTG 3597. Marketing Analytics
Prepares managers to (1) identify the competitive advantages that come from leveraged analytics, (2) apply the tools and evaluate the advantages and limitations of each, (3) implement these tools and ask relevant business questions that could be solved with them, and (4) interpret the input and communicate the output from such tools and models to achieve more profitable business decisions. (4 units)
MSIS 2431. Machine Learning
This course introduces participants to quantitative techniques and algorithms that are based on big data (numerical and textual) or are theoretical models of big systems or 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)