Master of Science in Business Analytics
The Master of Science in Business Analytics is a STEM qualified program that provides students with data analytics knowledge, tools and skills to become data-savvy managers. The program can be taken in one year (fall, spring, and summer semesters) as a full-time program or as a part-time program over two years. Classes are held at the LMU Playa Vista Campus in the heart of Silicon Beach, providing students great opportunities to engage with the local business community.
Students learn about all areas related to business analytics and big data analysis including data management, modeling, programming, analysis, visualization, data-mining, machine learning, and integration strategies to analyze large, structured and unstructured datasets for making effective business decisions. Students will also develop their problem-framing, teamwork, project management, and communication skills for managing business analytics projects in an organization through the program's capstone experience.
Program Overview (click to view)
Designed for college graduates with a statistics/quantitative background, such as engineering, science, computer science, economics and/or business, this high-quality, hands-on, comprehensive interdisciplinary Master of Science in Business Analytics program provides individuals with the knowledge, tools, and skills to analyze data to make effective business decisions. In addition, the program empowers students with soft skills needed to manage business analytics projects including problem-framing, teamwork, project management and communication. Courses are held in a face-to-face format in small cohorts of approximately 25 students at the LMU Playa Vista Campus. The program can be taken in one year (fall, spring, and summer semesters) as a full-time program or as a part-time program over two years.
The Master of Science in Business Analytics requires 30 - 36 credit hours across three semesters, starting in the fall semester and ending the following August. All academic requirements must be completed in residence. Business work experience is not required for this program. The curriculum consists of eight required courses, two electives, a capstone project and two capstone preparatory mini-courses. Students with approval from the program director may waive up to six credit hours, associated with the two required business foundation core courses, depending on previous business related course work at the undergraduate level.
The capstone project is an experiential learning assignment built around student teams providing business analytics solutions to real businesses under the guidance of on-site corporate managers and faculty members. The project may cover a wide range of industries in the Los Angeles area, with a focus on the Silicon Beach community, including retail, technology, manufacturing, banking, healthcare, nonprofit and hospitality.
Electives are designed to support three specialty tracks - Marketing Analytics, Healthcare Analytics and Data Analytics. The Data Analytics track helps students develop more expertise in general mathematical and analytical tools and to identify a capstone project in other industries, such as nonprofit, accounting or operations.
LMU’s Master of Science in Business Analytics is a STEM (science, technology, engineering and math) designated degree program.
Admission / Application Requirements
Requirements for the Masters of Science in Business Analytics and a completed online application for the program consists of the following:
- Application (Apply online here. Note: You will be redirected to LMU Graduate Admission.)
- Completion of a bachelor's degree from an accredited U.S. institution or the equivalent of a U.S. bachelor's degree from a foreign institution.
- Undergraduate grade point average over 3.0.
- Completion of a college statistics course in last six years with a grade of B or better.
- No work experience required.
- $50 non-refundable application fee.
- Official GMAT or GRE score. Submit your highest GMAT score for consideration. (No waivers granted.)
- GMAT scores are sent to colleges/institutions within 20 days of your test date.
- Have ETS send your GMAT score to LMU, GMAT Institution Code: MSX-XN-89.
- You may take the GRE in place of the GMAT. Send GRE score directly to LMU, GRE code is 4403.
- LMU may consider GMAT scores that are below average if other application submissions indicate a strong performance profile.
- International applicants who attended high school in a foreign country where English was not the primary language must submit a TOEFL score or IELTS score. Exceptions to this rule may be made for students from countries where English is one of several official languages, as well as at the discretion of the program director. Have ETS send your scores to LMU, Institution Code 4403. You must score:
- 80 or above on TOEFL (iBT only)
- The minimum IELTS score is 6.0
Note: Additional assessment of international students oral and written English proficiency (e.g., Skype Interviews) may be administered.
- One official sealed copy of all transcripts supplied by all schools you attended. This includes any coursework done at community colleges, junior colleges, extension programs, post-secondary schools and other institutions.
- International applicants who have completed their postsecondary education from an institution outside the U.S. must hold a degree from a university recognized by the Ministry of Education as a degree granting institution.
- Transcripts from an international institution must be translated and evaluated by a U.S. transcript evaluation service (e.g. Global Services Associates, Educational Credential Evaluators, International Education Research Foundation or World Education Services). The purpose of the evaluation is to verify that your postsecondary degree is the equivalent of a U.S. Bachelor’s degree.
- Request official transcripts be sent directly to:
BusinessCAS Transcript Processing Center
P.O. Box 9221
Watertown, MA 02471
Personal statement (1-2 pages)
- Two academic and/or professional letters of recommendation.
- Letters must not be more than a year old since the date they were issued.
- This requirement is waived for LMU graduates with minimum GPA of 3.5.
Additional information for international graduate students can be found here.
Fall 2020 Application Deadlines
All applications will be considered on a first-come, first-served and space-available basis.
Completed Application Deadlines
Notification By Completed Application Deadlines
Notification By Round 1 January 10, 2020 March 2, 2020 January 10, 2020 March 2, 2020 Round 2 March 10, 2020 May 15, 2020 March 10, 2020 May 15, 2020 Round 3 May 31, 2020 June 30, 2020 May 15, 2020 June 15, 2020
We will continue to accept applications after May 31, 2020 until all spaces are full.
Required Core Courses
Fundamentals of Business: Accounting, Finance & Operations
The fundamental principles of financial reporting, managerial accounting, financial planning, and operations and supply chain management are studied. This course provides a business perspective that focuses on cross-functional decision-making. Students will learn the role of each function in organizations as well as how information flows between different business functions. Students will use Excel to create balanced scorecards that provide a comprehensive view of a business by focusing on the operational and developmental performance of the organization as well as its financial measures.
Marketing for Managers
This course emphasizes the role of marketing and marketing management in society. Basic controllable variables essential to marketing success will be examined including market analysis, product development, pricing, distribution, and promotion. We will explore how marketing facilitates business strategy, discovers and creates demand for products/services, and influences product development.
Programming for Data Management
This course introduces learners to Python programming for data analytics. It introduces the basics of programming (algorithms, variables and data types, operators, looping and branching) and provides a working knowledge of Python libraries to process data. It includes how to retrieve, clean, manipulate, and analyze structured and unstructured data. Students will also be introduced to the basics of data management architecture such as relational databases and data warehouses, as well as use of SQL within Python for querying and interacting with such data architectures.
Data, Models and Decisions for Analytics
The course introduces students to the process of understanding, displaying, visualizing and transforming data into insight in order to help managerial decision makers make better, more informed, data-driven decisions. The course provides a basic introduction to descriptive analytics, including visualization, predictive analytics, and preliminary exposure to some aspects of prescriptive analytics. The approach taken by the course is very practical and applied: hands-on learning is the central focus of the course. For each topic, a case/problem analysis will require the use of Excel and other specialized analytics and decision-making software.
Customer Relationship Management Analytics
Customer relationship management (CRM) is a business strategy paradigm that is focused on the systematic development of ongoing, collaborative customer relationships as a key source of sustainable competitive advantage. CRM represents a fundamental change in approach from traditional marketing; the goals shift from market share to share of customer. Operating under the assumption that competitive advantage is often gained through building customer equity, this course introduces the theory and practical implementation of customer relationship management strategies using customer databases. CRM Strategy Topics include: fundamentals of CRM strategy, customer profiling, measuring customer life-time value, customer profitability analysis, customer loyalty programs, and CRM technology overview. CRM Analytic Topics include: modeling customer lifetime value with linear regression, logistic regression for churn prevention, modeling time to reorder with survival analyses, association rules for market basket analyses, and customer profitability analyses. Students will be introduced to R programming and Excel-based analytic tools.
Data Management for Business Intelligence
Current management practices place an increasing dependence on the use of information to manage a business – business intelligence systems and analytics tools play a critical role in this regard. To help managerial decision makers do their job effectively, it is necessary to understand the decision making process, the nature of data/information used in the decision making process and the role of information technology (in particular, business intelligence technologies) in that process. Data plays a significant role in creating a robust and reliable business intelligence system. This course focuses on various data wrangling tools and techniques that teach how to collect, store and clean data. We will focus on using various business analytics tools for extracting, transforming and loading data into an “analytics ready” data format. Students will also learn about different data storage architectures, such as relational and non-relational databases and data warehouses, as well as Big Data architecture and management of Big Data.
Introduction to Machine Learning
This course will provide students a hands-on application oriented exposure to machine learning (ML), while taking a deep dive into the fundamentals of supervised and unsupervised machine learning algorithms, model selection, feature engineering, data fitting, model evaluation and optimization. Students will also learn how to instantiate, test, and deploy ML models using platforms such as Azure ML and Python libraries using real life data sets. Finally, students will develop the skills to interpret ML based predictive models to support business decision making.
Strategic Analytics Integration
This course integrates concepts, tools, methods, and applications of modeling and strategic decision-making in business. Students will develop a working knowledge of quantitative data-driven decision making approaches, such as perceptual mapping, choice models, optimization, regression, cluster analysis, conjoint analysis, and diffusion modeling. This course is aimed at providing students, as future managers and/or data scientists, with the set of tools and skills needed to make intelligent and critical use of data in systematic decision making.
Required Capstone Classes
Statistics Bootcamp and Capstone Project Preparation
This is a mandatory workshop series to establish a baseline in statistics fluency and to help students prepare for the Summer Capstone Project. A portion of the workshops serves as a refresher for basic statistics concepts required in order to understand the program material. Other workshops cover how to identify and frame a business problem with the objective of delivering measurable business value and how to perform effectively as a team. Teams will be formed for the Summer Capstone Project and matched with industry clients so that teams can start working on identifying and framing their client’s business problem.
Capstone Project Preparation II: Research Design, Project Management, and People Dynamics
This is a mandatory workshop series covering the research design process to facilitate the Summer Capstone Project's approach to the identified business problem. Throughout the workshops, students will have the opportunity to learn about and practice the various interactions between the project team members, stakeholders, and clients. One of the workshops will cover experimental design. Teams will then start to collect data for their project and learn how to take into account ethical considerations when dealing with the data.
Capstone Project I
Students will practice team-oriented problem-solving skills in the context of undertaking and completing a live business analytics project. They will demonstrate their knowledge and understanding of business concepts and analytics techniques in identifying and structuring a problem, collecting and managing data and applying analytic modeling techniques to provide insights and recommendations for the project. The course will enable students to acquire and demonstrate their understanding, use, and proficiency in project management skills related to tackling business analytics projects as they work with a real client.
Capstone Project II
Students will continue to acquire and demonstrate their understanding, use, and proficiency in project management skills as they work on the live business analytics project started in Capstone Project I. Students will practice their written and oral communication skills as well as their ability to generate insights through data visualization techniques in the write-up and presentation of their projects.
Data Visualization and Geographic Information Systems
A picture is worth a thousand words, and a map is worth a thousand pictures. This course is intended to equip students with principles, skills, tools, and techniques in data visualization to be able to tell a story through data visually. Students will be able to uncover relationships between data in exploratory data analysis through visualization and present meaningful and interactive reports to non-technical persons, managers, and executives. In addition, students will dive into the use of Geographic Information Systems (GIS), where students can explore data spatially. Topics include data visualization design principles, exploratory data analysis through visualization, interactive dashboard creation, introduction to GIS tools and techniques, and hot spot analysis.
Text-Mining and Social Media Analytics
This course illustrates the functionalities of text mining and analytics as a business decision-making tool by using a variety of statistical methods to collect and analyze text data. Computational linguists have developed a research stream of understanding and analyzing text. Consequently, business organizations are acquiring knowledge on techniques of text analytics to make a better decision. Due to a large pool of unstructured text entangled in social media, leveraging effective text analytic method is the next leading edge. This course encompasses the fundamentals of computational linguistics that will include some technical features but will mostly emphasize the business application of text analytics. Business and industry cases are used to demonstrate the usefulness and effectiveness of the text analytics techniques used. Students use software to perform computational studies, obtain solutions, and analyze the results. This course also examines what business decisions can be promoted by text analytics as well as effective techniques for rapidly solving the business problems.
Marketing analytics refers to the techniques, practices, and processes of analyzing data related to markets and customers for deeper insights and better decisions. The focus of this course is to facilitate the students to possess the right skills to participate in the cooperative ecosystem of marketing analytics. This includes obtaining contextual knowledge (students will be able to employ a systematic framework to obtain contextual knowledge from industrial practitioners before they start to collect and analyze data); applying proper quantitative methods (students will be able to design proper sampling strategy and choose suitable methods for various problems and types of data); communicating insights (students can translate the analytical results into business insights and communicate the insights to their audiences like managers, customers, policymakers effectively). Specific modules of this course include introduction to marketing analytics, customer segmentation, customer life-time value, promotion and advertising, user generated contents and social media analytics, sales force analytics, sampling and experiment design, confounding factors in analytics, communication of marketing analytics. Students will practice the methods with R, Excel, and other necessary tools.
The initiation of ARRA by the US Federal government in 2009 has led to a significant impact in the Health Information Technology (HIT). One of the areas where the impact is distinctly visible is digitization of health records and its widespread adoption. Enterprise scale health information management software suites have led to organized capture, storage, and distribution of healthcare data in electronic form, making the healthcare vertical ripe and ready for analytic applications. The advances of modern data analytics, when combined with the HIT has already started demonstrating a potential of fundamentally changing the paradigm of disease diagnostics, medical decision making, and patient management. This introductory graduate level course is designed to provide an integrated perspective of healthcare information systems (EHR/PHR), data analytics, and the healthcare domain. Building on the concepts and vocabulary of these fields, students will carry out research and projects to develop analytics applications using data sets from the healthcare domain. This course will be suitable for students with healthcare domain knowledge, seeking training in data analytics and HIT as well as students with information technology and analytics knowledge seeking training in the healthcare domain.
Click here to view a listing of the distinguished College of Business Administration faculty teaching in the Master of Science in Business Analytics program.
Upon successful completion of the MS in Business Analytics program, graduates will:
- Possess the business foundation necessary to apply business analytic concepts in organizational settings
- Create and manage analytics ready data
- Demonstrate statistical and programming skills required to analyze data
- Be able to select and apply appropriate data modeling tools to provide insight for the analysis of business situations
- Be able to clearly explain information and insights gained from analytic models in a business context
Tuition and Fees
Tuition and Fees
LMU provides an excellent value for a quality Master of Science in Business Analytics program. The total cost of the program is calculated as $1,450 per unit ($43,500 - $52,200) which includes:
- Tuition and fees for all courses
- Dedicated career services
*The university reserves the right to change any fees upon reasonable notice.
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All need-based financial aid and most other aid awards are determined in whole or in part by the information submitted in a Free Application for Federal Student Aid or FAFSA. You can submit your FAFSA and apply for financial aid prior to applying for admission to LMU. The Financial Aid Office recommends applying for aid as soon as possible.
To learn more about completing your FAFSA and financial aid eligibility at LMU, please review:
- Free Application for Federal Student Aid (FAFSA). The CSS Profile is currently not required by LMU for aid application.
- Financial Aid Eligibility
Limited scholarship opportunities are offered to LMU students based on leadership, merit and/or need.
Grants are made available by LMU, the State of California and the Federal Government to help fund your LMU education. The Financial Aid Office considers your eligibility for these based on the information submitted on your FAFSA.
Visit LMU's Financial Aid Office for financial aid application instructions and other important information.
LMU Graduate Business Programs
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