Master of Science in Business Analytics
The Degree of the Future
Essential Skills for a Digital World.
A critical skill in 21st century business is the ability to find opportunity in data. How can you sift through endless quantities of information to uncover critical insights about business performance? How do you spot patterns in data, and decode opportunities using analytic tools? The Rutgers School of Business–Camden Master of Science in Business Analytics can get you started in one of the most sought-after and essential skills for modern business.
This fully-online 30-credit Master’s program will show you how to employ various software programs, such as R, Python, and SQL, to analyze business data.
A 12-credit Business Analytics Certificate program is also available.
The Data on MSBA
The MSBA Program is comprised of ten courses, through which students acquire knowledge of statistics, machine learning techniques, and relevant software, such as:
Think of the MSBA as a degree for the future. Going forward, the edge in business belongs to those who can find managerial insights hidden in customer, firm, and marketplace data. The MSBA program puts you at the vanguard of this emerging field. It will teach you techniques for extracting, collecting, cleaning, describing, segmenting, modeling, predicting, and reporting data.
MSBA students select 10 courses from offerings, such as:
This course develops students’ data extraction, data transformation, data analysis, data interpretation, and data visualization skills. Topics include fraud detection, Benford’s Law, managerial accounting analytics, and financial accounting analytics. This course incorporates a substantial data analytics class project.
Prerequisite: 53:010:505 Non-Credit Financial Accounting Knowledge Seminar (or Undergraduate Level courses 52:010:101 Intro to Financial Accounting and 52:010:305 Intermediate Accounting)
The course provides a comprehensive overview of new financial technologies. Such technologies combine traditional investment practice with the ever-increasing power of computation to facilitate the achievement of highly customized objectives. The course covers the rise of big data analytics (Artificial Intelligence and Machine Learning) as well as the rise of automated investment advisers and algorithmic trading. We also discuss the role of the Blockchain in cryptocurrencies, such as Bitcoin and Etherium, and their implications for investment management practice and financial services.
This course provides a broad and practical introduction to the modern methods of Financial Data Analysis. The course emphasizes the use of modern analytical techniques to extract insights from the most commonly used financial data. Using a hands-on approach, students will develop deep practical intuition into the nature of financial returns, bond valuation, and stock pricing. Using a programming language, such as “R,” students will build financial models using a mix of market and accounting information, build optimally weighted portfolios, learn the basics of risk management, and learn simulation techniques, such as Bootstrap and Resampling.
Prerequisite: Foundation of Finance and an assigned LinkedIn Learning course.
This course provides a broad and practical introduction to the modern methods of Investment Management. The course emphasizes the use of big data and modern analytical techniques of Machine Learning and Artificial Intelligence to improve the performance of investment management. We start with the introduction to Python and financial data. We then apply Python and machine learning algorithms to the fundamental topics on investment management, such as bond valuation, stock pricing, derivative pricing, portfolio construction and optimization, international asset allocation, Monte Carlo simulation, and performance measurement.
Prerequisite: Foundation of Finance and an assigned LinkedIn Learning course.
This course will introduce the use of R programming for processing large datasets and applying various statistical learning methods. An emphasis on using data visualization techniques to visualize the data in R and Tableau. Students will learn how to conduct data wrangling, build statistical learning models, and create basic graphs of geographic visualizations. We will work with case studies and data from a variety of open data sites and other sources. Students will get a chance to practice using large data sets that contain approximately a million records during the semester.
Information technology (IT) is an important driver and enabler of the dramatic transformation of the business landscape. This course is designed to provide concepts and framework to develop technology strategy for supporting corporate strategy. The course also introduces traditional and agile project management skills for successful selection, planning, and monitoring of projects. Case studies and hands-on assignments reinforce concepts, which students can directly apply in their work environment.
This course focuses on the design and management of the data resources of an organization and the extraction of business intelligence from the data for managerial decision making. The basic concepts and techniques of data management and mining data will be examined with real-world examples and cases to place these techniques in proper context. The course delivers adequate technical detail with hands-on training, while emphasizing the interpretation, organizational and implementation issues relevant to managers.
Prerequisite: 53:716:502 Business Analytics
Use of customer databases to develop classification models with extensive use of the SPSS software package includes a comparison of traditional RFM (regency/frequency/monetary) versus other more advanced approaches, such as logistic regression and decision trees, in maximizing the profitability of marketing campaigns. Students will learn how to build models that predict buyer behavior, as well as assess and improve model performance. Note: Students must purchase SPSS Premium software package from Rutgers software portal (approximately $100).
This course covers development and management of digital marketing strategy, and the uses of digital media technology, including: social, mobile, and web to enhance customer equity, brand value, and ROI within the framework of an organization’s overall marketing strategy.
This course prepares students to develop the analytical skills marketers require to monitor, grow, and sustain competitive advantage. Students will develop abilities in aligning business objectives with metrics; utilizing data visualization, modeling, and text mining techniques; analyzing quantitative and qualitative data; and drawing data-driven consumer insights. The applications will emphasize the use of analytics to help make strategic marketing decisions.
This course explores how to use social media marketing to achieve strategic marketing goals. Using a mix of theoretical and practical exercises, students will learn to deploy social media as a strategic marketing asset. Objectives include learning and applying social media principles and evaluating how an organization’s social media presence adds strategic value. Students will also learn to implement a social media plan, connecting strategic goals to tactical objectives and the social media tools used to listen to and engage with consumers. The course also provides the skills needed to manage and measure social media activity.
mining toolkits. The first part of the course focuses on strategic issues as well as learning basics on how to prepare text data for analyses. We will begin by framing text analytics questions, understanding sources of text data, and generating preliminary insights by coding text data. We will learn how to encode and preprocess text data for automated text analyses. Then the course proceeds to focus on automated text analyses. We will cover explorative text analytics using word analyses (including word frequency analysis, keyword analyses, and text parsing), text visualization, and topic modeling. The last part of the course focuses on text classification and predictions, for example, understanding customer opinions from review comments with sentiment analysis. There are different methods for text classification and predictions, ranging from the very simple to the very sophisticated. We will cover unsupervised dictionary approach as well as more advanced supervised machine learning approach.
Prerequisite: 53:716:502 Business Analytics.
Analytic competency is becoming tremendously important in the business world and is often the factor that distinguishes leading firms in any industry. This course is intended to provide an introductory overview of how firms implement data-driven decision-making. Students will learn statistical concepts, use spreadsheet modeling, and learn through a mix of lectures, cases, and class discussion. Students are required to have a functioning computer with Microsoft Excel installed. Within Excel, you must have DATA ANALYSIS and SOLVER functionality. The course’s primary goal is to coach students on “fact-based decision making” and enable them to carefully plan and run “business experiments” to make informed managerial decisions.
This course aims to (1) familiarize students with the major operational issues confronting managers, and (2) provide students with concepts, insights, and tools to deal with these issues. Topics include inventory management, capacity planning, forecasting, quality management, lean systems, supply chain management and logistics. Proficiency requirement: Excel for Business Executives.
The focus of the course will be to introduce basic concepts in machine learning and data-analytic thinking to students, with an applied business orientation. Students will understand how to use data to competitive advantage and to build and evaluate models for decision-making. Companies today have access to vast amounts of data from their business operations. Data Science is the craft of extracting patterns from this data and using available information for competitive advantage. This course represents an introduction to data science and data analytic thinking. Students will learn to leverage data to answer business questions relating to classification tasks (e.g., will this credit card prospect default or not?), prediction (e.g., how much will this customer spend/year?) and similarity profiling (what do my most profitable customers look like?). Note: Students must be comfortable installing packages independently and navigating in a computing environment. Important: The course assumes the student already has some basic familiarity with the Python programming language as well as a working knowledge of Jupyter notebooks.
This course illustrates how the field of data analytics can be applied to optimally manage supply chains. Students learn to apply data driven decision making methodology to the field of Supply Chain management. Topics encompass all portions of a supply chain, including sourcing, procuring, buying, making, moving, and selling. Topics include designing and planning supply chains, transportation analysis, facility and warehouse location models, demand and inventory management, and supply chain risk analytics. Case studies and hands-on assignments will introduce students to current business applications and innovative use of these ideas.
Prerequisite: 53 716 502 Business Analytics
In response to COVID-19, the Rutgers School of Business–Camden has decided to waive the GMAT/GRE standardized test score requirement for all graduate program applicants, effective now through Fall 2021.
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Lay the Groundwork for a Career in Demand
What can you do with a Rutgers School of Business–Camden MSBA degree? Make your mark in one of the most cutting-edge business fields to emerge in the last 20 years. The ability to analyze data using cutting-edge tools puts you at the intersection of business and technology. It can lead to a diverse range of career paths including data analysis, data science, business intelligence, digital marketing, data consultancy, and more.
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