To earn a Business Analytics concentration and MBA, students must complete 16 credits of MBA core curriculum, their corporate residency, and 12 credit hours from the analytics coursework listed below. Full-Time MBA students will also complete 12 credits from a second concentration of their choosing; whereas Full-Time MS in Finance/MBA students will need to complete 24 credits from the finance curriculum. Students in both programs will also complete 15 elective credits—of which 3 must be experiential in nature and 6 will be from courses offered at other schools and colleges at Northeastern to ensure that you graduate prepared to meet the interdisciplinary needs of businesses today.
Offers an intensive study of econometric techniques applied to cross-section, time-series, and panel data. Applies the fundamentals of econometrics to analyzing structural economic models, forecasting, and policy analysis. Computer applications and an empirical research project are an integral part of the course.
ECON 5140 | 4 credits
Introduces key analytics methods for using data through the perspectives of applied statistics and operations analysis. Covers application of these methods to business areas including marketing, supply chain management, and finance. Topics include business-analytic thinking; application of business analytics solutions to business problems; data mining, supervised and unsupervised machine learning; methods for detecting co-occurrences and associations; and achieving and sustaining competitive advantage by using business analytics methods.
MISM 6203 | 3 credits
Covers the leading data practices from early adopters, focusing on innovative information design, data quality, data sharing, and data integration perspectives and methods for managing data and business analytics. Explores how data analytics and management can be strategically implemented to transform a company. Discusses theories and contemporary industry practice, and real-world data and cases are used for discussion and projects. Offers students an opportunity to prepare for problem identification and solution perspectives of data-related projects, gearing up for MISM 6214.
MISM 6213 | 3 credits
Introduces relational database management systems as a class of software systems. Prepares students to be sophisticated users of database management systems. Covers design theory, query language, and performance/tuning issues. Topics include relational algebra, SQL, stored procedures, user-defined functions, cursors, embedded SQL programs, client-server interfaces, entity-relationship diagrams, normalization, B-trees, concurrency, transactions, database security, constraints, object-relational DBMSs, and specialized engines such as spatial, text, XML conversion, and time series. Includes exercises using a commercial relational or object-relational database management system.
CS 5200 | 4 credits
Examines data mining perspectives and methods in a business context. Introduces the theoretical foundations for major data mining methods and studies how to select and use the appropriate data mining method and the major advantages for each. Students use contemporary data mining software applications and practice basic programming skills. Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling.
MISM 6212 | 3 credits
Introduces how to measure, analyze, and evaluate the profit impact of marketing actions (MAP) by bringing together marketing, strategy, and finance. Your organization is going to spend millions on a new marketing or strategic initiative, but how will you know if it is working? Marketing performance measurement and feedback systems enable managers to take smarter risks by assessing experimental projects and forecasting the profit potential of bigger, bolder initiatives. Offers students an opportunity to explore systems that summarize marketing productivity and suggest steps for performance improvement in marketing strategy and tactics.
MKTG 6230 | 3 credits
Introduces the fundamental problems, theories, and algorithms of the artificial intelligence field. Topics include heuristic search and game trees, knowledge representation using predicate calculus, automated deduction and its applications, problem solving and planning, and introduction to machine learning. Required course work includes the creation of working programs that solve problems, reason logically, and/or improve their own performance using techniques presented in the course. Requires experience in Java programming.
CS 5100 | 4 credits
Introduces the systematic use of visualization techniques for supporting the discovery of new information as well as the effective presentation of known facts. Based on principles from art, graphic design, perceptual psychology, and rhetoric, offers students an opportunity to learn how to successfully choose appropriate visual languages for representing various kinds of data to support insights relevant to the user’s goals. Covers visual data mining techniques and algorithms for supporting the knowledge-discovery process; principles of visual perception and color theory for revealing patterns in data, semiotics, and the epistemology of visual representation; narrative strategies for communicating and presenting information and evidence; and the critical evaluation and critique of data visualizations. Requires proficiency in R.
PPUA 5302 | 4 credits
Introduces design principles for creating meaningful displays of information to support effective business decision making. Studies how to collect and process data; create interactive visualizations; and use them to demonstrate or provide insight into a problem, situation, or phenomenon. Introduces methods to critique visualizations along with ways to identify design principles that make good visualizations effective. Discusses the challenges of making data understandable across a wide range of audiences. Provides an overview of data visualization, key design principles and techniques for visualizing data, and the fundamentals of communication that are required for effective data presentation. Other topics may include ethical uses of information displays, storytelling, infographics, immersive visualizations, and information dashboard design. Offers students an opportunity to use one or more software tools.
MISM 6210 | 3 credits
Studies the importance of using an analytical approach to support marketing decision making in organizations and offers students an opportunity to learn how to implement such an approach in practice. Focuses on data science in marketing: identifying and acquiring the right data for addressing different marketing challenges, building skills necessary for conducting relevant quantitative analyses, and guiding how to use obtained insights to make better marketing decisions. Topics may include product innovation, market identification and segmentation, customer valuation, media attribution models, and assessment of digital and social media. Students are expected to apply statistical concepts and use relevant software packages for analyzing marketing datasets.
MKTG 6234 | 3 credits
Examines how marketers are collecting and using big data and marketing analytics tools on new media and devices to create successful digital marketing strategies. Explores how marketers can benefit from consumer-generated content on social media devices, such as location-based marketing via mobile devices, to reach consumers 24/7. Introduces digital marketing analytics tools and techniques commonly used to conduct market research and analysis. Offers students an opportunity to better understand the impact of devices, such as “wearables,” and recent phenomenon, such as the Internet of Things, on marketing strategies. Investigates privacy and ethical concerns that arise from the collection, analysis, and use of consumer data. Incorporates cases, discussions, readings, lectures, real-life examples, and student research projects and reports.
MKTG 6228 | 3 credits
Designed to develop strategic decision-making skills using the latest analytics capabilities and enabler. Examines the state of the art in analytics capabilities and how these drive supply chains, from marketing to sourcing. Also examines how organizations use analytics to meet their strategic objectives, provide value to the business, and make decisions. Focuses on industry best practices, including studying some of the leading companies.
SCHM 6215 | 3 credits
Introduces how to measure and manage a workforce strategically, including (1) identifying the strategic work that is truly necessary to execute firm strategy; (2) investing in differentiated management systems that support that work; and (3) designing and implementing targeted measurement systems, such as human resources function and workforce scorecards, designed to help to hold line managers accountable for strategic talent. Emphasizes helping students move from a focus on levels associated with a particular workforce attribute (e.g., what is our cost per hire?) to understanding the impact of the workforce on business-level outcomes (e.g., how might an increase in the quality of our project managers affect new product cycle time?).
STRT 6210 | 3 credits