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QTM2000 Case Studies in Business Analytics
4 Intermediate Liberal Arts CreditsThis course builds on the modeling skills acquired in the QTM core with special emphasis on case studies in Business Analytics - the science of iterative exploration of data that can be used to gain insights and optimize business processes. Data visualization and predictive analytics techniques are used to investigate the relationships between items of interest to improve the understanding of complex managerial models with sometimes large data sets to aid decision-making. These techniques and methods are introduced with widely used commercial statistical packages for data mining and predictive analytics, in the context of real-world applications from diverse business areas such as marketing, finance, and operations. Students will gain exposure to a variety of software packages, including R, the most popular open-source package used by analytics practitioners around the world. Topics covered include advanced methods for data visualization, logistic regression, decision tree learning methods, clustering, and association rules. Case studies draw on examples ranging from database marketing to financial forecasting. This course satisfies one of the core requirements towards the new Business Analytics concentration. It may also be used as an advanced liberal arts elective or an elective in the Quantitative Methods or Statistical Modeling concentrations.
Prerequisites: QTM1010 (or QTM2420)
AQM2000 Predictive Business Analytics
4 Foundation Liberal Arts Credits
**This Course is only open to students who started Fall 2021 or after**
This course introduces students to the foundational ideas of modern data science through a hands-on implementation in modern statistical software. Students will encounter key conceptual ideas like the importance of holdout data, the dangers of overfitting, and the most common performance indicators for various model types through a tour of popular and practical predictive analytics algorithms: linear regression, k-nearest neighbors, logistic regression, classification and regression trees, naive Bayes', and others. In addition to these supervised learning models, students will investigate unsupervised learning models like association rules and clustering, which are designed to uncover structure in data rather than predict a particular target. Throughout the course, students will practice communicating the results of their analyses to a variety of stakeholders.
QTM1000 Quantitative Methods for Business Analytics I
4 CreditsThe course introduces the necessary core quantitative methods that are prerequisites to follow-on courses in QTM and in Babson's integrated core business offerings. Statistical software and the use of spreadsheets are integrated throughout so that students better comprehend the importance of using modern technological tools for effective model building and decision-making. About two thirds of the course is data-oriented, exposing students to basic statistical methods, their conceptual underpinning, such as variability and uncertainty, and their use in the real world. Topics include data collection, descriptive statistics, elementary probability rules and distributions, sampling distributions, and basic inference. The last third of the course is dedicated to selected non-statistical quantitative techniques applied to business models. Topics include curve fitting, differential calculus applications to non-linear optimization, and introduction to the time value of money.
OIM3545 Business Intelligence and Data Analytics
Students who took this as MIS3545 cannot register for this course
This course is about how organizations, and their employees can successfully collect, evaluate and apply information to become better decision makers. It starts with basic concepts regarding business data needs and ends with hands-on experience using Business Intelligence (BI) tools. It takes a variety of experts to start and run a business - financial, operational, marketing, accounting, human relations, managerial, etc. Each knowledge base requires up-to-date information to plot strategy or keep it on track. Our ability to capture large volumes of data often outstrips our ability to evaluate and apply the data as management information. These are the challenges we will address in this course so that you can become an intelligent gatherer and user of data in your chosen field.
Prerequisites: SME2012 or OIM2000
QTM1010 Quantitative Methods for Business Analytics II
4 CreditsThis course explores decision-making problems in a managerial context using algebraic, spreadsheet, graphical, and statistical models. The focus is on understanding basic mathematical and modeling principles through the analysis of real data. The course emphasizes communicating in-context interpretations of the results of analysis in written, visual, and oral form. A foundation in introductory statistics and use of spreadsheets is essential because these concepts are extended and reinforced throughout the course. Topics include introductions to linear regression, time series analysis, linear programming, decision analysis and simulation. It emphasizes the use of appropriate software and the latest technological methods for accessing and analyzing data.
Prerequisites: QTM1000 or AQM1000
AQM1000 Foundations of Business Analytics
4 Foundation Liberal Arts Credits
The course introduces the necessary quantitative methods that are prerequisites to follow-on courses in AQM and in Babson's integrated core business offerings. Statistical software and the use of spreadsheets are integrated throughout so that students better appreciate the importance of using modern technological tools for effective model building and decision-making. The initial third of the course focuses on basic frequentist statistical methods, their conceptual underpinning, such as variability and uncertainty, and their use in the real world. Topics include data visualization, data collection, descriptive statistics, elementary probability rules and distributions, sampling distributions, confidence intervals, and hypothesis testing. The remainder of the course is dedicated to decision-making problems in a managerial context using algebraic, spreadsheet, graphical, and statistical models. Topics include introductions to linear regression, time series analysis, and simulation. The course emphasizes the effective communication of quantitative results through written, visual, and oral means.
QTM2623 Programming with R for Business Analytics
4 CreditsThis course provides experience in developing, testing, and implementing business analytics software using the R language. R has become the leading tool for analytics software design, statistical computing, and graphics. The language is greatly enhanced by numerous open-source contributed packages and textbooks submitted by users, and it is used almost exclusively in most of the leading-edge analytics applications, such as statistical analysis and data mining. No prior programming experience is assumed. Students will become proficient in programming in the R language with datasets of all kinds with an emphasis on statistical exploration, data mining, graphics, and advanced programming concepts. The course will be case-oriented. The intent is to further enhance the learning experience from other analytics courses, such as QTM1010 and QTM2000.
Prerequisites: QTM1010 and QTM2000 or permission from the instructor
ACC3536 Accounting Analytics
4 Advanced Management CreditsStudents who have taken ACC3545 cannot take this course and vise versa
Data and analytics are being used to assist businesses in becoming more efficient and effective in their decision-making process. This course will improve your ability to critically analyze data in order to make better business decisions and to communicate this information effectively to your audience. Students will learn how to use analytics tools from the lens of a manager, a financial statement user, a tax analyst, an auditor, and a forensic accountant. The course will introduce you to various analytics software products, and provide an opportunity to interact with professionals in the field.
Prerequisites: Junior or Senior Class standing
MKT4506 Marketing Analytics
4 Advanced Management CreditsToday's marketers have access to more data and technology than ever before. To fully realize the benefit of these resources, marketers need to develop data analysis and analytical skills to convert raw data into insights and insights into more informed marketing decision-making. The objective of this course is to introduce the benefits of using a systematic and analytical approach to marketing decision-making. This course integrates marketing concepts with practice, and emphasizes _learning by doing._ Students will learn different ways to explore the relationships and patterns in customer and marketing data. Advanced analytical software will be used to perform many of the most commonly used descriptive and predictive analysis techniques that are applied in the marketing field.
The course builds on the marketing core course(s) through the direct application of marketing concepts such as segmentation, targeting and brand positioning. The course emphasizes the application of marketing analytics to a diverse set of business problems. This includes the use of marketing analytics to identify opportunities to cost-effectively acquire new customers, increase the value and loyalty of existing customers, and to improve the overall experience the customer has with a brand. It also includes the use of analytics to set up marketing experiments, assess the value of different product strategies and measure the ROI of marketing campaigns.
Prerequisites: SME2011 or MKT2000
COM3522 Business Writing
4 Advanced Management Credits
Business Writing is an interdisciplinary writing course designed to improve the business communication competency of undergraduate students. In this course students will gain the tools necessary to produce effective business writing in a variety of multi-modal contexts. Students will read, discuss, and respond to materials that provide historical context for business communication norms and genres and present research-driven strategies for communicating effectively to a variety of audiences. Students will complete practice cases where they will be expected to apply a problem-solving approach to producing audience-driven, goal-oriented business communication genres. These cases will build toward a larger service learning project with an external partner in order to deepen their understanding of business norms and practices.