NST1080 Paradigms of Scientific Invest
4 Foundations Liberal Arts Credits

A multidisciplinary examination of the principles of scientific research and routes to discovery with examples from the history of the subject from its Greek beginnings to modern times. The course will provide insight into the sources, motivations, and methods of approach utilized by the developers of modern science. Topics from biology, physics, and engineering will be used to discover how we unravel the mysteries of the natural world and address the question of how do we know what we know is true by critically examining how the science community has resolved conflicting interpretations of the natural world and analyzing the consequent paradigm shifts from previously accepted theories. These concepts will be applied to addressing societal challenges in developing a national science policy, why things go wrong and mitigating man-made disasters. Finally, the real-world utility of these concepts is applied to applications within an entrepreneurship context in terms of evaluating and managing technology ventures.

Prerequisites: None

FIN2520 Personal Finance
4 General CreditsThis course teaches students to negotiate the retail financial landscape, emphasizing issues that have a large impact on their future financial well-being. It assumes no financial knowledge other than first-year finance. The course covers topics such as understanding and appreciating the time value of money, the financial planning process, financing the purchase of a house and other consumer loans, saving for retirement and other goals, selecting a financial advisor, taxes, estate planning, behavioral finance and common investment scams. Specific investment products studied include mutual funds, exchange-traded funds, municipal bonds, alternative investments (including hedge funds, private equity funds, and commodities), annuities, and insurance products. Consideration will be given to the problem of an entrepreneur or start-up employee who has a substantial fraction of personal wealth invested in a single business venture, including evaluating stock-and option-based compensation plans. Over the duration of the course, students will work to develop a personal financial plan.

Prerequisites: SME2021

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.

Prerequisites: AQM1000

SME2021 Finance
3 Intermediate Management CreditsSME finance is designed to develop student understanding of the role of finance in the management of a business venture. Effective financial management, whether performed by the general manager in a small business, or by the finance organization in a large corporation, is necessary if a venture is to succeed and grow. A successful financial manager must have skills, abilities, tools, and a theoretical understanding in many areas, including valuation, financial forecasting, capital budgeting, investor expectations regarding risk and return, the cost of investor supplied capital, and financial strategy. Student skills will be developed in all of these areas in the SME finance stream through readings, lectures, class discussions, exercises, and an analytical project. A successful financial manager must also understand the venture's economic environment, its products, services, and market position, its operational capabilities, and its organizational behavior characteristics. The SME finance stream will link financial management analysis and decisions to these other critical functional areas, so the student will understand its part in achieving overall success for the venture.

Prerequisites: ACC1000 and QTM1000

FIN2000 Finance

4 Intermediate Management Credits

**Students who took SME2021 cannot take this course as they are equivalent.**

FIN 2000 Principles of Finance helps students understand the role of Finance in the management of business ventures and in their daily lives. Effective financial management, whether performed by the general manager in a small business, or by the finance organization in a large corporation, is necessary for ventures to succeed and grow. A successful financial manager must have skills, tools, and perspectives in many areas, including valuation of stocks and bonds, capital budgeting, investment risk and return, the cost of investor-supplied capital, and capital structure. A successful financial manager also must appreciate the key characteristics of a venture - including its products, services, market position, and purpose - and the economic and social environment in which the venture operates. Accordingly, this course links financial analysis and decision-making to critical contextual factors, allowing students to understand the part played by Finance in the overall impact of ventures. Throughout the topic coverage, connections between managerial Finance and personal Finance are recognized and explored, facilitating development of essential financial literacy capabilities. Competency will be developed through readings, lectures, class discussions, and exercises.

Prerequisites: ACC1000 AND AQM1000

FIN3504 Private Equity

4 Advanced Management Credits

This course will provide students with the opportunity to develop a practical understanding of the private equity industry and related topics generally in the venture capital industry. The course will focus on various phases of activity including fund organization, prospecting, valuation, LBO modeling, negotiating skills and exits. The course will also provide students with an understanding of careers in private equity and related industries. The course is designed to enable students to practically apply financial theory in a way that is consistent with industry practices, techniques and professional expectations. As such, the class will utilize case studies involving real companies and will have a heavy emphasis on the practical financial research skills relevant to private equity, and how industry-specific issues are addressed by private equity professionals.

Prerequisites: Any FIN3000 level course

QTM3675 Probability for Risk Management

4 CreditsThe fundamental objective of this course is to prepare students for the successful completion of the first level probability examination (Exam P) of the Society of Actuaries. While the necessary theory is addressed, this course focuses on problem solving, so it is well suited for any student with an interest in applied probability concepts and how they are related to a wide variety of situations within and beyond actuarial science, finance, and economics. Topics include general probability and univariate and multivariate probability distributions.

Prerequisites: QTM1010 or AQM 2000

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

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.

Prerequisites: None

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