BazookaCow19/class-info huggingface dataset:
FIN 503: Quantitative Finance II This course covers topics in time series analysis with an emphasis on applications. It is intended to prepare MSF students for more advanced courses in finance. This course provides some basic knowledge of financial time series data. It also introduces models and methods widely used by academics and practitioners. The purpose of this course is to understand proper use and limitations of econometric methods in applied time series analysis. 2 graduate hours. No professional credit. Credit is not given for FIN 503 and FIN 580: Section QM2, (68387). This course covers topics in time series analysis with an emphasis on applications. It is intended to prepare MSF students for more advanced courses in finance. This course provides some basic knowledge of financial time series data. It also introduces models and methods widely used by academics and practitioners. The purpose of this course is to understand proper use and limitations of econometric methods in applied time series analysis. Prerequisite: FIN 580 Section Q1M.
FIN 504: Accounting for Financial Analysis This course will develop an understanding of the most fundamental accounting concepts and provide key building blocks necessary for intermediate and advanced financial statement analysis. It is designed to provide a basic but practical application of financial analyses commonly performed by industry professionals. 2 graduate hours. No professional credit. Credit is not given for FIN 504 and FIN 580 (58811), Section FS.
FIN 511: Investments Introduction to investment analysis, including the theory and implementation of portfolio theory; empirical evidence on the performance of financial assets; evaluation of portfolio investment strategies; and the extension of diversification to international markets. Prerequisite: FIN 520; or MBA 505 - Section G (Finance II); or consent of instructor.
FIN 512: Financial Derivatives Introduction to options, futures, swaps and other derivative securities; examination of institutional aspects of the markets; theories of pricing; discussion of simple as well as complicated trading strategies (arbitrage, hedging, and spread); applications for asset and risk management. 4 graduate hours. No professional credit.
FIN 514: Valuation of Complex Derivative Securities Presents the main ideas and techniques of modern option pricing theory, including: the Black-Scholes-Merton analysis; risk-neutral probabilities and the probabilistic solution; numerical techniques for computing option prices; an introduction to term structure modeling; and perhaps other topics, at the discretion of the instructor. Prerequisite: Prior or concurrent registration in FIN 513 or consent of instructor.
FIN 516: Term Structure Models Coverage of the fundamental models models of the term structure of interest rates, including their implementation, calibration, and use in valuing interest rate derivatives. Focus will be on the Black model and short rate models such as Black-Derman-Toy and Hull-White. 2 graduate hours. No professional credit. Approved for Letter and S/U grading. Prerequisite: FIN 512; IE 525 OR Fin 514 OR FIN 513.
FIN 517: Advanced Term Structure Models This class is a continuation of FIN 516 Term Structure Models. Coverage of advanced term structure models with a focus on the LIBOR Market Model (LMM). Students will learn the theory behind the model, how to calibrate the model to data and how to to develop numerical algorithms in order to implement the model to price a variety of real world interest rate products. 2 graduate hours. No professional credit. Approved for Letter and S/U grading. Prerequisite: FIN 516.
FIN 518: Financial Modeling The objective is to learn the fundamentals and practice building financial models using Microsoft Excel. By the end of the term, each student should be able to develop an understanding of any financial relationship and build that financial relationship into a model using the built-in function of Excel. Financial modeling, by definition, requires significant work outside of the classroom. Models are introduced, demonstrated, and reviewed in class, but each student is expected to research and collect data, and to construct the models, prior to each week's class meeting. Prerequisite: MSF students only.
FIN 521: Advanced Corporate Finance Addresses both the theoretical and applied aspects of firms' financing decisions; topics include capital structure and cost of capital theories; mergers, acquisitions and leveraged buyouts; options, warrants, and convertibles; venture capital and initial public offerings; and pensions. Prerequisite: FIN 520, plus either ECON 506 or BADM 572 or concurrent registration in either course; or MBA 505 - Section G (Finance II); or consent of instructor.
FIN 522: Cases in Financial Strategy Course focuses on financial management cases.  Provides students with an active learning experience. Case work is based on concepts learned in introductory corporate finance. Topics discussed include measuring and interpreting cash flow performance, financial forecasting and turnaround management ; capital investment and cost of capital; and capital structure, dividend policy; and firm valuation. Prerequisite: FIN 520, plus either ECON 506 or BADM 572 or concurrent registration in either course; or MBA 505 - Section G (Finance II); or consent of instructor.
FIN 526: Investment Banking Provides key building blocks necessary for a career in investment banking, valuation and other related fields. It is designed to provide a practical application of financial statement analysis, modeling, valuation, and presentation skills commonly performed by industry professionals. The course has three major parts. The first focuses on the mechanics of financial statement analysis, ratio analysis, and financial model building. The second applies the financial statements and forecasts within the context of company valuation, utilizing common industry techniques. In the third part of the course, we will employ these valuation techniques in common investment-banking deliverables such as pitch books and fairness opinions. 4 graduate hours. No professional credit. Credit is not given for FIN 526 if the student has received credit for FIN 563 Behavioral Finance (67127, 67128). Prerequisite: Previous introductory accounting and finance coursework recommended. Graduate students only.
FIN 527: Mergers & Acquisitions Topics Focuses on identifying ways to increase firm value through mergers and acquisitions (M&A) and corporate restructurings. We will develop your skills in the design and evaluation of transactions. Specific topics addressed in the course are the valuation of companies, structuring of transactions, deal tactics and strategy, valuation of leveraged buyouts, and spin-offs/carve-outs. We will also delve into issues of law, accounting and taxation and how they affect the structuring and outcome of merger transactions. Knowledge about M&A is an important component of any corporate finance professional and is the foundation for effective work in a wide range of fields including corporate development, investment banking, consulting, and advising senior management. 2 or 4 graduate hours. No professional credit.
FIN 529: Applied Financial Analysis Provides key building blocks necessary for many careers in finance. Designed to provide a practical approach to analyzing and interpreting complex financial statements to make decisions from a range of user perspectives, including investment banks, equity investors and commercial banks. Advanced financial analysis and forecasting will be applied through assignments and casework. There will be an emphasis on business writing skills commonly applied by finance professionals. 4 graduate hours. No professional credit. Credit is not given for FIN 529 if the student has received credit for FIN 564 Applied Financial Analysis (71521, 71522, 73395, 75067, 75850). Prerequisite: ACCY 501 or equivalent. A baseline understanding of financial accounting is expected of all students coming into this course.
FIN 530: Foundations in Risk Management This course introduces risk management including basic concepts and techniques of pure risk and financial risk management. Corporate hazard risk management including insurance and securitization of pure risks will be covered in detail. Insurer risk management will be examined including reinsurance, loss reserving, underwriting of risks, and catastrophic risk management. Students will also be introduced to Enterprise Risk Management (ERM). 2 graduate hours. No professional credit. Prerequisite: MSF and MBA students only.
FIN 532: Managing Market Risks for Financial Institutions Covers management of tradable financial market risks in the context of financial institutions which incur these risks through their operations, product offerings, assets, and liabilities. We examine the models and methods in practice to measure and manage interest rate, equity, credit, and other market risks with a focus on using financial derivatives such as futures, swaps, and options. Course is applications-oriented with heavy emphasis on numerical modeling. 4 graduate hours. No professional credit. Credit is not given if the student has received graduate credit in FIN 432.
FIN 535: Wealth Management This course studies personal wealth management techniques with an emphasis on life insurance products; covers life insurance policies, annuities, trusts, buy-sell arrangements, investing in stocks, bonds and mutual funds, banking and borrowing, purchasing residential and commercial real estate, income and estate taxation and management of personal financial portfolio. The course also allows students to build a wealth management plan based on a case scenario. 2 or 4 graduate hours. No professional credit.
FIN 545: Real Estate Investment Real estate accounts for one-third of the world's capital assets. This course provides students with a comprehensive understanding of real estate valuation, cycles, markets, investments, and decision-making. The bulk of the course covers income-producing commercial property, although we will also discuss residential housing. This course provides a unified finance based framework to answer real estate investment decision making problems in the real world. 4 graduate hours. No professional credit. Prerequisite: Fin 541 is recommended but not required. Graduate students only.
FIN 550: Big Data Analytics in Finance for Predictive and Causal Analysis Recent trends in "big data" present both enormous challenges and opportunities for businesses. This course introduces concepts and techniques of data analytics and shows how they can be used for making predictions, and to distinguish between correlation and causation, in the context of financial and economic analysis. Covered tools include data visualization, machine learning, regression analysis, randomized trials, A/B testing, and quasi-experiments. Students will apply these tools using R programming within the Amazon Web Services cloud computing environment. 4 graduate hours. No professional credit. Credit is not given for FIN 550 if the student has received credit for FIN 510 Big Data Analytics in Finance for Predictive and Causal Analysis (71511, 71512, 71513, 72873, 72874, 72875). Prerequisite: Consent of Instructor.
FIN 552: Applied Financial Econometrics The aim of this course is to equip students with a working knowledge of important econometric techniques necessary to understand and interpret financial market data. The course covers time-series and cross-sectional properties of asset returns, predictability of equity returns, empirical tests of asset pricing models, modelling time-varying volatility. The interplay between asset pricing theories, statistical assumptions and relevant econometric techniques is explored in the context of published empirical work, including classical papers as well as a more recent research. 4 graduate hours. No professional credit. Credit is not given for FIN 552 and FIN 580 Section DK2 (72033) or FIN 580 Section DK (70390). Prerequisite: FIN 511.
FIN 553: Machine Learning in Finance Machine Learning includes the design and the study of algorithms that can learn from experience, improve their performance and make predictions. In this course students will learn the foundations of Machine Learning and explore state of the art algorithms and tools. Topics include supervised learning (neural networks, support vector machines), unsupervised learning (clustering, dimensionality reduction) and reinforcement learning (dynamic programming, Q-learning, SARSA, policy gradient methods). Applications include option pricing, portfolio selection and credit card fraud detection. Students will gain practical experience implementing these models in Python with frequently used packages such as TensorFlow. 2 or 4 graduate hours. No professional credit. Credit is not given for FIN 553 and FIN 580: Section V2, (72206); or FIN 580: Section V1 (72205).
FIN 554: Algorithmic Trading Systems Design and Testing Provides a detailed research process and tools for replicating, assessing, conceptualizing, and developing systematic trading strategies. Students will apply their knowledge in hands-on projects to replicate and evaluate existing research and to create and evaluate a new strategy model. Students will use the R Language for Statistical Computing and Graphics to replicate academic research and evaluate the claims made in papers. Students will also construct a non-trivial strategy from scratch, evaluate the power of each of its components, and examine the likelihood of overfitting. Projects are designed to mimic as closely as possible the day-to-day research activities of working strategy quants, so that students will have practical experience building, testing, and evaluating quantitative models. 4 graduate hours. No professional credit. Credit is not given if student received credit in FIN 580 FIN 580 Basics of Trading Algorithm Design CRN 46818 and/or FIN 580 Analysis and Testing of Trading Algorithms CRN 46819.
FIN 556: Algorithmic Market Microstructure Introduces the modern theoretical, empirical and institutional foundations of market microstructure and trading activity, with an emphasis on applications to algorithmic and high-frequency trading. The first part of the course addresses market microstructure and the algorithmic implementation of traditional microstructure-inspired tasks such as minimizing execution costs. The second part of the course proceeds to examine actual algorithmic strategies, and ultimately high-frequency trading. Recurrent themes throughout the course will be the use of economic theory to simplify computationally challenging problems, and the use of theory-driven structural models to construct more robust trading algorithms. 4 graduate hours. No professional credit. Credit is not given for FIN 556 if the student has received credit for FIN 566 Algorithmic Market Microstructure (67130, 68314).
FIN 557: Financial Data Management and Analysis Proper data handling and management is essential to the success of data analysis. The primary goal of this course is to learn principles and practices of data management with an emphasis on working with financial databases. Students will gain practical skills in data storage, data preparation, and data extractions that eventually lead to data analysis. Data management procedures including SQL queries, and data analysis techniques using large-scale statistical software are presented. 4 graduate hours. No professional credit. Credit is not given toward graduation if credit has been received in FIN 580 CRN 60175 Section G1, FIN 580 CRN 60175 Section GFD, FIN 580 CRN 46819 Section FD2. Prerequisite: Restricted to MS: Finance, MS:Business Analytics.
FIN 558: Data Science and Python for Finance Focuses on introducing Python for financial analysis. It provides a detailed understanding of Python basics. Students will apply Python in solving problems in corporate finance and performing investment analysis. Topics include capital budgeting decisions, equity valuation, risk and return, portfolio optimization, and technical trading strategies. 2 graduate hours. No professional credit. Credit is not given toward graduation for FIN 558 if credit for FIN 580 CRN 60285, 60176, 66703 has been given.
FIN 559: Advanced Data Science and Python for Finance Provides an advanced understanding of Python and data analytics tools to solve problems in finance. Students will analyze data and solve real-world problems such as investigating market responses to earnings announcements, comparing value and growth investing, forecasting stock prices, predicting bankruptcy, and estimating house prices. 2 graduate hours. No professional credit.
FIN 572: The Finance of Mergers and Acquisitions Focuses on the theory and practice of mergers and acquisitions (M&A), with a focus on the Finance. The Finance of M&A uses tools from different areas of Finance to help managers and investment bankers design successful M&A deals. In particular, we will learn to value and price M&A deals and how to choose the optimal financing mix for an M&A deal. The course focuses on all the major types of M&A deals including strategic M&A, private equity leveraged buyouts (LBOs), and restructuring deals such as spinoffs and asset transfers. The course will benefit any student who desires to increase their ability to understand and execute M&A deals, including (but not limited to), entrepreneurs, consultants, bankers, investors, analysts, corporate managers, marketers, strategists, and deal-makers of all types. The course will also deepen students' understanding of financial modeling and capital structure, both in theory and practice. 4 graduate hours. No professional credit. Prerequisite: It is recommended that students complete ACCY 500, FIN 570, and BADM 572 (or equivalents) before enrolling in this course. Requires that students have a grasp of core accounting principles and have a basic knowledge of concepts in finance (time value of money, net present value, cost of capital and a basic notion of capital structure). Students should also be comfortable with work that is quantitatively focused, and have a basic notion of Statistics (means, medians, standard deviation, and regression tools).
FIN 574: Microeconomics for Business Microeconomics for professional business students. Shows relevance of value and distribution theories for business managers. Includes demand and supply theory, consumer choice, production and cost theory, industrial structure, and wage and capital theory. Intended for students in the Master of Business Administration program. 4 graduate hours. No professional credit. Credit is not given toward graduation for FIN 574 and ECON 302, ECON 500 or ECON 528. Prerequisite: Enrollment is often restricted to students in specialized programs.
FIN 580: Special Topics in Finance Lectures and discussions relating to new areas of interest. See class schedule for topics and prerequisites. 0 to 4 graduate hours. No professional credit. Approved for Letter and S/U grading. May be repeated to a maximum of 18 hours in a semester; may be repeated to a maximum of 32 hours in subsequent semesters. Credit is not given for FIN 528 and FIN 580 (66393), Section ADF. Prerequisite: Varies by section.
FIN 581: Professional Development Effective communication skills are one of the most sought-after traits of business leaders across industries and throughout the world. Understanding the world around you, as well as communicating clearly and persuasively is critical to your success as a student, as an employee and as a leader in the business world. These skills will help establish your own credibility and lead you to become an effective leader among your peers and colleagues. This course will introduce successful strategies for structuring both written and verbal communication in the business world, with an eye toward the specific outcomes listed below. 1 or 2 graduate hours. No professional credit. Prerequisite: Restricted to MSF and MSFE students.
FIN 582: Project Management This course is all about learning by doing. It is designed to assist you in your real-world experience as you work in a team with a real organization to help solve a problem the organization is facing. In class, we will help you gain the skills you will need to successfully complete the project. If you work hard in this class, you will have a compelling story to tell as you interview for internships and jobs and you will gain skills that will help you succeed in your career. 1 to 2 graduate hours. No professional credit. Approved for S/U grading only. Prerequisite: Concurrent enrollment in FIN 583 is required. Restricted to MSF students.
FIN 583: Practicum A semester-long, typically corporate-sponsored, team project for MSFE and MSF students that is usually completed by the end of the third semester of study. The goals of the Practicum are: a) to use learned or new tools on real world projects of interest to the corporate sponsor; b) to replicate as closely as possible the environment of the working world where students will soon find themselves employed; c) to work cohesively with other members of a team so as to efficiently produce the desired project results; and d) to be able to communicate effectively with technical and non-technical audiences, which may include the sponsor and/or co-workers. 1 to 4 graduate hours. No professional credit. Prerequisite: Restricted to MSF and MSFE students.
FIN 589: Applied Portfolio Management Applies academic topics on financial markets, security analysis/valuation and portfolio management to hands-on investment management. Students will form and review objectives, constraints, and investment policy as it relates to the client's money under management. They will purchase securities, monitor performance of the portfolio, and make recommendations for any adjustments to the holdings. They will be fully educated and responsible for the fiduciary and ethical standards of professional money management as guided by the CFA Institute. 4 graduate hours. No professional credit. May be repeated to a maximum of 8 hours. Prerequisite: Credit or concurrent enrollment in FIN 511.
FIN 590: Individual Study and Research Directed reading and research. 0 to 4 graduate hours. No professional credit. Approved for Letter and S/U grading. Course may be repeated up to 16 hours.
FIN 591: Theory of Finance Examines theoretical frameworks for financial decision making under certainty and uncertainty, as well as perfect and imperfect capital markets; discusses state preference, mean-variance, and continuous time models; emphasizes the structure of individual utility functions. Prerequisite: ECON 502; STAT 400; and admission to doctoral program or consent of instructor.
FIN 592: Empirical Analysis in Finance Designed to train the student in the conduct of empirical work in Finance. Covers the major tools and databases needed to replicate the results of published academic papers and to conduct original research. Prerequisite: Enrollment in the doctoral program in Finance or consent of instructor.
FIN 594: Seminar in Corporate Finance Theories, paradigms, and models of nonfinancial corporations; investigates the theoretical foundations and empirical evidence regarding corporate resource allocation, capital structure decisions, and dividend policies; covers in detail contingent claim analysis, signaling theory, and agency theory. Prerequisite: FIN 591 and ECON 507.
FIN 599: Thesis Research Required for those writing master's and doctoral theses in finance. 0 to 16 graduate hours. No professional credit. Approved for S/U grading only. May be repeated in separate terms.
FLTE 475: Learning to Teach World Language Course focuses on pedagogy, teacher knowledge and efficacy, and the development of professional dispositions for use in K-12 teaching. Emphasis is on preparing for the edPTA through lesson planning and assessment development. Includes multiple teaching components and 55 hours of early field experience. 4 undergraduate hours. No graduate credit. Prerequisite: Successful completion of FLTE 471.