About This Specialization

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "About This Specialization"

Transcription

1 About This Specialization Wharton's Business and Financial Modeling Specialization is designed to help you make informed business and financial decisions. These foundational courses will introduce you to spreadsheet models, modeling techniques, and common applications for investment analysis, company valuation, forecasting, and more. When you complete the Specialization, you'll be ready to use your own data to describe realities, build scenarios, and predict performance. 5 courses Follow the suggested order or choose your own Projects Follow the suggested order or choose your own Certificates Follow the suggested order or choose your own

2 4 weeks of study, 3-5 hours/week English, Russian About the Course How can you put data to work for you? Specifically, how can numbers in a spreadsheet tell us about present and past business activities, and how can we use them to forecast the future? The answer is in building quantitative models, and this course is designed to help you understand the fundamentals of this critical, foundational, business skill. Through a series of short lectures, demonstrations, and assignments, you ll learn the key ideas and process of quantitative modeling so that you can begin to create your own models for your own business or enterprise. By the end of this course, you will have seen a variety of practical commonly used quantitative models as well as the building blocks that will allow you to start structuring your own models. These building blocks will be put to use in the other courses in this Specialization.

3 Week 1 Module 1: Introduction to Models In this module, you will learn how to define a model, and how models are commonly used. You ll examine the central steps in the modeling process, the four key mathematical functions used in models, and the essential vocabulary used to describe models. By the end of this module, you ll be able to identify the four most common types of models, and how and when they should be used. You ll also be able to define and correctly use the key terms of modeling, giving you not only a foundation for further study, but also the ability to ask questions and participate in conversations about quantitative models. Video 1.1 Course Introduction Video 1.2 Definition and Uses of Models, Common Functions Video 1.3 How Models Are Used in Practice Video 1.4 Key Steps in the Modeling Process Video 1.5 A Vocabulary for Modeling Video 1.6 Mathematical Functions Video 1.7 Summary Quiz Module 1: Introduction to Models Quiz Reading PDF of Lecture Slidess

4 Week 2 Module 2: Linear Models and Optimization This module introduces linear models, the building block for almost all modeling. Through close examination of the common uses together with examples of linear models, you ll learn how to apply linear models, including cost functions and production functions to your business. The module also includes a presentation of growth and decay processes in discrete time, growth and decay in continuous time, together with their associated present and future value calculations. Classical optimization techniques are discussed. By the end of this module, you ll be able to identify and understand the key structure of linear models, and suggest when and how to use them to improve outcomes for your business. You ll also be able to Video 2.1 Introduction to Linear Models and Optimization Video 2.2 Growth in Discrete Time Video 2.3 Constant Proportionate Growth Video 2.4 Present and Future Value Video 2.5 Optimization Video 2.6 Summary Quiz Module 2: Linear Models and Optimization Quiz Reading PDF of Lecture Slides

5 Week 3 Module 3: Probabilistic Models This module explains probabilistic models, which are ways of capturing risk in process. You ll need to use probabilistic models when you don t know all of your inputs. You ll examine how probabilistic models incorporate uncertainty, and how that uncertainty continues through to the outputs of the model. You ll also discover how propagating uncertainty allows you to determine a range of values for forecasting. You ll learn the most-widely used models for risk, including regression models, tree-based models, Monte Carlo simulations, and Markov chains, as well as the building blocks of these probabilistic models, such as random variables, probability distributions, Bernoulli random variables, binomial random variables, the empirical rule, and perhaps the most important of all of the statistical distributions, the normal distribution, characterized by mean and standard deviation. By the end of this module, you ll be able to define a probabilistic model, identify and understand the most commonly used probabilistic models, know the components of those models, and determine the most useful probabilistic models for capturing and exploring risk in your own business. Video 3.1 Introduction to Probabilistic Models Video 3.2 Examples of Probabilistic Models Video 3.3 Regression Models Video 3.4 Probability Trees Video 3.5 Monte Carlo Simulations Video 3.6 Markov Chain Models Video 3.7 Building Blocks of Probability Models Video 3.8 The Bernoulli Distribution Video 3.9 The Binomial Distribution Video 3.10 The Normal Distribution Video 3.11 The Empirical Rule Video 3.12 Summary Quiz Module 3: Probabilistic Models Quiz Reading PDF of Lecture Slides

6 Week 4 Module 4: Regression Models This module explores regression models, which allow you to start with data and discover an underlying process. Regression models are the key tools in predictive analytics, and are also used when you have to incorporate uncertainty explicitly in the underlying data. You ll learn more about what regression models are, what they can and cannot do, and the questions regression models can answer. You ll examine correlation and linear association, methodology to fit the best line to the data, interpretation of regression coefficients, multiple regression, and logistic regression. You ll also see how logistic regression will allow you to estimate probabilities of success. By the end of this module, you ll be able to identify regression models and their key components, understand when they are used, and be able to interpret them so that you can discuss your model and convince others that your model makes sense, with the ultimate goal of implementation. Video 4.1 Introduction to Regression Model Video 4.2 Use of Regression Models Video 4.3 Interpretion of Regression Coefficients Video 4.4 R-squared and Root Mean Squared Error (RMSE) Video 4.5 Fitting Curves to Data Video 4.6 Multiple Regression Video 4.7 Logistic Regression Video 4.8 Summary of Regression Models Quiz Module 4: Regression Models Quiz Reading PDF of Lecture Slides

7 4 weeks of study, 1-3 hours/week English, Portuguese (Brazilian) About the Course The simple spreadsheet is one of the most powerful data analysis tools that exists, and it s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have know how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required.

8 Week 1 Spreadsheets: A Tool for Thinking with Numbers This module was designed to introduce you to the history of spreadsheets, their basic capabilities, and how they can be used to create models. You'll learn the different types of data used in spreadsheets, spreadsheet notations for mathematical operations, common built-in formulas and functions, conditional expressions, relative and absolute references, and how to identify and correct circular references. By the end of this module, you'll understand the context of spreadsheets, be able to navigate a spreadsheet, use built-in formulas and functions in spreadsheets, create your own simple formulas, and identify and correct common errors so you can put spreadsheets to work for you. Video 2.0 Module Introduction Video 2.1 Using assumptions and decision variables in spreadsheet models Video 2.2 Structuring a spreadsheet to model variables, objectives, and objective functions Video 2.3 Constructing simple cashflow model Video 2.4 What-if analysis and sensitivity analysis Video 2.5 Limits to simple, deterministic models Quiz Module 2 Quiz: Classic Models Reading PDF of Module 2 Lecture Slides Reading Module 2 examples

9 Week 2 Addressing Uncertainty and Probability in Models This module was designed to introduce you to how you can use spreadsheets to address uncertainty and probability. You'll learn about random variables, probability distributions, power, exponential, and log functions in model formulas, models for calculating probability trees and decision trees, how to use regression tools to make predictions, as well as multiple regression. By the end of this module, you'll be able to measure correlations between variables using spreadsheet statistical functions, understand the results of functions that calculate correlations, use regression tools to make predictions, and improve forecasts with multiple regression. Video 3.0 Introduction Video 3.1 Random variables and probability distributions Video 3.2 Changes in discrete and continuous time Video 3.3 Power, exponential, and log functions Video 3.4 Probability trees and decision trees Video 3.5 Correlation and Regression Quiz Module 3 Quiz: Probability, Correlation, and Regression Reading PDF of Module 3 Lecture Slides Reading Module 3 examples Reading Additional reading on exponential and other functions

10 Week 3 Simulation and Optimization In this module, you'll learn to use spreadsheets to implement Monte Carlo simulations as well as linear programs for optimization. You'll examine the purpose of Monte Carlo simulations, how to implement Monte Carlo simulations in spreadsheets, the types of problems you can address with linear programs and how to implement those linear programs in spreadsheets. By the end of this module, you'll be able to model uncertainty and risk in spreadsheets, and use Excel's solver to optimize resources to reach a desired outcome. You'll also be able to identify the similarities and differences between Excel and Sheets, and be prepared for the next course in the Business and Financial Modeling Specialization. Video 4.0 Introduction Video 4.1 Monte Carlo Simulations Video 4.2 Linear Programming Video 4.3 Next Steps, and Differences between Excel and Sheets Quiz Module 4 Quiz: Simulations, Scenarios, and Optimization Reading PDF of Module 4 Lecture Slides Reading Module 4 examples Reading Links and other resources for further study

11 4 weeks of study, 1-3 hours/week English, Portuguese (Brazilian) About the Course Useful quantitative models help you to make informed decisions both in situations in which the factors affecting your decision are clear, as well as in situations in which some important factors are not clear at all. In this course, you can learn how to create quantitative models to reflect complex realities, and how to include in your model elements of risk and uncertainty. You ll also learn the methods for creating predictive models for identifying optimal choices; and how those choices change in response to changes in the model s assumptions. You ll also learn the basics of the measurement and management of risk. By the end of this course, you ll be able to build your own models with your own data, so that you can begin making data-informed decisions. You ll also be prepared for the next course in the Specialization.

12 Week 1 Week 1: Modeling Decisions in Low Uncertainty Settings This module is designed to teach you how to analyze settings with low levels of uncertainty, and how to identify the best decisions in these settings. You'll explore the optimization toolkit, learn how to build an algebraic model using an advertising example, convert the algebraic model to a spreadsheet model, work with Solver to discover the best possible decision, and examine an example that introduces a simple representation of risk to the model. By the end of this module, you'll be able to build an optimization model, use Solver to uncover the optimal decision based on your data, and begin to adjust your model to account for simple elements of risk. These skills will give you the power to deal with large models as long as the actual uncertainty in the input values is not too high. Video Course Introduction Video 1.1 How To Build an Optimization Model: Hudson Readers Ad Campaign Video 1.2 Optimizing with Solver, and Alternative Data Inputs Video 1.3 Adding Risk: Managing Investments at Epsilon Delta Capital Reading PDFs of Slides for Week 1 Reading Excel Files for Week 1 Quiz Week 1: Modeling in Low Uncertainty Quiz

13 Week 2 Week 2: Risk and Reward: Modeling High Uncertainty Settings What if uncertainty is the key feature of the setting you are trying to model? In this module, you'll learn how to create models for situations with a large number of variables. You'll examine high uncertainty settings, probability distributions, and risk, common scenarios for multiple random variables, how to incorporate risk reduction, how to calculate and interpret correlation values, and how to use scenarios for optimization, including sensitivity analysis and the efficient frontier. By the end of this module, you'll be able to identify and use common models of future uncertainty to build scenarios that help you optimize your business decisions when you have multiple variables and a higher degree of risk. Video 2.1 High Uncertainty Settings, Probability Distributions, Uncertainty and Risk Video 2.2 Common Scenarios for Multiple Random Variables, Risk Reduction, and Calculating and Interpreting Correlation Values Video 2.3 Using Scenarios for Optimizing Under High Uncertainty, Sensitivity Analysis and Efficient Frontier Reading PDFs of Lecture Slides for Week 2 Reading Excel Files for Week 2 Quiz Week 2: Modeling in High Uncertainty Quiz

14 Week 3 Week 3: Choosing Distributions that Fit Your Data When making business decisions, we often look to the past to make predictions for the future. In this module, you'll examine commonly used distributions of random variables to model the future and make predictions. You'll learn how to create meaningful data visualizations in Excel, how to choose the the right distribution for your data, explore the differences between discrete distributions and continuous distributions, and test your choice of model and your hypothesis for goodness of fit. By the end of this module, you'll be able to represent your data using graphs, choose the best distribution model Video 3.1 Data and Visualization: Graphical Representation Video 3.2, pt 1: Choosing Among Distributions: Discrete Distributions Video 3.2, pt 2: Choosing Among Distributions: Continuous Distributions Video 3.3 Hypothesis Testing and Goodness of Fit Reading PDFs of Lecture Slides for Week 3 Reading Excel Files for Week 3 Quiz Week 3: Choosing Fitting Distributions Quiz

15 Week 4 Week 4: Balancing Risk and Reward Using Simulation This module is designed to help you use simulations to enabling compare different alternatives when continuous distributions are used to describe uncertainty. Through an in-depth examination of the simulation toolkit, you'll learn how to make decisions in high uncertainty settings where random inputs are described by continuous probability distributions. You'll also learn how to run a simulation model, analyze simulation output, and compare alternative decisions to decide on the most optimal solution. By the end of this module, you'll be able to make decisions and manage risk using simulation, and more broadly, to make successful business decisions in an increasing complex and rapidly evolving business world. Video 4.1: Modeling Uncertainty: From Scenarios to Continuous Distributions Video 4.2 Connecting Random Inputs and Random Outputs in a Simulation Video 4.3 Analyzing and Interpreting Simulation Output: Evaluating Alternatives Using Simulation Results Video Course Conclusion Reading PDFs of Lecture Slides Reading Excel files for Week 4 Quiz Week 4: Using Simulations Quiz

16 4 weeks of study, 1-3 hours/week English About the Course This course is designed to show you how use quantitative models to transform data into better business decisions. You ll learn both how to use models to facilitate decision-making and also how to structure decision-making for optimum results. Two of Wharton s most acclaimed professors will show you the step-by-step processes of modeling common business and financial scenarios, so you can significantly improve your ability to structure complex problems and derive useful insights about alternatives. Once you ve created models of existing realities, possible risks, and alternative scenarios, you can determine the best solution for your business or enterprise, using the decision-making tools and techniques you ve learned in this course.

17 Week 1 Evaluation Criteria: Net Present Value This module was designed to introduce you to the many potential criteria for selecting investment projects, and to explore the most effective of these criteria: Net Present Value (NPV). Through the use of concrete examples, you'll learn the key components of Net Present Value, including the time value of money and the cost of capital, the main utility of NPV, and why it is ultimately more accurate and useful for evaluating projects than other commonly used criteria. By the end of this module, you'll be able to explain why net present value analysis is the appropriate criteria for choosing whether to accept or reject a project, and why other criteria, such as IRR, payback, ROI, etc. may not lead to decisions which maximize value. Video Course Introduction & Overview Video 1.1 Introduction: Criteria for Evaluating Projects Video 1.2 Time Value of Money Video 1.3 NPV Analysis of Projects Video 1.4 Other Evaluation Techniques Video 1.5 The Cost of Capital Quiz Evaluation Criteria: Module 1 Quiz Reading PDFs of Lecture Slides Reading Excel Spreadsheets: Module 1

18 Week 2 Evaluating Projects In this module, you'll learn how to evaluate a project with emphasis on analyzing the incremental after-tax cash flows associated with the project. You'll work through a concrete example using alternative scenarios to test the effectiveness of this method. You'll also learn why only future cash flows are relevant, why to ignore financial costs, include all incidental effects, remember working capital requirements, consider the effect of taxes, forget sunk costs, remember opportunity costs, use expected cash flows, and perform sensitivity analysis. By the end of this module, you'll be able to evaluate projects more thoroughly and effectively, with emphasis on how to model the change in the company s after-tax cash flows, so that you can make more profitable decisions. Video 2.1 Introduction and Analyzing Incremental After-Tax Cash Flows - Initial Investment Phase Video 2.2 Analyzing Incremental After-Tax Flows - Operating Phase Video 2.3 Analyzing Incremental After-Tax Flows - Terminal Phase Video 2.4 Example: New Production Machine Video 2.5 Key Considerations in Evaluations Quiz How to Evaluate Projects: Module 2 Quiz Reading PDFs of Lecture Slides

19 Week 3 Expressing Business Strategies in Financial Terms This module was designed to give you the opportunity to learn how business activities, transactions and events are translated into financial statements, including balance sheets, income statements, and cash flow statements. You'll also learn how these three statements are linked to each other, and how balance sheets and income statements can help forecast the future cash flow statements. By the end of this module, you'll be able to explain how accounting systems translate business activities into financial terms, and how to use this to better forecast future cash flows, so that you can express your business strategies in these financial terms, and show "the bottom line" for your proposed plan of action. Video 3.1 Introduction to Financial Statements Video 3.2 Balance Sheets and Income Statements Video 3.3 Cash Flow Statements Quiz Financial Statements and Forecasting: Module 3 Quiz Reading PDFs of Lecture Slides

20 Week 4 New Product Value In this module, you'll apply what you ve been learning to an analysis of a new product venture. You ll learn how to map out a plan of the business activities, transactions and events that need to happen to implement the new venture, including their timing. You'll also learn how to set up a spreadsheet to help with forecasts, and to re-calculate things automatically as we re-think our plans. You'll see how to forecast out the implied financial statements, and calculate the Net Present Value (NPV). By the end of this module, you'll be able to use spreadsheets to explore different risks a venture may face, and analyze the implications of these scenarios for NPV, so that you can make the most profitable, data-driven decision possible. Video 4.1 Introduction and Speadsheet Setup Video 4.2 Forecasting Future Cash Flows Video 4.3 NPV and IRR Calculations Video 4.4 Formulation and Evaluation of Alternative Scenarios Video 4.5 Expanding Beyond the Time Horizon Video Course Conclusion Quiz Calculating Value: Module 4 Quiz Reading PDFs of Lecture Slides Reading Excel Spreadsheets: Module 4

21 English About the Course In this Capstone you will recommend a business strategy based on a data model you ve constructed. Using a data set designed by Wharton Research Data Services (WRDS), you will implement quantitative models in spreadsheets to identify the best opportunities for success and minimizing risk. Using your newly acquired decision-making skills, you will structure a decision and present this course of action in a professional quality PowerPoint presentation which includes both data and data analysis from your quantitative models. Wharton Research Data Services (WRDS) is the leading data research platform and business intelligence tool for over 30,000 corporate, academic, government and nonprofit clients in 33 countries. WRDS provides the user with one location to access over 200 terabytes of data across multiple disciplines including Accounting, Banking, Economics, ESG, Finance, Insurance, Marketing, and Statistics.

22 Week 1 Getting Started Welcome! This opening module was designed to give you an overview of the Business and Financial Modeling Capstone, in which you will be working with historical financial data to calculate individual returns and summary statistics on those returns. The project has multiple steps, which are outlined below in the "Project Prompt", and culminates in a recommendation for portfolio allocation that you will prepare a presentation on. You will draw on elements from all courses to complete this project, and you can use your final presentation as a work sample to improve your current job or even find a new one. Before moving on, complete the "Project Scope Quiz." The work you do this week enables you to understand the steps needed to successfully complete your final project. Reading Project Description - Read me first! Reading Project Prompt Reading Historical Stock Data Quiz Project Scope Quiz Other Module 1 Discussion: Introductions Other Questions about the Project

23 Week 2 Steps 1 and 2: Yahoo Finance In this module, which correlates to Steps 1 and 2 in the Project Prompt, you'll be working with a historical data set to calculate performance data and to provide summary statistics on that data. These calculations will allow you to practice using Spreadsheets for financial calculations, and provides the foundational skills and numbers for the next steps of the project. First, you'll use the set to calculate daily returns on a set of securities. You'll then use your Spreadsheet skills to calculate summary statistics. You'll be given the opportunity to test your knowledge with a sample return to see if your calculations are correct. And you may want to refresh your recollection of the content from the Specialization with the lectures included here. The work you complete this week allows you to form the basis for comparing stock performance, Reading More on Close Price versus Adjusted Close Price Reading More on the Sharpe Ratio Reading Sample Returns Spreadsheet (AAPL) Quiz Daily Returns Quiz Quiz Summary Statistics Quiz Other Questions about Calculating Performance and Summary Statistics Video Definition and Uses of Models, Common Functions (Fundamentals of Quantitative Modeling) Video How Models are Used in Practice (Fundamentals of Quantitative Modeling) Video Mathematical Functions (Fundamentals of Quantitative Modeling) Video Navigating a Spreadsheet and Crafting Formulas (Introduction to Spreadsheets) Video How To Build an Optimization Model: Hudson Readers Ad Campaign (Modeling Risk and Realities) Video Data and Visualization: Graphical Representation (Modeling Risk and Realities) Reading PDFs of Refresher Video Slides

24 Week 3 Step 3: Creating an optimal risky portfolio on the efficient frontier In this module, you'll go beyond calculating simple returns to tackle the more advanced task of finding the minimum variance and "optimal risk portfolio" weights for a portfolio of selected securities (note, the "optimal risky portfolio" is also known as an "optimal portfolio" or "tangent portfolio"). You'll follow the tasks in Step 3 in the Project Prompt and use the resources below to calculate the portfolio weights for two securities that results in the portfolio with the minimum variance; then, you'll calculate the "optimal risky portfolio" on the efficient frontier for these same two securities, then for all 10 stocks in the pool. You'll be quizzed on your calculations and other insights that emerge from this exercise. The work you complete this week gives you practice in creating an optimal risky portfolio, which is a key component of your final project. Note: There are a number of resources available on the internet providing step-by-step instructions on how to use Excel to create an "optimal risky portfolio" on the efficient frontier given a certain set of available assets. We encourage you to attempt to use the skills you gained during the Specialization to work through these steps independently; you are, however, permitted to utilize third-party resources if you find it necessary. We've included some lectures from the underlying Specialization courses concerning Solver, optimization, and other relevant topics. Reading Videos Explaining the Efficient Frontier and Optimal Risky Portfolio Video Optimization (Fundamentals of Quantitative Modeling) Other Third party resources for calculations Video Linear Programming (incl. Solver) (Introduction to Spreadsheets) Reading More on Portfolio Variance Reading More on the Efficient Frontier Reading More on Short Selling Video Optimizing with Solver, and Alternative Data Inputs (Modeling Risk and Realities) Video Adding Risk: Managing Investments at Epsilon Delta Capital (Modeling Risk and Realities) Quiz The Minimum Variance and Optimal Risky Portfolio Other Questions about minimum variance and "optimal risky portfolio" weights Video Introducton to Linear Models and Optimization (Fundamentals of Quantitative Modeling) Video Present and Future Value (Fundamentals of Quantitative Modeling) Video Using Scenarios for Optimizing Under High Uncertainty, Sensitivity Analysis and Efficient Frontier (Modeling Risk and Realities) Reading PDFs of Refresher Video Slides

25 Week 4 Step 4: Optional exercise using CAPM tables The Capital Asset Pricing Model, or CAPM, is another tool used by investors to weigh the risks and rewards of potential investments. In this optional module covering Step 4 in the Project Prompt, you can use CAPM as a vehicle to further strengthen your financial modeling skills, including using regression concepts. You may revisit the Specialization lectures below touching on regression. To test whether you've grasped the concepts in the CAPM model, this module includes a short quiz. This assessment is formative, meaning your score will not count towards your final grade. The work you do this week may inform how you build the mixed asset portfolio of your final project, but it is not necessary to complete the final project. Reading Video on the Capital Asset Pricing Model (CAPM) Reading More on the Capital Asset Pricing Model Practice Quiz Capital Asset Pricing Model Other Questions, comments, and helpful resources for CAPM Video Introduction to Regression (Fundamentals of Quantitative Modeling) Video Use of Regression Models (Fundamentals of Quantitative Modeling) Video Correlation and Regression (Introduction to Spreadsheets) Reading PDFs of Refresher Video Slides

26 Week 5 Step 5: Creating Your Asset Allocation & Final Presentation In this final module you are asked to move beyond a stock-only portfolio to one utilizing more diversified assets and to prepare a short presentation summarizing your findings. As explained in Step 5 of the Project Prompt, you have $5 million to invest in the Vanguard Total Bond Market Index Fund (ticker: VBTLX) and Vanguard 500 Index (ticker: VFIAX) investment vehicles. There are two assessments in this module. First, you'll complete a short quiz on the characteristics of your optimal risky portfolio. Then, in the peer review component of this Capstone, you are tasked with preparing a short presentation that (i) explores how your portfolio of mixed asset class of funds compares to a single security (AAPL) and (ii) uses that comparison to discuss the importance of portfolio diversification. Reading VBTLX and VFIAX Monthly Returns Quiz Working with a Diversified Portfolio Other Module 5 Discussion - Reflect on your experience and share your insights Peer Review Portfolio Performance Presentation Other How did you create a Mixed Asset Portfolio? Other Arguments for and against mixed asset portfolios

Syllabus (Version: 2/2/16)

Syllabus (Version: 2/2/16) UNIVERSITY OF SOUTHERN CALIFORNIA Marshall School of Business DSO 570 The Analytics Edge: Data, Models, and Effective Decisions (Spring 2016) Syllabus (Version: 2/2/16) Contact Information Instructor:

More information

Syllabus (Version: 1/12/15)

Syllabus (Version: 1/12/15) UNIVERSITY OF SOUTHERN CALIFORNIA Marshall School of Business DSO 570 The Analytics Edge: Data, Models, and Effective Decisions (Spring 2015) Syllabus (Version: 1/12/15) Contact Information Instructor:

More information

CURRICULUM INFORMATION BABSON TWO- YEAR MBA PROGRAM

CURRICULUM INFORMATION BABSON TWO- YEAR MBA PROGRAM CURRICULUM INFORMATION BABSON TWO- YEAR MBA PROGRAM The Babson One-Year MBA program curriculum consists of a combination of core courses, signature learning experiences, and electives totaling 55 credits.

More information

Business Analytics Syllabus

Business Analytics Syllabus B6101 Business Analytics Fall 2016 Business Analytics Syllabus Course Description Business analytics refers to the ways in which enterprises such as businesses, non-profits, and governments can use data

More information

Financial Analysis and Risk Management, M.S.

Financial Analysis and Risk Management, M.S. Financial Analysis and Risk Management, M.S. 1 Financial Analysis and Risk Management, M.S. FOX SCHOOL OF BUSINESS AND MANAGEMENT (http://www.fox.temple.edu) About the Program The Fox School of Business

More information

Econ : Economics of Corporate Finance Syllabus

Econ : Economics of Corporate Finance Syllabus University of Pittsburgh Department of Economics CRN: 18363 Econ 1440-1070: Economics of Corporate Finance Syllabus Lecturer: Svitlana Maksymenko, Ph.D. Office: 4703 WWPH Tel: 412-383-8155 Fax: 412-648-1793

More information

MGT/MGP/MGB 261: Investment Analysis

MGT/MGP/MGB 261: Investment Analysis UNIVERSITY OF CALIFORNIA, DAVIS GRADUATE SCHOOL OF MANAGEMENT SYLLABUS for Fall 2014 MGT/MGP/MGB 261: Investment Analysis Daytime MBA: Tu 12:00p.m. - 3:00 p.m. Location: 1302 Gallagher (CRN: 51489) Sacramento

More information

DEPARTMENT OF FINANCE MCCOMBS SCHOOL OF BUSINESS UNIVERSITY OF TEXAS AT AUSTIN. Finance 367: Investment Management. Spring 2008

DEPARTMENT OF FINANCE MCCOMBS SCHOOL OF BUSINESS UNIVERSITY OF TEXAS AT AUSTIN. Finance 367: Investment Management. Spring 2008 DEPARTMENT OF FINANCE MCCOMBS SCHOOL OF BUSINESS UNIVERSITY OF TEXAS AT AUSTIN Finance 367: Investment Management Spring 2008 Monday & Wednesday 08:00-09:30, UTC 1.104, unique #02945 Monday & Wednesday

More information

UNIVERSITY OF PENNSYLVANIA The Wharton School FNCE612 ACCELERATED CORPORATE FINANCE COURSE SYLLABUS

UNIVERSITY OF PENNSYLVANIA The Wharton School FNCE612 ACCELERATED CORPORATE FINANCE COURSE SYLLABUS Course Description UNIVERSITY OF PENNSYLVANIA The Wharton School FNCE612 ACCELERATED CORPORATE FINANCE COURSE SYLLABUS This course is intended for students with prior knowledge of finance or with strong

More information

Applied Mathematics. Dr. Carlos Marques, Chair Mathematics Dept School of Arts & Sciences

Applied Mathematics. Dr. Carlos Marques, Chair Mathematics Dept School of Arts & Sciences Applied Mathematics Dr. Carlos Marques, Chair Mathematics Dept. Carlos.Marques@farmingdale.edu 631-420-2182 School of Arts & Sciences Bachelor of Science Degree The Applied Mathematics Bachelor of Science

More information

UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences

UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences COURSE: PROFESSOR: Office hours: STATISTICAL ANALYSIS, POM-500 (ONLINE) Prerequisite: Finite Math MTH

More information

Session: Semester II, 2013/2014 (13 January April 2014)

Session: Semester II, 2013/2014 (13 January April 2014) NUS Business School Department of Finance FIN3101 CORPORATE FINANCE Session: Semester II, 2013/2014 (13 January 2014 18 April 2014) Instructors Associate Professor Ruth Tan (ruthtan@nus.edu.sg) #07-48

More information

NATIONAL DIPLOMA: ECONOMIC MANAGEMENT ANALYSIS Qualification code: NDEB03 - NQF Level 6

NATIONAL DIPLOMA: ECONOMIC MANAGEMENT ANALYSIS Qualification code: NDEB03 - NQF Level 6 NATIONAL DIPLOMA: ECONOMIC MANAGEMENT ANALYSIS Qualification code: NDEB03 - NQF Level 6 Campus where offered: Ga-Rankuwa Campus Important notification to new applicants: Students who intend to enrol for

More information

ESCUELA SUPERIOR POLITÉCNICA DEL LITORAL FACULTAD DE CIENCIAS SOCIALES Y HUMANÍSTICAS (FCSH) COURSE SYLLABUS FORMULATION AND EVALUATION OF PROJECTS

ESCUELA SUPERIOR POLITÉCNICA DEL LITORAL FACULTAD DE CIENCIAS SOCIALES Y HUMANÍSTICAS (FCSH) COURSE SYLLABUS FORMULATION AND EVALUATION OF PROJECTS ESCUELA SUPERIOR POLITÉCNICA DEL LITORAL FACULTAD DE CIENCIAS SOCIALES Y HUMANÍSTICAS (FCSH) COURSE SYLLABUS 1. CODE AND NUMBER OF CREDITS: CODE NUMBER OF CREDITS Theoretical: 4 Practical: 0 2. COURSE

More information

Mathematics of Personal Finance

Mathematics of Personal Finance Core focuses on real-world financial literacy, personal finance, and business subjects. Students apply what they learned in Algebra I and Geometry to topics including personal income, taxes, checking and

More information

Statistics for the Life Sciences, 5/e, Samuels, Witmer and Schaffner ISBN:

Statistics for the Life Sciences, 5/e, Samuels, Witmer and Schaffner ISBN: v Credits 4 credits Course Title Statistics Course Number STA 3100 Pre-requisite None Co-requisite (s) None (s) Hours 60 theory hours/60 clock hours Total Outside Hours 120 hours Note: A minimum of 2 hours

More information

I. PREREQUISITE For information regarding prerequisites for this course, please refer to the Academic Course Catalog.

I. PREREQUISITE For information regarding prerequisites for this course, please refer to the Academic Course Catalog. Note: Course content may be changed, term to term, without notice. The information below is provided as a guide for course selection and is not binding in any form, and should not be used to purchase course

More information

Portfolio Management

Portfolio Management MCF 17 Advanced Courses Portfolio Management SYLLABUS Professor: Massimo Guidolin Email: massimo.guidolin@unibocconi.it Office: Via Rontgen 1, II floor, room E2-11 Phone: +39 0258365334 Learning Objectives:

More information

Len Lundstrum, Ph.D., FRM

Len Lundstrum, Ph.D., FRM , Ph.D., FRM Professor of Finance Department of Finance College of Business Office: 815 753-0317 Northern Illinois University Fax: 815 753-0504 Dekalb, IL 60115 llundstrum@niu.edu Education Indiana University

More information

MAT 12O ELEMENTARY STATISTICS I

MAT 12O ELEMENTARY STATISTICS I LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 12O ELEMENTARY STATISTICS I 3 Lecture Hours, 1 Lab Hour, 3 Credits Pre-Requisite:

More information

King Saud University. Deanship of Graduate Studies. College of Business Administration. Council of Graduate Programs in Business Administration

King Saud University. Deanship of Graduate Studies. College of Business Administration. Council of Graduate Programs in Business Administration King Saud University Deanship of Graduate Studies King Saud University Deanship of Graduate Studies College of Business Administration Council of Graduate Programs in Business Administration Master of

More information

BUSINESS ADMINISTRATION (ACC) (BUS) (WEB)

BUSINESS ADMINISTRATION (ACC) (BUS) (WEB) BUSINESS ADMINISTRATION (ACC) (BUS) (WEB) Degree offered: B.A. or B.S. The B.A. and B.S. in Business Administration are designed to provide students with a common body of knowledge in Business that will

More information

Probability An Introduction with Applications

Probability An Introduction with Applications Probability An Introduction with Applications 0.5 0.2 0 0 2 0 0 5 0.05 0.1 0 5 10 15 0 40 60 80 Gordon B. Hazen Preface to the instructor This text is meant as an introduction to calculus-based probability,

More information

Diploma in Management

Diploma in Management School of Public Sector Policy and Management Diploma in Management PROGRAMME DOCUMENT VERSION 1.0 DM v1.0 May 2005 University of Technology, Mauritius La Tour Koenig, Pointe aux Sables, Mauritius Tel:

More information

The Fidelity Advisor 529 Plan Start investing today. Be ready for college tomorrow. Not FDIC Insured May Lose Value No Bank Guarantee

The Fidelity Advisor 529 Plan Start investing today. Be ready for college tomorrow. Not FDIC Insured May Lose Value No Bank Guarantee The Fidelity Advisor 529 Plan Start investing today. Be ready for college tomorrow. Not FDIC Insured May Lose Value No Bank Guarantee Learning Objectives Upon completion of this seminar you will be able

More information

MAT 119 STATISTICS AND ELEMENTARY ALGEBRA 5 Lecture Hours, 2 Lab Hours, 3 Credits Office Hours: Pre-Requisite: MAT 095 or placement in MAT 096

MAT 119 STATISTICS AND ELEMENTARY ALGEBRA 5 Lecture Hours, 2 Lab Hours, 3 Credits Office Hours: Pre-Requisite: MAT 095 or placement in MAT 096 LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 119 STATISTICS AND ELEMENTARY ALGEBRA Instructor Name: 5 Lecture Hours, 2 Lab Hours,

More information

COURSE SYLLABUS MATH 2311

COURSE SYLLABUS MATH 2311 COURSE SYLLABUS MATH 2311 ****************************************************************************** YEAR COURSE OFFERED: 2017 SEMESTER COURSE OFFERED: Spring Session DEPARTMENT: MATH COURSE NUMBER:

More information

BGS Training Requirement in Statistics

BGS Training Requirement in Statistics BGS Training Requirement in Statistics All BGS students are required to have an understanding of statistical methods and their application to biomedical research. Most students take BIOM611, Statistical

More information

AP Statistics Course Syllabus

AP Statistics Course Syllabus AP Statistics Course Syllabus Textbook and Resource materials The primary textbook for this class is Yates, Moore, and McCabe s Introduction to the Practice of Statistics (TI 83 Graphing Calculator Enhanced)

More information

UNIVERSITY OF SOUTHERN CALIFORNIA MARSHALL SCHOOL OF BUSINESS SPRING FBE 441: Investments

UNIVERSITY OF SOUTHERN CALIFORNIA MARSHALL SCHOOL OF BUSINESS SPRING FBE 441: Investments UNIVERSITY OF SOUTHERN CALIFORNIA MARSHALL SCHOOL OF BUSINESS SPRING 2011 FBE 441: Investments Class 15360R, 2:00-3:50pm MW Class 15362R, 4:00-5:50pm MW JKP112 JKP112 1. Contact Information: - Instructor:

More information

Business Administration (BUS ADM)

Business Administration (BUS ADM) University of Wisconsin-Green Bay 1 Business Administration (BUS ADM) Courses BUS ADM 202. Business and Its Environment. 3 Credits. The major components of the business enterprise and its resources, competitive

More information

Modelling Student Knowledge as a Latent Variable in Intelligent Tutoring Systems: A Comparison of Multiple Approaches

Modelling Student Knowledge as a Latent Variable in Intelligent Tutoring Systems: A Comparison of Multiple Approaches Modelling Student Knowledge as a Latent Variable in Intelligent Tutoring Systems: A Comparison of Multiple Approaches Qandeel Tariq, Alex Kolchinski, Richard Davis December 6, 206 Introduction This paper

More information

AP Statistics Curriculum

AP Statistics Curriculum AP Statistics Curriculum COURSE OUTLINE, TIMELINE AND LEARNING OBJECTIVES Graphical and Numeric Representations of Data (independent summer work through mid Sept.) Learning Objective: Students will be

More information

MATHEMATICAL SCIENCES, BACHELOR OF SCIENCE (B.S.) WITH A CONCENTRATION IN OPERATIONS RESEARCH

MATHEMATICAL SCIENCES, BACHELOR OF SCIENCE (B.S.) WITH A CONCENTRATION IN OPERATIONS RESEARCH Mathematical Sciences, Bachel of Science (B.S.) with a concentration in operations research 1 MATHEMATICAL SCIENCES, BACHELOR OF SCIENCE (B.S.) WITH A CONCENTRATION IN OPERATIONS RESEARCH The curriculum

More information

BIP 390: Theories of Financial Investing Course Outline and Syllabus Spring 2015

BIP 390: Theories of Financial Investing Course Outline and Syllabus Spring 2015 BIP 390: Theories of Financial Investing Course Outline and Syllabus Spring 2015 Office Hours: The TA will be in the classroom approximately 30 minutes before each class to answer questions. Both the TA

More information

MBA - GENERAL (MBA) Courses. MBA - General (MBA) 1

MBA - GENERAL (MBA) Courses. MBA - General (MBA) 1 MBA - General (MBA) 1 MBA - GENERAL (MBA) Courses MBA 4000 Business Speaking Lab (4 Credits) MBA 4001 Business Writing Lab (4 Credits) MBA 4010 Business Speaking Lab II (1 Credit) MBA 4011 Business Writing

More information

REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN BUSINESS ANALYTICS (MSc[BA])

REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN BUSINESS ANALYTICS (MSc[BA]) REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN BUSINESS ANALYTICS (MSc[BA]) These Regulations apply to candidates admitted to the Master of Science in Business Analytics curriculum in the academic

More information

Core Curriculum and Majors. MBA Academic Affairs, August 8, 2017

Core Curriculum and Majors. MBA Academic Affairs, August 8, 2017 Core Curriculum and Majors MBA, August 8, 2017 What do I need to earn my Wharton MBA? Quarter Quarter Credit Units 0.5CU 1.0CU 0.5CU Semester 2-years tuition = 21CUs Master s Degree = 19CUs 2 The Fixed

More information

Economics (ECON) Economics (ECON) Courses

Economics (ECON) Economics (ECON) Courses Economics (ECON) Economics (ECON) Courses ECON 5020 [0.5 credit] (ECO 6122, ECO 6522) Microeconomic Theory An introduction to graduate-level microeconomic theory, including topics such as utility maximization

More information

KWHS Investment Competition Guidebook-Region High School Investment Competition GUIDEBOOK

KWHS Investment Competition Guidebook-Region High School Investment Competition GUIDEBOOK 2017-2018 Knowledge@Wharton High School Investment Competition GUIDEBOOK 1 Introduction Welcome to the 2017-2018 KWHS Investment Competition! CASE STUDY: You graduated college and now work at an up-and-coming

More information

The programme shall normally extend over six years of part-time study.

The programme shall normally extend over six years of part-time study. 1 REGULATIO S FOR THE DEGREE OF BACHELOR OF ACCOU TI G (BAcc) 1 (See also General Regulations and Regulations for First Degree Curricula) Definition BAC 1 For the purpose of these regulations and the syllabuses

More information

Module Descriptions. for the Bachelor in Business Administration of the University of Münster from 07. Februar 2017 valid from Winter Semester 2017/18

Module Descriptions. for the Bachelor in Business Administration of the University of Münster from 07. Februar 2017 valid from Winter Semester 2017/18 Module Descriptions for the Bachelor in Business Administration of the University of Münster from 07. Februar 2017 valid from Winter Semester 2017/18 Contents Contents... 2 Study Plan... 4 Foundations

More information

AP STATISTICS Course Outline

AP STATISTICS Course Outline AP STATISTICS Course Outline NUMBER: 314 LEVEL: Honors TEXTBOOK: Stats Modeling the World, Pearson Education Inc., Bock, Velleman and DeVeaux, 2004. The Practice of Statistics, W. H. Freeman and Company,

More information

BFIN 2145 (20593): Financial Modeling SYLLABUS

BFIN 2145 (20593): Financial Modeling SYLLABUS University of Pittsburgh Joseph M. Katz Graduate School of Business BFIN 2145 (20593): Financial Modeling SYLLABUS Abstract: The course is an introduction to computation finance and financial econometrics.

More information

Bachelor of Science in Business Administration - Financial Management

Bachelor of Science in Business Administration - Financial Management Bachelor of Science in Business Administration - Financial John Griffith, Chair and Chief Departmental Advisor Financial management comprises four majors: finance, personal financial planning, real estate,

More information

CENTRAL TEXAS COLLEGE SYLLABUS FOR MATH 1342 ELEMENTARY STATISTICAL METHODS. Semester Hours Credit: 3

CENTRAL TEXAS COLLEGE SYLLABUS FOR MATH 1342 ELEMENTARY STATISTICAL METHODS. Semester Hours Credit: 3 I. INTRODUCTION CENTRAL TEXAS COLLEGE SYLLABUS FOR ELEMENTARY STATISTICAL METHODS Semester Hours Credit: 3 A., Elementary Statistics, is a three-semester-hour introductory course in statistics. The general

More information

BUSINESS FINANCE 7750 HEALTHCARE FINANCE

BUSINESS FINANCE 7750 HEALTHCARE FINANCE BUSINESS FINANCE 7750 HEALTHCARE FINANCE Course Syllabus 3 Credit Hours Spring Semester 2016 TR 9:35 10:55AM / 210 Gerlach Hall Instructor: Dr. Bill Rives TA: Chris Kvale Phone: (614) 292-2979 Phone: Phone,

More information

MASTER COURSE SYLLABUS

MASTER COURSE SYLLABUS MASTER COURSE SYLLABUS MAT 120 ~ PROBABILITY AND STATISTICS Course Number MAT 120 Course Title Probability and Statistics Credit Hours 3 Prerequisites Course Description Student Learning Goals/Objectives

More information

ESD.70J / 1.145J Engineering Economy Module

ESD.70J / 1.145J Engineering Economy Module MIT OpenCourseWare http://ocw.mit.edu ESD.70J / 1.145J Engineering Economy Module Fall 2009 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. ESD.70J Engineering

More information

Executive Master Program Financial Engineering. Technology + Management. KIT The Research University in the Helmholtz Association

Executive Master Program Financial Engineering. Technology + Management. KIT The Research University in the Helmholtz Association Executive Master Program Financial Engineering Technology + Management KIT The Research University in the Helmholtz Association The KIT is system-accredited by The HECTOR School is the Technology Business

More information

Learning Outcomes. Course BA4346, Section 002, Investment Management Professor Yexiao Xu Term Fall 2009 Meetings Tuesday, 1:00-3:45PM

Learning Outcomes. Course BA4346, Section 002, Investment Management Professor Yexiao Xu Term Fall 2009 Meetings Tuesday, 1:00-3:45PM Course BA4346, Section 002, Investment Management Professor Yexiao Xu Term Fall 2009 Meetings Tuesday, 1:00-3:45PM Professor s Contact Information Office Phone (972)883-6703 Office Location SM 3.812 (School

More information

Pakistan Institute of Public Finance Accountants. Examiners Comment

Pakistan Institute of Public Finance Accountants. Examiners Comment Pakistan Institute of Public Finance Accountants Examiners Comment General Instructions while solving the Question Paper 1- Students must start each question from new page of the answer scripts. 2- Before

More information

San José State University

San José State University San José State University School: Lucas Graduate School of Business Department: Accounting and Finance Course Number: BUS 270 Title: Financial Management Section: 1 Semester: Spring Year: 2015 Instructor:

More information

Revision of GCSE Specifications. Draft Proposals. Business Studies

Revision of GCSE Specifications. Draft Proposals. Business Studies Revision of GCSE Specifications Draft Proposals Business Studies GCSE Draft Proposals for Consultation_2016 Contents Introduction 3 Specification at a Glance 4 Subject Content for each Unit 5 Summary of

More information

Mathematics 4 8 (115)

Mathematics 4 8 (115) Purpose Mathematics 4 8 (115) The purpose of the Mathematics 4 8 test is to measure the requisite knowledge and skills that an entry-level educator in this field in Texas public schools must possess. The

More information

FACULTY BEHAVIOURAL, MANAGEMENT AND SOCIAL SCIENCES (BMS) FINANCE & ACCOUNTING

FACULTY BEHAVIOURAL, MANAGEMENT AND SOCIAL SCIENCES (BMS) FINANCE & ACCOUNTING FACULTY BEHAVIOURAL, MANAGEMENT AND SOCIAL SCIENCES (BMS) FINANCE & ACCOUNTING 2 BMS - FINANCE & ACCOUNTING 3 RESEARCH GROUP Sijia Yan BSc IBA graduate I think Finance is important to all businesses. Start-ups

More information

READ EVERYTHING VERY CAREFULLY!

READ EVERYTHING VERY CAREFULLY! BANA 7012 Decision Modeling Spring Semester 2017 Flex 2 Distance Learning Syllabus (February 27 April 22) READ EVERYTHING VERY CAREFULLY! Instructor James R. Evans, Ph.D. Professor Department of Operations,

More information

Statistics and Machine Learning, Master s Programme

Statistics and Machine Learning, Master s Programme DNR LIU-2017-02005 1(9) Statistics and Machine Learning, Master s Programme 120 credits Statistics and Machine Learning, Master s Programme F7MSL Valid from: 2018 Autumn semester Determined by Board of

More information

Secondary Masters in Machine Learning

Secondary Masters in Machine Learning Secondary Masters in Machine Learning Student Handbook Revised 8/20/14 Page 1 Table of Contents Introduction... 3 Program Requirements... 4 Core Courses:... 5 Electives:... 6 Double Counting Courses:...

More information

Section 1.1: Introduction

Section 1.1: Introduction Section 11: Introduction Discrete-Event Simulation: A First Course c 2006 Pearson Ed, Inc 0-13-142917-5 Discrete-Event Simulation: A First Course Section 11: Introduction 1/ 18 Introduction What is discrete-event

More information

UC - Davis Graduate School of Management. Financial Theory and Policy Spring 2013

UC - Davis Graduate School of Management. Financial Theory and Policy Spring 2013 UC - Davis Graduate School of Management Management 205 Brad M. Barber Financial Theory and Policy Spring 2013 1. General Information Office: 3218 Gallagher Telephone: (530) 752-0512 e-mail: bmbarber@ucdavis.edu

More information

Probability and Statistics Curriculum Pacing Guide

Probability and Statistics Curriculum Pacing Guide Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods

More information

ANALYSIS OF THE RELATIONSHIP BETWEEN THE PERFORMANCE AND THE COMPOSITION OF STUDENT GROUPS IN A PRODUCTION SIMULATION GAME

ANALYSIS OF THE RELATIONSHIP BETWEEN THE PERFORMANCE AND THE COMPOSITION OF STUDENT GROUPS IN A PRODUCTION SIMULATION GAME ANALYSIS OF THE RELATIONSHIP BETWEEN THE PERFORMANCE AND THE COMPOSITION OF STUDENT GROUPS IN A PRODUCTION SIMULATION GAME UZONYI-KECSKÉS Judit (HU) - KOLTAI Tamás (HU) Budapest University of Technology

More information

Investment Banking Questions

Investment Banking Questions Resources/Sample Interview Questions Investment Banking interviews have been described as "quick and painful". The banks are the first firms to interview on Grounds and the process is usually over within

More information

School of Business FINC300 Foundations in Financial Management 3 Credit Hours 8 weeks Prerequisite: None

School of Business FINC300 Foundations in Financial Management 3 Credit Hours 8 weeks Prerequisite: None School of Business FINC300 Foundations in Financial Management 3 Credit Hours 8 weeks Prerequisite: None Course Description Course Scope Course Objectives Course Delivery Method Course Resources Evaluation

More information

Index School of Economics and Finance Postgraduate Student Handbook

Index School of Economics and Finance Postgraduate Student Handbook Index School of Economics and Finance Postgraduate Student Handbook 2017-18 How to use this Handbook This handbook should be used together with the Academic Regulations and the Student Guide. This handbook

More information

Student Learning Outcomes Draft for Implementation Spring 2015

Student Learning Outcomes Draft for Implementation Spring 2015 Quantitative Reasoning Student Learning Outcomes Draft for Implementation Spring 2015 About the New Mathways Project The New Mathways Project is a systemic approach to improving student success and college

More information

Algebra II Common Core

Algebra II Common Core Core Algebra II introduces students to advanced functions, with a focus on developing a strong conceptual grasp of the expressions that define them. Students learn through discovery and application, developing

More information

SPK : MODEL DAN PENDUKUNG

SPK : MODEL DAN PENDUKUNG SPK : MODEL DAN PENDUKUNG Dasar Pengambilan Keputusan Pendekatan Sistem Proses pengambilan keputusan Fase proses pengambilan keputusan Metodologi pendukung keputusan Referensi lihat SAP : [5] Bab 2, [7]

More information

PRINCIPLES OF SEQUENCING AND SCHEDULING

PRINCIPLES OF SEQUENCING AND SCHEDULING PRINCIPLES OF SEQUENCING AND SCHEDULING Kenneth R. Baker Tuck School of Business Dartmouth College Hanover, New Hampshire Dan Trietsch College of Engineering American University of Armenia Yerevan, Armenia

More information

University of California, Berkeley Department of Statistics Statistics Undergraduate Major Information 2018

University of California, Berkeley Department of Statistics Statistics Undergraduate Major Information 2018 University of California, Berkeley Department of Statistics Statistics Undergraduate Major Information 2018 OVERVIEW and LEARNING OUTCOMES of the STATISTICS MAJOR Statisticians help design data collection

More information

How to Play Working Capital Simulation: Managing Growth

How to Play Working Capital Simulation: Managing Growth How to Play Working Capital Simulation: Managing Growth Video Transcript This short video will explain how to play the Working Capital Simulation: Managing Growth. You are the CEO of Sunflower Nutraceuticals.

More information

Curriculum for Business Economics and Information Technology

Curriculum for Business Economics and Information Technology Curriculum for Business Economics and Information Technology University of Southern Denmark August 2012 1 General regulations for all institutions providing the programme Curriculum Applicable for Business

More information

HKUST Business School Department of Economics

HKUST Business School Department of Economics HKUST Business School Department of Economics Financial Economics I: Portfolio Analysis Spring 2017 Instructor: Prof. Fei DING Office: LSK 6073 Phone: 2358-7626 E-mail: feiding@ust.hk Office hours: MoWe

More information

STID Statistics and Business Intelligence

STID Statistics and Business Intelligence STID Statistics and Business Intelligence IUT Roubaix Lille 2 University France Sylvia CANONNE Description of teaching modules. September 2014 3 Course descriptions subject to change Term 1 M1101A -Mathematics

More information

AECN 436: Commodity Price Forecasting A Peer Review of Teaching Project Inquiry Portfolio

AECN 436: Commodity Price Forecasting A Peer Review of Teaching Project Inquiry Portfolio University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln UNL Faculty Course Portfolios Peer Review of Teaching Project 2017 AECN 436: Commodity Price Forecasting A Peer Review of

More information

Business Administration

Business Administration Business Administration The Business Administration program offers study in finance, management, marketing, entrepreneurship/small business management, international business, management information systems,

More information

Pre-MBA Accounting & Finance Program

Pre-MBA Accounting & Finance Program Pre-MBA Accounting & Finance Program Tel: 05-8179688 or 01-3768066 Page 1 of 8 Pre-MBA Accounting & Finance Program Ace your MBA study with The Pre-MBA Accounting & Finance Program by MBAThinkTank! Thorough

More information

A Modesto City School Joseph A. Gregori High School 3701 Pirrone Road, Modesto, CA (209) FAX (209)

A Modesto City School Joseph A. Gregori High School 3701 Pirrone Road, Modesto, CA (209) FAX (209) A Modesto City School Joseph A. Gregori High School 3701 Pirrone Road, Modesto, CA 95356 (09) 550-340 FAX (09) 550-3433 May 4, 016 AP Statistics Parent(s): I am very excited to have your student in AP

More information

MULTI-CRITERIA DECISION-MAKING IN MANAGEMENT UNDER CONDITIONS OF RISK AND UNCERTAINTY

MULTI-CRITERIA DECISION-MAKING IN MANAGEMENT UNDER CONDITIONS OF RISK AND UNCERTAINTY MULTI-CRITERIA DECISION-MAKING IN MANAGEMENT UNDER CONDITIONS OF RISK AND Karel CHOBOT VSB Technical University of Ostrava, Ostrava, Czech Republic, EU, k.chobot@seznam.cz Abstract Decision-making processes

More information

ACTL5103 Stochastic Modelling For Actuaries. Course Outline Semester 2, 2014

ACTL5103 Stochastic Modelling For Actuaries. Course Outline Semester 2, 2014 UNSW Australia Business School School of Risk and Actuarial Studies ACTL5103 Stochastic Modelling For Actuaries Course Outline Semester 2, 2014 Part A: Course-Specific Information Please consult Part B

More information

Dudon Wai Georgia Institute of Technology CS 7641: Machine Learning Atlanta, GA

Dudon Wai Georgia Institute of Technology CS 7641: Machine Learning Atlanta, GA Adult Income and Letter Recognition - Supervised Learning Report An objective look at classifier performance for predicting adult income and Letter Recognition Dudon Wai Georgia Institute of Technology

More information

I. III. ANALYSIS TOOLS

I. III. ANALYSIS TOOLS I. III. ANALYSIS TOOLS A. INTRODUCTION Conducting the Top Down and Bottom Up analysis as described in the previous chapter required an extensive analysis effort. This analysis examined a variety of different

More information

GENERAL BUSINESS (GEN BUS)

GENERAL BUSINESS (GEN BUS) General Business (GEN BUS) 1 GENERAL (GEN BUS) GEN BUS 100 INTRODUCTION TO Introduction to the basic concepts, practices and analytical methods that are part of the market enterprise system. Overview of

More information

Finance 357: Business Finance

Finance 357: Business Finance Finance 357: Business Finance (03075) Time: Tuesdays and Thursdays 2:00-3:30 pm Venue: UTC 1.132 Instructor: Woochan Kim Office: CBA 6.304D Office Hours: Fridays 9-11, others by appointment Tel: 512-232-6823

More information

Mathematical Modeling

Mathematical Modeling Mathematical Modeling I. UNIT OVERVIEW & PURPOSE: Students will gain a deeper understanding of the use of polynomial, exponential, and logarithmic functions by applying them to real-world situations including

More information

Course Descriptions. Graduate Courses in Economics

Course Descriptions. Graduate Courses in Economics Graduate Courses in Economics Course Descriptions ECO 5301 Microeconomics Theory I (3 semester hours) Modern approaches to the theory of the firm, the theory of the consumer, and formal relationships among

More information

Online Course Syllabus FIN440 Financial Analysis, Forecasting and Planning. Important Notes:

Online Course Syllabus FIN440 Financial Analysis, Forecasting and Planning. Important Notes: Online Course Syllabus FIN440 Financial Analysis, Forecasting and Planning Important Notes: This document provides an overview of expectations for this online course and is subject to change prior to the

More information

GLOBAL EDITION. Using and Understanding Mathematics. A Quantitative Reasoning Approach SIXTH EDITION. Jeffrey Bennett William Briggs

GLOBAL EDITION. Using and Understanding Mathematics. A Quantitative Reasoning Approach SIXTH EDITION. Jeffrey Bennett William Briggs GLOBAL EDITION Using and Understanding Mathematics A Quantitative Reasoning Approach SIXTH EDITION Jeffrey Bennett William Briggs Why Should you Care About Quantitative reasoning? Quantitative reasoning

More information

AN INQUIRY-BASED APPROACH FOR TEACHING STUDENTS TO FORMULATE LINEAR PROGRAMMING MODELS

AN INQUIRY-BASED APPROACH FOR TEACHING STUDENTS TO FORMULATE LINEAR PROGRAMMING MODELS AN INQUIRY-BASED APPROACH FOR TEACHING STUDENTS TO FORMULATE LINEAR PROGRAMMING MODELS Emma Jane Riddle, College of Business, Winthrop University, Rock Hill, South Carolina, 29733, riddlee@winthrop.edu

More information

Education & Training Plan Business Math Specialist Certificate Program with Externship. Business Math Specialist Certificate Program with Externship

Education & Training Plan Business Math Specialist Certificate Program with Externship. Business Math Specialist Certificate Program with Externship C.15.32 (Created 07-17-2017) AUBURN OHICE OF P ROFESSIONAL AND CONTINUING EDUCATION Office of Professional & Continuing Education 301 OD Smith Hall Auburn, AL 36849 http://www.auburn.edu/mycaa Contact:

More information

Syllabus for FIN 338 Financial Management 3 Credit Hours Fall 2013

Syllabus for FIN 338 Financial Management 3 Credit Hours Fall 2013 Syllabus for FIN 338 Financial Management 3 Credit Hours Fall 2013 I. COURSE DESCRIPTION A study of the basic principles and theories of business finance including the tax environment, cashflow analysis,

More information

Chapter 127. Texas Essential Knowledge and Skills for Career Development. Subchapter B. High School

Chapter 127. Texas Essential Knowledge and Skills for Career Development. Subchapter B. High School High School 127.B. Chapter 127. Texas Essential Knowledge and Skills for Career Development Subchapter B. High School Statutory Authority: The provisions of this Subchapter B issued under the Texas Education

More information

CHAPTER 11 DECISION SUPPORT SYSTEMS

CHAPTER 11 DECISION SUPPORT SYSTEMS CHAPTER 11 DECISION SUPPORT SYSTEMS Management Information Systems, 10 th edition, By Raymond McLeod, Jr. and George P. Schell 2007, Prentice Hall, Inc. 1 Learning Objectives Understand the fundamentals

More information

Master of International Business 1 st Semester Module Catalogue Winter Semester 2012/13

Master of International Business 1 st Semester Module Catalogue Winter Semester 2012/13 Master of International Business 1 st Semester Module Catalogue Winter Semester 2012/13 Table of contents: Module 1: Principles of Economics... 3 Module 2: Principles of Accounting... 5 Module 3: Principles

More information

GUIDE TO GRADUATION. B.S. in. Academic Year

GUIDE TO GRADUATION. B.S. in. Academic Year GUIDE TO GRADUATION Academic Year 2008-2009 B.S. in Real Estate AND Economic Development www.ubalt.edu/realestate The Merrick School of Business is committed to providing the guidance and support you need

More information

Scope & Sequence Elementary School Programs

Scope & Sequence Elementary School Programs Scope & Sequence Elementary School Programs * Success signify a program s primary focus; however, each : Programs are delivered to students through an integration of face-to-face volunteer and teacher

More information

Warehouse-Manufacturing Team Lead Certification Program

Warehouse-Manufacturing Team Lead Certification Program Warehouse-Manufacturing Team Lead Certification Program Course Descriptions Unit I: Week 1 - Inventory Management Tools In Unit One you will be introduced to one of the most powerful tools available for

More information

Profession Degree Programme in Marketing Management

Profession Degree Programme in Marketing Management Zealand Institute of Business and Technology Curriculum Profession Degree Programme in Marketing Management September 2015 TABLE OF CONTENTS 1. Curriculum framework... 3 1.1 Commencement of the curriculum...

More information

Unit title: Analysis of Scientific Data and Information

Unit title: Analysis of Scientific Data and Information Unit title: Analysis of Scientific Data and Information Unit code: F/601/0220 QCF level: 4 Credit value: 15 Aim This unit develops skills in mathematical and statistical techniques used in the analysis

More information