About this course

Want to know how to avoid bad decisions with data?

Making good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.

These big picture ideas have motivated the development of quantitative models, but in most traditional statistics courses, these concepts get lost behind a wall of little techniques and computations. In this course we keep the focus on the ideas that really matter, and we illustrate them with lively, practical, accessible examples.

We will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?

This course will be is relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor’s degree contemplating graduate study in a business field.

Opportunities in analytics are abundant at the moment. Specific techniques or software packages may be helpful in landing first jobs, but those techniques and packages may soon be replaced by something newer and trendier. Understanding the ways in which quantitative models really work, however, is a management level skill that is unlikely to go out of style.

What you’ll learn

  • Variability in the real world and implications for decision making
  • Data types and data quality with appropriate visualizations
  • Apply data analysis to managerial decisions, especially in start-ups
  • Making effective decisions from no data to big data (what should we collect and then what do we do with all this data?)

Meet your instructors

Rick Cleary

Professor Rick Cleary is a statistician and mathematician with research and consulting interests in a variety of fields including sports, biomechanics, and statistical approaches to fraud detection and audit risk. Prior to coming to Babson College in 2013, Professor Cleary taught at St. Michael’s College in Vermont, Cornell University, Bentley University, and Harvard University. He has held many leadership positions in the Mathematical Association of America, including six years on the Executive Committee as Associate Treasurer and a term as chair of the Joint Data Committee. He is currently on the Nominations Committee and the Polya lecturer selection committee. Professor Cleary enjoys working with mathematics teachers at all levels to improve statistics education and he gives frequent talks and workshops on ways to encourage statistical thinking.

Nathan Karst

Dr. Nathan Karst received his B.S. in electrical and computer engineering from Franklin W. Olin College of Engineering in 2007 and his doctorate in applied mathematics from Cornell. He is an avid teacher and researcher, having won the Dean’s Award for Excellence in Undergraduate Teaching in 2014 and the Dean’s Award for Excellence in Scholarship in 2015. Most recently, his research has focused on the role of nonlinear dynamics in microvascular networks and event-scale streamflow recession variability.

Davit Khachatryan

Dr. Davit Khachatryan is an Assistant Professor of Statistics and Analytics at Babson College. He is an applied statistician with research interests in analyzing intellectual property data to study the formation and diffusion of knowledge in emerging industries. Davit’s current and former research has produced publications in academic, peer-reviewed journals such as Journal of the Royal Statistical Society (Series C), The American Statistician, IEEE Transactions on Engineering Management (forthcoming), and Quality and Reliability Engineering International. Prior to joining Babson College, Davit was a Senior Associate at the National Economics and Statistics practice of Pricewaterhouse Coopers (PwC). In the latter role he consulted in the area of predictive modeling and advanced data analytics, helping clients from financial, healthcare, and government sectors with building automatic predictive models and enhancing business intelligence solutions. Davit has earned his B.S. in Applied Mathematics and Informatics from Yerevan State University, M.S. in Statistics and Ph.D. in Management Science from the University of Massachusetts, Amherst.

George Recck

Mr. Recck has taught at Babson College since 1984. He currently serves as the Chair of the Business Analytics/Statistics Education special interest group for the American Statistical Association (ASA). Mr. Recck is also the founder of Total Information, Inc., an consulting firm specializing in providing information service to small businesses.

Babak Zafari

†Dr. Zafari is an Assistant Professor of Analytics and Statistics in the Math & Science Division. His area of interests are Predictive Modeling and Data Mining Methods for Business Applications, Bayesian Statistics, Healthcare Fraud Analytics and Online Auctions. Prior to joining Babson College, he was a Visiting Assistant Professor at The George Washington University School of Business teaching courses in Data Analysis and Decisions, Business Analytics and Data Mining. He was also a senior statistician consultant at Integrity Management Services where he was responsible for developing statistical models for fraud detection in Medicare and Medicaid programs. He received his B.S. in Applied Mathematics from Sharif University of Technology, M.S. in Operations Research/Computer Science from Bowling Green State University and Ph.D. in Decision Sciences from The George Washington University School of Business.

About MIT horizon

MIT Horizon is an expansive content library built to help you explore emerging technologies. Through easy-to-understand lessons, you’ll be guided through the complexities of the latest technologies and simplified expert-level concepts. Designed for both technical and non-technical learners, you can examine bite-size content that can lead to maximum career outcomes.

For a limited time, gain access to the complete MIT Horizon library.

Register today for exclusive entry.

About this course

Research from the World Economic Forum (WEF) and Mckinsey shows that AI will increasingly disrupt what we do, who does it and how all work is done – e.g. humans versus machines. On the positive side, AI is expected to add significant growth and value to the world’s economy for the companies and countries that get it. As such, it is more important than ever that all leaders, managers, executives and board members develop their AI skills to compete and prosper in the AI world.

However, most leaders, executives and board members lack the necessary AI education, skills, strategies and tactics to create AI-powered business models with platform and network effects. Further, they don’t understand how AI will impact their customers, employees, investors, operations and product/service offerings.

WHAT TO EXPECT

AI for Leaders features a series of lessons with video lectures, real world case studies, and hands on practice sessions that will help you learn the skills you need to advance your company and career. In addition, you will learn how to leverage today’s AI capabilities to improve your organization’s:

a). Customer offerings and interactions,
b). Employee engagement and capabilities,
c). Operations,
d). Competitive positioning, and
e). The 7 attributes of AI centered leadership.

Finally, our program provides 5 clear steps, which we call PIVOT – that help you and your organization build today’s modern business model – along with a capstone project focused on how you build your own AI powered (autonomous) business model.

WHAT THIS COURSE CONTAINS

To ensure your success as a leader in the AI world, this course contains:

  1. 40+ videos
  2. Lectures from renowned faculty and business practitioners
  3. Real-world case studies
  4. 25+ exercises
  5. Preeminent articles from world class publications including HBR, Forbes and MITSMR

WHO SHOULD TAKE THIS COURSE

All leaders, board members, executives and team leaders at all types of organizations and at all levels should take this course. Further if you are looking to rise to a new role in your company, this course will arm you with the tools and techniques you need to drive your career and organization into the world of AI powered platforms and join companies like, Amazon, Apple, Alphabet, Uber and Airbnb who are at the forefront of this revolution.

What you’ll learn

  • How platform business models and AI technologies complement each other.
  • The characteristics of leaders that embrace AI powered platform business models.
  • Where to look for data and what data is valuable to your business and AI.
  • How you can get started and the 5 steps for success – which we call PIVOT.
  • How your organization and team can catch-up with today’s leaders.
  • The economics of these new technologies and business models.
  • The 7 attributes of AI led organizations.

Meet your instructor

Thomas Davenport

Tom Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, cofounder of the International Institute for Analytics, Fellow at the MIT Initiative on the Digital Economy, and Senior Advisor to Deloitte Analytics. He teaches analytics/big data in executive programs at Babson, Harvard Business School and School of Public Health, and MIT Sloan School. Davenport pioneered the concept of “competing on analytics” with his best-selling 2006 Harvard Business Review article and 2007 book. His most recent book (with Julia Kirby) is Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. He wrote or edited seventeen other books and over 100 articles for Harvard Business Review, Sloan Management Review, The Financial Times, and many other publications. He is a regular contributor to the Wall Street Journal. He has been named one of the top 25 consultants by Consulting News, one of the 100 most influential people in the IT industry by Ziff-Davis, and one of the world’s top fifty business school professors by Fortune magazine.

Megan Beck

Megan Beck is the Chief Product Officer at AIMatters, a digital startup that uses alternative data and AI to reframe how companies are valued in the era of transformational technology such social, mobile, big data, and machine learning. AIMatters uses new datasets to create AI-based products and services to help leaders, organizations, and investors manage and invest more wisely. Megan leads research, publication, curriculum, and new product initiatives for AIMatters. Megan coauthored The Network Imperative: How to Survive and Grow in the Age of Digital Business Models, published by the Harvard Business Review Press in 2016. She has published articles with Forbes, Harvard Business Review, The Wall Street Journal and many others and gives key notes on topics such as business model transformation, the gig economy, artificial intelligence, alternative data, and women in the workforce. Entering the workforce as an engineer, Megan later transitioned to strategy consulting and spent several years at Bain & Company before leaving to advise clients directly in her areas of expertise. A loyal Longhorn, she received a BS in Computer Science and a BA in the Plan II Honors Program at the University of Texas at Austin. She holds an MBA from the McCombs School of Business and resides in Dallas, TX.

Barry Libert

Barry is a digital board member, CEO and board advisor. He is the founder and chairman of AIMatters, Inc., an AI start up that is reinventing consulting. He is an expert in digital business models – particularly platforms, networks and AI. Barry served as a senior fellow at The Wharton School where he led their research into new business models (platforms with network effect) at the SEI Center since 2011. Barry has spent the last 6 years advising CEOs and boards on how to transform their business models from product to platform, customer to network, and data to AI in order to achieve exponential growth and value. His startup portfolio companies, collectively, have built and managed more than 15,000 customer and employee networks with more than 40 million users for 350 brands with more than 100 million fans. Current boards include Enterprise Community Inc., Bellwether, Wharton’s SEI Center, as well as a variety of start-ups. Past and present institutional clients include Barrick Gold, iRobot, Salesforce, Microsoft, GE Healthcare, Sun Life, Goldman Sachs, Deloitte, PWC, and ESPN. Barry is an active columnist for HBR, Forbes, Knowledge at Warton and MIT’s Sloan Management Review (SMR). Starting in 2018, he will add CIO to his monthly features. His sixth book, The Network Imperative was published in June 2016 by Harvard Business Review (HBR) Press. This book, along with his others – including We Are Smarter than Me and Social Nation - focuses on why and how digital business models outperform all other types. Mr. Libert has delivered more than 500+ keynote speeches to 50,000 people globally at leading industry conferences, private corporate events, and institutional investor gatherings. He has published 1,250+ articles in such periodicals as Harvard Business Review, The Wall Street Journal, Newsweek, Barron's, and The New York Times. He has also appeared on CNN, CNBC, Fox News Network, NPR, and Facebook Live. Barry began his career with McKinsey & Company, is a graduate of Tufts University (Magna Cum Laude) and holds an MBA from Columbia University (Beta Gamma Sigma).

About this course

How do you find the money necessary to effectively manage your business? How do you know if a business opportunity is worthwhile? When should you invest in a stock, bond or company? Do you fear the financial side of growing your organization?

This finance course will take the mystery out of financial analysis and help you make the right business decisions. In order to establish your company you need to secure funding. Once you have money, you need to determine the most efficient and effective use of your capital. You also need to have the knowledge to have professional and engaging conversations with finance professionals who control access to funding.

In this course, you will discover a variety of options for funding your business and how to successfully negotiate financial opportunities. You will learn how to value and evaluate ideas to determine the appropriate benefits and costs in order to screen them correctly. Finally, you will learn how to value a business and the securities you can use to potentially fund your organization.

What you’ll learn

  • Tools and techniques for funding a growing business
  • How to evaluate an idea for a new product or service and determine cost and benefits
  • How to value a stock, bond, and company for business opportunities

Meet your instructor

Mark Potter

Mark Potter, Ph.D., is a Professor of Finance and the Associate Dean of the Graduate School at Babson College.​ Dr. Potter has been at Babson College since 1995, teaching over one hundred courses at the undergraduate, graduate and executive education levels spanning a myriad of topics that includes investments, corporate finance, derivatives, and international finance. In the past five years, he has worked with companies in the consumer services, medical device, high technology, financial services, and litigation support industries. Professor Potter’s areas of expertise includes investments and portfolio performance, behavioral finance, and alternative investment strategies. His research has appeared in the Journal of Portfolio Management, Journal of Business Finance and Accounting, Journal of Financial Research, Journal of Alternative Investments, Journal of Financial Education, Mergers & Acquisitions, and Derivatives Quarterly, among others. His work has also been featured in the Wall Street Journal, Kiplinger’s, and CFA Digest.

About this course

Are financial statements a mystery to you? Do all those terms and metrics make your head spin? Do you avoid conversations with your finance leaders because you are not confident of your finance ability?

Having a solid understanding of financial terms, statements and metrics is critical to becoming a successful entrepreneur or manager. In this finance course, you will learn how to interpret and use the information contained in financial statements to make key operating decisions, evaluate business performance, and create forecasts of profits and cash flow.

This course introduces you to the form, content and definitions included in the primary financial statements: income statement, balance sheet, and cash flow statement. You will learn how to use this information to make key operating decisions, such as how to balance growth with cash constraints. You will learn how to use ratios to diagnose a company’s financial health and apply these concepts and tools to evaluate a company of your own choosing.

Eliminate your fear of accounting! Financial accounting can be fun once the barriers to learning are broken down. Through a series of learning scenarios that take you through the creation of a simple business, you will become comfortable with basic accounting tools and concepts that you need to more effectively manage your business. By the end of the course, you will become a much more confident user of financial information and will be able to effectively engage with your finance leaders.

What you’ll learn

  • How primary financial statements are constructed and what types of information is captured in each statement
  • How cash and profits can differ and how to use that information to effectively manage growth
  • How to calculate key performance ratios and use those metrics to evaluate the performance of your business
  • How to create a financial forecast that you can use to better manage your business and to present to potential investors and creditors

Syllabus

Week 1: Balance Sheet and Transaction Analysis

  • Why do we need financial statements? What purpose do they serve?
  • What kind of information is contained in the balance sheet?
  • How are day-to-day operating decisions captured and recorded?
  • How are start-up activities (e.g., raising capital, investing in your business) reflected in the financial statements?

Week 2: Income Statement and Cash Flows

  • How do sales and operating expenses impact earnings and cash flow?
  • What are the early warning signs that my business may run out of cash?
  • Is all growth the same? What is the difference between good growth and bad growth?

Week 3: Ratio Analysis

  • What are the key ratios for evaluating profitability, return on investment, and liquidity or solvency?
  • How well is my business performing? How are my competitors doing?
  • How do different strategies and business models reveal themselves in the financial statements?

Week 4: Forecasting

  • How do I translate my growth plans into a forecast of profits and cash flow?
  • How can a forecast help me manage my business more effectively?
  • What do potential investors or creditors look for in a forecast?

Meet your instructor

Peter Wilson

Peter Wilson currently serves on the faculty of Babson College where he teaches courses in financial reporting and financial statement analysis in the Accounting and Law Division. He has served as the Executive Director of Graduate Blended Learning Programs at Babson overseeing Babson’s Blended Learning MBA program delivered on campuses in Wellesley and San Francisco. Dr. Wilson holds a BA degree in Philosophy from the University of North Carolina at Chapel Hill, an MBA degree from UNC-Greensboro, and a Ph.D. degree in Accounting from the University of North Carolina at Chapel Hill.

About This Course:

Demand is a simple yet challenging concept that is essential to understanding how markets function. In this economics course, you will gain a solid understanding of demand, its underlying principles, major determinants and how they are beneficial for individuals, decision makers inside the firm, and policy makers.

During your time in this course, you will discover how managers can better understand the impact of pricing changes on units sold, revenue and the relationship between products in order to inform strategic planning. You will learn how many programs and policies are designed to change how individuals and businesses interact in the market and you will gain the tools to identify them. Models of consumer choice and demand will guide you in thinking about how individual incentives change and what the likely impact will be of those changes.

All people respond to changing market conditions, but the type and magnitude of those responses can be better understood through the economic model of consumer demand. You will learn how business strategy can benefit from a strong appreciation of elasticity, determinants of demand and how consumers make decisions.

What You’ll Learn:

  • Why demand is a negative relationship between price and quantity
  • How to identify market changes that will impact demand
  • How to define the price elasticity of demand
  • The relationship between price elasticity of demand and changing market conditions

Meet Your Instructor:

John Korsak

John Korsak received his BA in Economics and Philosophy from Boston College, his MA and PhD in Economics from Clark University. Korsak has worked as a comptroller in political campaigns and as a product marketing manager for an internet software company. He has been a featured speaker at Comnet, worked on a project to provide an online economics class for the Commonwealth of Virginia and has been a recurring panelist at the Colleges of the Fenway teach-in on global climate change. His fields of interest are poverty research, behavioral economics and statistical measurement. He has taught classes on Microeconomics, Macroeconomics, Game Theory, Statistics, Probability and the Economics of Everyday Life.

About this course

Want to know how to avoid bad decisions with data?

Making good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.

These big picture ideas have motivated the development of quantitative models, but in most traditional statistics courses, these concepts get lost behind a wall of little techniques and computations. In this course we keep the focus on the ideas that really matter, and we illustrate them with lively, practical, accessible examples.

We will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?

This course will be is relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor’s degree contemplating graduate study in a business field.

Opportunities in analytics are abundant at the moment. Specific techniques or software packages may be helpful in landing first jobs, but those techniques and packages may soon be replaced by something newer and trendier. Understanding the ways in which quantitative models really work, however, is a management level skill that is unlikely to go out of style.

What you’ll learn

  • Variability in the real world and implications for decision making
  • Data types and data quality with appropriate visualizations
  • Apply data analysis to managerial decisions, especially in start-ups
  • Making effective decisions from no data to big data (what should we collect and then what do we do with all this data?)

Meet your instructors

Rick Cleary

Professor Rick Cleary is a statistician and mathematician with research and consulting interests in a variety of fields including sports, biomechanics, and statistical approaches to fraud detection and audit risk. Prior to coming to Babson College in 2013, Professor Cleary taught at St. Michael’s College in Vermont, Cornell University, Bentley University, and Harvard University. He has held many leadership positions in the Mathematical Association of America, including six years on the Executive Committee as Associate Treasurer and a term as chair of the Joint Data Committee. He is currently on the Nominations Committee and the Polya lecturer selection committee. Professor Cleary enjoys working with mathematics teachers at all levels to improve statistics education and he gives frequent talks and workshops on ways to encourage statistical thinking.

Nathan Karst

Dr. Nathan Karst received his B.S. in electrical and computer engineering from Franklin W. Olin College of Engineering in 2007 and his doctorate in applied mathematics from Cornell. He is an avid teacher and researcher, having won the Dean’s Award for Excellence in Undergraduate Teaching in 2014 and the Dean’s Award for Excellence in Scholarship in 2015. Most recently, his research has focused on the role of nonlinear dynamics in microvascular networks and event-scale streamflow recession variability.

Davit Khachatryan

Dr. Davit Khachatryan is an Assistant Professor of Statistics and Analytics at Babson College. He is an applied statistician with research interests in analyzing intellectual property data to study the formation and diffusion of knowledge in emerging industries. Davit’s current and former research has produced publications in academic, peer-reviewed journals such as Journal of the Royal Statistical Society (Series C), The American Statistician, IEEE Transactions on Engineering Management (forthcoming), and Quality and Reliability Engineering International. Prior to joining Babson College, Davit was a Senior Associate at the National Economics and Statistics practice of Pricewaterhouse Coopers (PwC). In the latter role he consulted in the area of predictive modeling and advanced data analytics, helping clients from financial, healthcare, and government sectors with building automatic predictive models and enhancing business intelligence solutions. Davit has earned his B.S. in Applied Mathematics and Informatics from Yerevan State University, M.S. in Statistics and Ph.D. in Management Science from the University of Massachusetts, Amherst.

George Recck

Mr. Recck has taught at Babson College since 1984. He currently serves as the Chair of the Business Analytics/Statistics Education special interest group for the American Statistical Association (ASA). Mr. Recck is also the founder of Total Information, Inc., an consulting firm specializing in providing information service to small businesses.

Babak Zafari

†Dr. Zafari is an Assistant Professor of Analytics and Statistics in the Math & Science Division. His area of interests are Predictive Modeling and Data Mining Methods for Business Applications, Bayesian Statistics, Healthcare Fraud Analytics and Online Auctions. Prior to joining Babson College, he was a Visiting Assistant Professor at The George Washington University School of Business teaching courses in Data Analysis and Decisions, Business Analytics and Data Mining. He was also a senior statistician consultant at Integrity Management Services where he was responsible for developing statistical models for fraud detection in Medicare and Medicaid programs. He received his B.S. in Applied Mathematics from Sharif University of Technology, M.S. in Operations Research/Computer Science from Bowling Green State University and Ph.D. in Decision Sciences from The George Washington University School of Business.