About This Course:

The Business and Professional Communications for Success program will provide learners with the essential knowledge to create appropriate business messages and apply proper business writing techniques in their written business communications, all while working in diverse environments. This program also examines the various types of business presentations in the work environment and allows learners to apply their knowledge to create and present their work.

Business Writing Techniques will expand on the different communication styles of business writers. We will discuss business writers’ best practices by providing real-world scenarios and applications such as proofreading and rewriting. Learners will examine how to use the 6 C’s to enhance their business messages. Learners will also discuss the proper etiquette of business writing and examine the use of emoji in business communications.

What You’ll Learn:

By the end of this course learners will be able to:

 Identify Parts of a business letter

 Examine the 6 C’s for business messages

 Evaluate Direct Vs. Indirect messages

 Examine the use of emojis in the business setting

Meet Your Instructor:

Debora Sepich - Pearson Advance

Debora Sepich

EdD MBA at Doane University
Debora Sepich is an entrepreneur turned educator who has spent the last 15 years blurring the lines between the world of work and the halls of education. Debora is the Director of Graduate Business and Technology Programs at Doane University. Prior to working at Doane University, she founded Dolphin Software, an environmental health and safety software company focused on supporting pharmaceutical companies, hospitals and other highly regulated industries focus on protecting people, the planet while also making sustainable profits. Debora is delighted to be marrying her years of applied business experience with online education.

About This Course:

The Business and Professional Communications for Success program will provide learners with the essential knowledge of effective business communications to aid in their company’s success. Learners will create appropriate business messages through business letters and messaging and apply proper business communications techniques and business writing techniques. Learners will examine the basics of business communication while working in diverse business environments with diverse team members. This program also examines the various types of business presentations in the work environment. It allows learners to apply their knowledge to create and present their work for internal and external communication.

Learners will engage with the fundamentals of business communication. Learners will discover different communication styles and how to address them in a business setting. Learners will also assess their listening styles and emotional intelligence and how both can affect their communication skills.

What You’ll Learn:

  • By the end of this course learners will be able to:
  • Define communication
  • Examine the communication principles
  • Identify the Communication Model
  • Identify ways to effectively listen
  • Identify their communication style
  • Examine Emotional Intelligence and its use in communication

Meet Your Instructor:

Debora Sepich - Pearson Advance

Debora Sepich

EdD MBA at Doane University
Debora Sepich is an entrepreneur turned educator who has spent the last 15 years blurring the lines between the world of work and the halls of education. Debora is the Director of Graduate Business and Technology Programs at Doane University. Prior to working at Doane University, she founded Dolphin Software, an environmental health and safety software company focused on supporting pharmaceutical companies, hospitals and other highly regulated industries focus on protecting people, the planet while also making sustainable profits. Debora is delighted to be marrying her years of applied business experience with online education.

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.

What is a bootcamp?

Our facilitated bootcamps focus on rapid skill acquisition by progressing you through a standard course on an accelerated schedule with peers who are committed to progressing on pace. Our bootcamps include:

  • Live kick-off event
  • Instructor facilitated Q&A for expert feedback and coaching
  • Learner Success Support: welcome call, advising sessions, personalized pace reminders
  • 24/7 help desk

About This Course:

In this bootcamp you will learn how data scientists exercise statistical thinking in designing data collection, derive insights from visualizing data, obtain supporting evidence for data-based decisions and construct models for predicting future trends from data.

This course can be used towards completion of a Professional Certificate in Data Science for Executives.

What You Will Learn:

  • Data collection, analysis and inference
  • Data classification to identify key traits and customers
  • Conditional Probability-How to judge the probability of an event, based on certain conditions
  • How to use Bayesian modeling and inference for forecasting and studying public opinion
  • Basics of Linear Regression
  • Data Visualization: How to create use data to create compelling graphics

Meet Your Instructors:

Andrew Gelman

Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.
David Madigan - Pearson Advance

David Madigan

David Madigan received a bachelor’s degree in Mathematical Sciences and a Ph.D. in Statistics, both from Trinity College Dublin. He has previously worked for AT&T Inc., Soliloquy Inc., the University of Washington, Rutgers University, and SkillSoft, Inc. He has over 100 publications in such areas as Bayesian statistics, text mining, Monte Carlo methods, pharmacovigilance and probabilistic graphical models. He is an elected Fellow of the American Statistical Association and of the Institute of Mathematical Statistics. He recently completed a term as Editor-in-Chief of Statistical Science.

Lauren Hannah

Lauren Hannah is an Assistant Professor in the Department of Statistics at Columbia University. Dr. Hannah received a Ph.D. in Operations Research and Financial Engineering from Princeton University, and an A.B. in Classics, again from Princeton University. After completing her Ph.D., Dr. Hannah completed a postdoc at Duke in the Statistical Science Department. Her interests include machine learning, Bayesian statistics, and energy applications.

Eva Ascarza

Eva Ascarza is an Assistant Professor of Marketing at Columbia Business School. She is a marketing modeler who uses tools from statistics and economics to answer marketing questions. Her main research areas are customer analytics and pricing in the context of subscription businesses. She specializes in understanding and predicting changes in customer behavior, such as customer retention and usage. Another stream of her research focuses on developing statistical methodologies to be used by marketing practitioners. She received her PhD from London Business School (UK) and a MS in Economics and Finance from Universidad de Navarra (Spain).

About this course

Businesses today have access to an increasingly large amount of detailed customer data, and this influx of “big data” is only going to continue. Combined with a detailed history of marketing actions, there is a newfound potential for deriving actionable insights, but you need the tools to do so. Using real-world applications from various industries, this course will help you understand the tools and strategies used to make data-driven decisions that you can put to use in your own company or business.

This valuable data may include in-store and online customer transactions, customer surveys, web analytics, as well as prices and advertising. You’ll also learn how to assess critical managerial problems, develop relevant hypotheses, analyze data and, most importantly, draw inferences to create convincing narratives which yield actionable results. Artificial intelligence and machine learning will be explored as tools to deepen analytical skills and acumen and hone decision making.

This comprehensive exploration into digital marketing analytics tools and techniques is critical knowledge for any marketing influencers, digital marketing analysts and product and brand decision makers within small and medium businesses as well as larger organizations with international reach.

What you’ll learn

  • SEO and SEM – KPIs and keyword strategies
  • Web Analytics – A/B Testing
  • Recommendation Systems
  • Machine Learning/AI applications/Big Data
  • Text and Image Analysis
  • Attribution/MMM

Meet your instructor

Michael Trusov

Michael Trusov is Professor of Marketing at the Robert H. Smith School of Business at the University of Maryland. He is also the Academic Director of the MS in Marketing Analytics program. He received his Ph.D. degree from the Anderson School of Management at UCLA. He also holds a Master's degree in Computer Science and a Master's degree in Business Administration. **** His research interests include Internet Marketing (social media marketing, search engine marketing, social networks, clickstream analysis, electronic word-of-mouth marketing, e-commerce, recommendation systems, consumer-generated content), Text Analysis, Eye-tracking and Data Mining. **** Professor Trusov has extensive industry experience. He spent seven years working in the area of software development and IT consulting in the Southern California region, specializing in marketing automation, database management, Internet applications, and e-commerce. He teaches Digital Marketing and Digital Analytics courses across various programs at the University of Maryland.

Liye Ma

Liye Ma teaches the undergraduate Marketing Research Methods class and the Data Science class in the MS Marketing Analytics program. His research focuses on the dynamic interactions of firms and consumers on Internet, social media, and mobile platforms. His work has been published in various journals including Marketing Science, Journal of Marketing Research, Management Science, and Information Systems Research. He joined the Marketing Department in Fall 2011 after obtaining the Ph.D. degree from the Tepper School of Business at Carnegie Mellon University.

About this course

In this course, you will gain an understanding of time-honored financial concepts and rules, and how these can be applied to value firms, bonds, and stocks.

We will cover the time value of money, cost of capital and capital budgeting. You will be using Excel for many process including valuing bonds and stocks, computing NPV and finding IRR.

An introductory finance course that is required for all first-year MBA students at Columbia Business School, the course is taught by a world-class instructor, actively training the next generation of market leaders on Wall Street.

Participants from all backgrounds will be prepared to participate on the ever-evolving financial playing field.

What you’ll learn

  • How to value any asset
  • Decide which projects to take out of the many a corporation might be considering
  • Compute the return on any project
  • Compute the value that a project adds
  • Value a bond and compute its yield
  • Value a stock using a simple model (i.e., determine the fair price of a stock)


Week 1: The Time Value of Money & Present Value
Week 2: Net Present Value & The Internal Rate of Return Rule
Week 3: Capital Budgeting
Week 4: Valuation of Bonds and Stocks

Meet your instructor

Daniel Wolfenzon

Daniel Wolfenzon is the Stefan H. Robock Professor of Finance and Economics at Columbia Business School. He received a Masters and a PhD in economics from Harvard University and holds a BS in economics and a BS in mechanical engineering from MIT. He is also a Faculty Research Fellow at the National Bureau of Economic Research. Areas of Research: His research interests are in corporate finance and organizational economics. He has studied control sharing in small firms, the effects of investor protection on ownership concentration, and the structure of business groups around the world. His most recent research focuses on family firms. He has examined the consequences of family succession on firm performance and also the importance of managerial talent in family controlled firms.

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.


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.


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


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

يتولى اقتصاديون من صندوق النقد الدولي مهمة تعريف الدعم بأشكاله وقياس حجمه ومعرفة انعكاساته الاقتصادية والاجتماعية والبيئية، وذلك في القسم الأول من الدورة. وتحاول الدورة في قسمها الثاني أن تحقق هدفين، وهما: استعراض أفضل الأساليب التي تحقق النجاح على صعيد إصلاح دعم الطاقة؛ وشرح أوجه النجاح والإخفاق في سياق تجارب بعض البلدان كما وردت في دراسات الحالة

What you’ll learn

  • معرفة أساليب قياس حجم دعم الطاقة بأشكاله.
  • الانعكاسات الاقتصادية والاجتماعية والبيئية لدعم الطاقة
  • التدابير والإجراءات التي تكفل نجاح عملية إصلاح دعم الطاقة.
  • القواعد التي تتيح للبلدان رفع الدعم بشكل تدريجي.
  • كيفية قياس حجم أثر دعم الطاقة في بلدك.

Meet your instructor

Younes Zouhar

Younes Zouhar is a Senior Economist in the IMF Institute and joined the Fund in 2009. He worked previously in the Middle East and Central Asia Department where he actively participated in program negotiations in low-income and emerging-market countries. Prior to joining the Fund, he was the head of the balance of payments Division at the Treasury Department in the Ministry of Finance in Morocco. He is a graduate of the National Institute of Statistics and Applied Economics in Rabat and he has a bachelor’s degree in applied mathematics. He is a national of Morocco.

About this course

Although there are some robots you might never get to meet (or might hope you never meet), such as those sent to space, war or rescue situations, many other robots and bots are being developed to populate people’s homes, the online spaces they frequent, their workplaces, and the social spaces they visit.

This course explores how people communicate with robots and bots in everyday life, both now and into the future.

Module 1 discusses the difficulties of defining what a robot is, as well as briefly introducing bots.

Module 2 focuses on bots, chatbots and socialbots in detail, to consider how people communicate with these programs in online spaces, as well as some ethical questions these interactions raise.

Robots in the home are the subject of Module 3, with a discussion of robots designed to act as personal assistants leading into some examples of assistive and care robots, as well as telepresence robots that allow people to interact with one another at a distance through a robot.

Module 4 considers robots at work, from the potential of telepresence robots to enable remote operations, to robots designed to share people’s workspaces, and potentially even take their jobs. One example of a public space where robots might alter people’s working and social lives greatly is on the roads with the development of self-driving vehicles, robots that need to be able to communicate with their passengers as well as with other road users.

What you’ll learn

  • Some ways to define what robots and bots are
  • How people interpret robots and bots as communicating, social, even emotional others
  • Whether robots and bots need to communicate in humanlike ways to be understood
  • The potential of robots with non-humanlike form, behaviour and communication


Module 1: Robots, bots and communication

  • How robots are presented in popular culture and the media
  • Ways to define a robot
  • Why people build (or don’t build) humanoid or humanlike robots
  • The difference between robots and bots

Module 2: Bots and social bots

  • What it’s like to interact with some bots
  • How and why bots are designed to be humanlike in order to be ‘socialbots’
  • Broader conceptions of bots and their activities in digital spaces
  • Socialbots and bots as they become more specifically embodied

Module 3: Robots in the home

  • The potential of more sophisticated robots designed to act as personal assistants
  • Robots that do more practical work around the home
  • Assistive and care robots, designed to help older adults and people with disabilities of all ages
  • Telepresence robots that allow people to interact with one another at a distance in more flexible and active ways than teleconferencing technologies such as Skype or Facetime

Module 4: Robots at work and on the road

  • Remote operations as an extension of telepresence
  • Robots at work more generally and the question of whether your job might be at risk
  • The introduction of self-driving and semi-autonomous vehicles onto road systems also populated with human drivers, cyclists and pedestrians
  • How ethics can be built into robots and the importance of ethics for designers and manufacturers of robotic technologies

Meet your instructors

Eleanor Sandry

Eleanor is a Senior Lecturer in Internet Studies at Curtin University. Her first degree was in Natural Sciences from Cambridge University. More recently, she completed a Masters in Communication Studies followed by a PhD in Communication and Cultural Studies at the University of Western Australia. Her research uses a range of communication theories and philosophies of technology to drive analyses of human-technology interactions and relations. She is particularly interested in the ways human-robot communication, where robots need not be humanlike in form, behaviour or intelligence, can support collaboration between humans and robots to complete joint tasks in the home, at work or in social spaces.

Gwyneth Peaty

Gwyneth is a sessional academic in Internet Studies at Curtin University. She completed a PhD exploring the grotesque in popular culture, and her wider research interests include monstrosity, post-humanism, horror and the Gothic.

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

About this course

In Disability and Digital Media , we will explore the relationship between digital technologies and disability in the Internet age.We will consider:

  • the evolving impact of social media on representations of disability;
  • the politics of experiencing, embodying, and discussing disability online;
  • the presence of disability in memes, viral content, and online culture; and
  • the role of accessibility in the digital world.

What you’ll learn

You will learn about:

  • Social and medical models of disability;
  • Key concepts and terminology for understanding digital disability;
  • How social media is changing representations of disability;
  • The opportunities and challenges of representing disability online;
  • How memes and viral content are being used by disability activists;
  • How the tools of digital accessibility can benefit all media users.


Module 1: Introducing digital disability

Module 2:Disability and social media

Module 3: Accessibility and the digital world

Module 4: The future of digital disability

Meet your instructors

Gwyneth Peaty

Gwyneth is a sessional academic in Internet Studies at Curtin University. She completed a PhD exploring the grotesque in popular culture, and her wider research interests include monstrosity, post-humanism, horror and the Gothic.

Katie Ellis

Mike Kent