Data science is revolutionizing businesses across industries and providing strategic and operational insights that are disrupting old business models. Data science and AI are changing the way organizations design products and services, buy and sell, store and use data and run businesses, including government and not-for-profit organizations of all sizes.

The core of data science is centered on highly technical mining of insights from internal and external data. In the data science-enabled enterprise, knowledgeable teams use data science tools to create charts and visualizations that prove patterns and substiles in business data, such as new customer segmentations or cost patterns, which inform decision-making throughout the organization. The outcome can be sufficiently profound to change the direction of entire lines of business.

During this eLearning course, participants will become familiar with key data science and AI concepts, definitions and technologies. Participants will gain a foundational understanding of how data science, AI and big data overlap. Participants will also explore key characteristics of analytics departments and specific use cases.


By the end of this course, participants will be able to:

  • Explain how the definition of “data” has evolved and distinguish between distinct types of data
  • Define data science and explain how it has revolutionized traditional approaches to business strategy
  • Define big data and outline the 4 Vs of big data
  • Explain what artificial intelligence (AI) is and how it ties in with data science
  • Outline key developments in the field of data science and explain why its adoption has become so widespread
  • Provide examples of how data science is being leveraged across a broad variety of industries
  • Describe specific mechanisms and tools that you can use to weave data science into the operating model of your organization and positively impact your business
  • Define data analytics and outline tools and techniques typically used to analyze data sets and help organizations make more informed business decisions
  • Provide examples of how a business challenge can be re-framed as an analytics challenge
  • Differentiate between financial modelling, financial forecasting and data modelling
  • Describe the benefits of creating a data science roadmap for your organization


  • Defining data science
  • Structured vs. unstructured data
  • Big data
  • Artificial intelligence
  • Neural networks
  • Machine learning
  • Internet of Things
  • Biometrics
  • Data analytics techniques
  • Data visualization
  • Financial forecasting models vs. predictive models
  • Starting and framing a data science project
  • Key roles on the data analytics team


  • Individuals new to the field of data science, as well as those interested in broadening their knowledge of the data science and AI landscapes
  • Individuals who would like to gain a foundational understanding of key concepts, terminology and types of problems that data science typically helps solve