Making Data Science Pay

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Data Science is a big deal. But if you were to ask a hundred people what Data Science is – and more importantly, to state its value – you’d probably get a hundred different answers. Data Science is too important to be so elusive. This course remedies that by defining the value and explaining the technology behind it. The purpose is to cut through the market buzz surrounding data science and boil it down to its practical concepts and applications.

Participants will learn the real-world usage and ROI of data science including why projects typically succeed or fail. The course simplifies the technology and the essential tasks of the data scientist. It peels away the complexities surrounding data science, boiling it down to its essence, presented in a style that all can understand.

This course is a non-biased, coherent, and often entertaining integration of facts and figures, explanations and real-world usage of data science — translating its technology into value, and its value into strategic competitive advantage.

Data Science is purported to have substantial organizational value. But the reality is that most people don’t know how to realize that value. This course illuminates and clarifies data science’s strategic potential.

Even companies that are early adopters of data science and have successfully shown isolated value with a project or two are challenged by issues related to a) integrating it into organizational processes and culture, and b) scaling initial successes into enterprise-wide strategic advantage.

This course gives a high-level, yet comprehensive overview of data science and associated analytics, and methodically addresses each of these issues from a strategic, value-focused perspective.

There are no prerequisites for this course.

1 Day/Lecture & Lab

This course is designed for:

  • Executives, directors and managers struggling to understand the reality, measure the value, overcome the challenges, and realize the rewards of data science.
  • Business Intelligence leaders seeking the rationalization for data science initiatives
  • Analytic professionals trying to understand the differences in data analysis and data science
  • Data analysts, statisticians, engineers, and computer scientists who aspire to become data scientists
  • The curious who are tired of being bombarded by the Data Science market buzz and frustrated at not understanding it sufficiently to make reasoned decisions about its use

  • What is Data Science?
  • What is the Organizational Value of Data Science?
  • How is Data Science Different from Data Analytics
  • What are the Risks of Data Science?
  • What are Data Science Technologies? A Layman’s View
  • What are the Skills Needed for Data Science?
  • How Do You Organize Data Science in Your Organization?
  • The Future of Data Science and Advanced Analytics
  • Picking through the Rhetoric to Define Your Organization’s Data Science Reality




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