Data Science for Business: Theory and Practice

Instructor(s): Prof. V. Peristeras
Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Exam & Coursework

 

Aims

The course examines the impact of data science in modern private and public organisations and presents challenges, opportunities and trends in the field. The students will gain the necessary conceptual understanding of the uprising “data economy” with its underlying technological and business characteristics. Business cases will be presented and discussed, while specific business problems will be matched with new data technologies. Data/information management and interoperability topics will be also presented and discussed.

 

Learning Outcomes

On completing the course, students will be able to:

  • Understand the scope of data science and the role/function of data scientists.
  • Identify different types of data that are relevant in business environments.
  • Know which data science solutions can address specific types of business problems.
  • Be able to design a data governance policy.
  • Understand challenges and opportunity in the data-driven economy and public policy.

 

Content

  • Defining Data Science.
  • Data-analytic thinking.
  • Big/smart/open/linked/meta/reference/master data.
  • Data interoperability.
  • The data value chain.
  • Business problems and data science solutions.
  • Data governance.
  • Data for policy.
  • Data-driven economy.

 

Reading

  • Data Science for Business, Foster Provost, Tom Fawcett, O'Reilly Media, 2013.