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Data science for business intelligence

Published: 4 March 2021

Understand the new domain in terms of basic concepts, technology used, as well as the context of using. Additionally, you will learn how to run and Manage a big data project. The course gives you 7,5 ECTS.

About the course

Computer science is one of the hottest concepts in business contexts during the 2000s. Nowadays, companies as well as public enterprises have a great interest in the area. Computer science is a combined study of different subjects together, including databases, warehousing, data architectures, business analysis, data mining, big data and more! Computer Science supports and complements Business Intelligence.

The course is designed to give participants a mix of theory and practice. Basic concepts in computer science are presented as a basis, as well as R-programming as one of the most important skills / tools that a computer scientist should have. The course also provides practical experience of using computer science tools and techniques.

After completing the course you will:

  • Know the basics of computer science
  • Be familiar with using tools such as R programming
  • Be able to apply Business Intelligence

The aim of the course is to give you an understanding of the basic concepts, technology used and the contexts in which computer science can be useful. You will also learn how to run and manage a "big data" project.

Arrangement

The course is divided into two blocks of three days each, with case studies between the blocks.

Content:

  • The concepts of data analysis, its motivation, definition, the relationship between data analysis and database systems, statistics, machine learning and information retrieval
  • Understand and analyze the knowledge process with emphasis on the iterative and interactive nature of the KDD process
  • Analysis of different types of data: relational, transactional, object relational, spatiotemporal, text, web
  • Analysis of different types of knowledge such as classification, regression, clusters, frequent patterns, discriminatory, outliers et cetera
  • Evaluate knowledge: interest or quality of knowledge, including accuracy, usefulness and relevance
  • Data analysis applications: market, energy, insurance, sports and health analysis
  • Model and solve data analysis problems with Rapid Miner [member of Gartner's Magic Data Quadrant, 2015]

 

 

 

For more information contact:

Helena Karlberg

Helena Karlberg,

Organisation: Communications Office, Professional Support