Data Analytics 7,5 Credits

Course Contents

The growth of data today is exponential in many different industries. Companies and organizations need the ability to organize and analyze their data to find valuable connections. Traditionally, data analytics has been conducted using various statistical methods, but today many of the most powerful technologies come from the subfield of artificial intelligence called machine learning. In practice, data analytics is about utilizing advanced algorithms to generate decision-making data from large and unstructured data sets, which in the long run constitute competitive advantages.

The course includes the following elements:
• The main task categories in data analytics, i.e. classification, regression, clustering, association rules and anomaly analysis.
• Basic statistical techniques for data analytics.
• Basic techniques from the field of machine learning used for data analytics.
• Organizing a data analytics project, and its various phases, ie. project understanding, data understanding, data preprocessing, modelling, evaluation and implementation.
• Software tool for data analytics.
• Data analytics and its use in specific domains.
• Ethical considerations regarding data analytics.
• Main research orientations for data analytics.

Prerequisites

Passed courses 150 credits in first cycle and 30 credits in Mathematics including 7,5 credits in Statistics (or the equivalent).

Level of Education: Master
Course code/Ladok code: TIGR21
The course is conducted at: School of EngineeringLast modified 2023-02-24 07:18:33