Statistical and Machine Learning

Knowledge

Students:

• Has in-depth knowledge of areas within data analysis and statistical methods for conducting an analysis of data sets of different types and sizes.
• Has in-depth knowledge of international research in topics related to the course, such as regression analysis, decision trees and machine learning.
• Have knowledge and practical understanding of data integrity, including reliability of data, complete and accurate data transfer, data washing and data clearing.
• Can apply knowledge in new areas within data analysis, map the data flow in systems and processes that are relevant to different industries.
• Can analyze academic issues with tools and techniques from the subject's uniqueness.

Skills

Students:

• Can analyze existing models, theories and methods in statistics for large amounts of data using appropriate tools
• Can use relevant methods and theories for research within the subject's main topics in an independent way.
• Can analyze and critically relate to different sources of information used in different contexts within automation of work tasks.
• Can carry out, produce and evaluate an independent analytical research work under supervision in line with research ethics norms.
• Can use analytical and digital tools, such as R, in practical work, both for obtaining data sets, analysis of data sets, as well as communication of results and analyzes both written, oral and graphical

General competence

Students:

• Has a thorough understanding of the change processes that are taking place, and is expected in the future, as well as analyzing relevant issues within digitization of various industries.
• Can convey extensive independent work and masters forms of expression in digitization, automation and the relevance of future work tasks, such as presentation of graphic illustration of connections between variables and changes over time, individuals or both
• Can apply his expertise in new areas and issues within business analysis and consulting.
• Can contribute to new thinking and innovative solutions to problems related to digitization and automation.
• Can discuss and communicate professional issues with different stakeholders.
• Can assess and consider the most appropriate method of digital business analysis for a given industry or data set.
• Can apply and easily acquire skills in new digital tools.