Statistical and Machine Learning
Knowledge
The student:
¿ Has advanced knowledge of what digitalization and automation means for different industries, witha special focus on finance.
¿ Has in-depth knowledge of areas in data analysis and statistical methods for carrying out an analysis of data types of different types and sizes, using methods such as for example random Forests.
¿ 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 data reliability, complete and accurate data transfer, data cleaning and data erasure.
¿ Can apply knowledge to new areas in data analysis, map data flow into systems and processes relevant to different industries.
¿ Can analyze professional issues with tools and techniques from the subject's specificity.
Skills
The student:
¿ Analyze existing models, theories and methods of statistics for large amounts of data using appropriate tools
¿ Can use relevant tools, methods, and theories for research within the subject's main themes in an independent manner.
¿ Can analyze and relate critically to various sources of information used in different contexts within automation of work tasks.
¿ Can conduct, produce and evaluate independent analytical research work under guidance in accordance with research ethical norms.
¿ Can use analytical and digital tools, such as for example R, Python or Tableu, in practical work, both for data collection, data analysis, and communication of results as well as present results in both written, verbal and graphical terms
- Can write a complete scientific report in one file using a software of own choice
General competence
The student:
¿ Have a thorough understanding of the change processes that are happening and expected in the future, as well as analyze relevant issues within the digitization of different industries.
¿ Communicate comprehensive independent work and master expressions in digitalization, automation and relevance for future tasks, such as presentation of graphical illustrations of variables and changes over time, individuals or both
¿ Can apply their competence to new areas and issues in business analysis and counseling.
¿ Can contribute to innovative thinking and innovative solutions to issues related to digitization and automation.
¿ Can discuss and communicate professional issues with different stakeholders.
¿ Can evaluate and consider the most appropriate method of digital business analysis for a given industry or data source.
¿ Can use and easily acquire skills about new digital Tools.
Composite assessment, grading rule Letter grades
- Written school exam, 4 hours, counts 60/100 of the grade, grading rule Letter grades.
- Assignment - group work, counts 20/100 of the grade, grading rule Letter grades.
- Oral - Presentation, counts 20/100 of the grade, grading rule Letter grades.
We reserve the right to change the assessment method. The correct assessment method is displayed in StudentWeb when registering for the relevant course.