• Understand theory behind information function of organization.
• Analyze business processes and learn how to extract relevant information.
• Learn how to create descriptive charts and graphs from the structured datasets.
• Understand the basics of the statistical and machine learning tools.
• Understand the optimization techniques and the simulation methods.
• Learn to use the R and RStudio.

Learning outcomes

On successful completion of this course, the students will be able to:

• LO1: Differentiate between digitization, digitalization and digital transformation and connect it with the information function in organizations. [Relevant Theory, Digital transformation, Business management qualifications, Interdisciplinary skills]
• LO2: Analyze and evaluate different tools and methods for a given challenge and apply the selected tool and method. [Relevant Theory, Digital transformation, Business management qualifications, Interdisciplinary skills, Research Skills and Methodological competencies, Social Skills, Interaction with companies, empirical/practical projects]
• LO3: Use modelling techniques (BPMN 2.0, UML or EPK) to work on complex and complicated digital transformation projects [Relevant Theory, Digital transformation, Business management qualifications, Interdisciplinary skills, Research Skills and Methodological competencies, Social Skills, Interaction with companies, empirical/practical projects]
• LO4: Distinguish good and bad practices used by descriptive analytics (bias, misleading, etc.) and apply knowledge in practical exercises. [Ethics, Responsibility and Sustainability (ERS), Analytical skills, Digital transformation]
• LO5: Choose right prediction models and design the model for chosen practical example. They will be able to recognize the ethics problems of machine learning. [Ethics, Responsibility and Sustainability (ERS), Analytical skills, Problem Solving and Reflections Skills, Research Skills and Methodological competencies]
• LO6: Acquire the tools necessary to formulate a problem as an optimization model and assess the result computed by the model. They will be able to design a simulation for a given scenario and analyze the outcomes of the simulation. [Problem Solving and Reflections Skills, Analytical skills, Research Skills and Methodological competencies]
• LO7: Apply R and RStudio to solve problems arising in Descriptive, Predictive and Prescriptive analytics. [Digital skills, empirical/practical projects]

• Students complete practical Homeworks on case companies in groups. These Homeworks are designed to practice further the theory from lectures and the practices learned during tutorials. A selection of homework is presented in class and discussed to deepen the understanding of the content and challenges. The assessment results in grades for the whole group [LO1, LO2, LO3, LO4, LO5, LO6, LO7]
• Exams are designed to test both practical knowledge gained on seminars and the theoretical knowledge presented during lectures. Students have to apply their new skills, evaluate topics and draw connections by giving own examples. To minimize plagiarisms, the exam is time limited, and each student has different set of question chosen randomly from the question pool. The exams result in individual grades. [LO1, LO2, LO3, LO4, LO5, LO6, LO7]
• The final grade is based both on the practical knowledge of students, shown by the homework and part of the exam, the theoretical knowledge examined during the tests and overview of current business practices and tool currently used to support business decisions examined during the test. [LO1, LO2, LO3, LO4, LO5, LO6, LO7]

Range of assessment methods and synchronization of learning outcomes and assessments: Students complete four group homework for different learning objectives [LO1, LO2, LO3, LO4, LO5, LO7]. Student can get for each project up to 10 points.

In the end of semester, students will have 2 part exam. 1. part examine learning objectives [LO1,LO2,LO3] and 2. part examine learning objectives [LO4,LO5,LO6,LO7]. For each part students can get 30 points.

To pass the course student has to get:

1. At least 50 points in total
2. At least 40% from exams = at least 24 out of 60 points from exams