Business Analytics, 15 Credits

Contents

The course is designed to equip students with fundamental data-driven techniques to enhance business decision-making processes. Students will engage with a variety of real-world cases that illustrate the critical role of data in forming sound business strategies. The course covers a wide range of topics including data collection, visualizations for clear communication, explorative analysis to uncover relationships within datasets, inference, and the fundamentals of predictive modelling applicable across various business contexts.

The course consists of two modules:

• Module 1. Foundations of Business Analytics (10 Credits)
• Module 2. Applications of Business Analytics (5 Credits)

Module 1. Foundations of Business Analytics (10 Credits)

Data Collection and Exploratory Analytics
We introduce data collection, and ethics, delve into how data visualization and other exploratory analyses vary based on the data type and business problems at hand. We discuss effective methods of communicating with data to facilitate informed business decisions.

Predictive Analytics, Regression, and Inference
We use regression, classification, and other analytical techniques to solve business problems.  We focus on model building, prediction, and inference to make data-driven decisions. We learn how to perform these techniques in statistical software, such as Excel and R.  

Module 2. Applications of Business Analytics (5 Credits)
We will engage the students to work on real-world business cases in which they describe and analyze data to motivate certain data-driven decisions. 

Expected learning outcomes

Knowledge and understanding
Students must be able to

1. describe the fundamental concepts, terminology, and analytical methods and their importance in business decision-making.  
2. describe data collection methods and identify sources for obtaining relevant data.

Skill and abilities
Students must be able to

3. recognize trends and summarize data sets.
4. create data visualizations to effectively communicate findings.
5. build and evaluate models for prediction.
6. draw data-driven conclusions (prediction, inference) in real-world business scenarios.
7. use relevant software for data analysis, such as Excel and R.
8. use business analytics on practical problems.
9. present and interpret the results of a data analysis and, for the given problem, justify and argue for specific assumptions, method selection and conclusions.  

Judgment and approach
Students must be able to

10. critically review reports based on data analyses.
11. identify ethical considerations and potential biases in data analyses.

Required Knowledge

Form of instruction

Learning is supported by lectures, lessons, workshops, seminars, and supervision.

Examination modes

Module 1 is examined by an individual written hall exam. The exam is graded Pass with distinction (VG), Pass (G) or Failed (U).

Module 2 is examined through quizzes, seminars, a written report, an oral presentation, and an opposition of another group's work. All examinations in this module are graded Pass (G) or Failed (U). The grade on the course is Pass with distinction (VG), Pass (G) or Failed (U). For the grade Pass (G) all examinations need at least the grade Pass (G). For the grade Pass with distinction (VG) the student also needs Pass with distinction (VG) on the written exam.

A student who has passed an examination is not allowed to take another examination in order to get a higher grade. For students who do not pass, an additional examination opportunity will be held according to a pre-determined schedule.

A student that has failed an examination on two occasions has a right to have another examiner or grading teacher appointed unless there are special reasons against it. A written request addressed to the Director of Studies should be made no later than two weeks before the next examination opportunity.

Examinations based on the same course syllabus as the ordinary examinations are guaranteed to be offered up to two years after the date of the student's first registration for the course.

Adaptations
Examiners may decide to deviate from the modes of assessment in the course syllabus. Individual adaptation of modes of assessment must give due consideration to the student's needs. The adaptation of modes of assessment must remain within the framework of the intended learning outcomes in the course syllabus. Students who require an adapted examination - and have received a decision on the right to support from the coordinator at the Student Services Office for students with disabilities - must submit a request to the department holding the course no later than 10 days before the examination. The examiner decides on the adaptation of the examination, after which the student will be notified.

Academic integrity and cheating 
As a student, you are expected to act with academic integrity. This means writing and presenting within the limits of the academic rules and expectations communicated in the university's regulations and what is otherwise specified by the responsible department. Disciplinary action may be taken against students who use unauthorized help aids or in some other way try to mislead on a test or on another type of task for examination. Rules and regulations concerning the production of academic texts and correct referencing will be applicable to written assignments. Submitted material may be subject to plagiarism control. In addition, Umeå University rules and regulations for education and research apply.

Academic credit transfer
Academic credit transfers are according to the University credit transfer regulations.