GRA 3117 Strategy and Entrepreneurship Analytics
GRA 3117 Strategy and Entrepreneurship Analytics
This course will teach the students applied and quantitative approaches to model and analyze businesses and business opportunities, as well as typical problems of a strategic and entrepreneurial nature that they face. Students will learn how to quantify and analyze businesses, business environments, and different strategic and entrepreneurial initiatives, and to assess financial implications. The course offers the students a set of quantitative tools, methods and skills that complement their knowledge of conceptual theories in entrepreneurship, innovation, and strategic management.
After completing this course, students will have advanced knowledge of relevant theories, tools, and methods required to model businesses to solve strategic and entrepreneurial problems. The students will have advanced understanding of how they can implement a variety of state-of-the-art analytic tools for supporting strategic business decisions.
After completing this course, students will be able to carry out applied modeling of real-world business challenges in a spreadsheet framework. The students will be able to collect data for such modeling, structure the data, make sound judgements in how to analyze the data and use the data to make strategic decisions. They will be able to set it up in a structured way, manipulate it, and conduct sensitivity analyses. Students will have enough familiarity with advanced analytics, such as machine learning techniques and simulation, to understand how they may be applied to business development challenges.
After completing this course, students will have the knowledge and skills to quantitatively model and analyze businesses to support their decisions. The students will be able to quantify their theoretical knowledge of strategy and entrepreneurship, and to make critical assessments for the business in question.
Being able to model and analyze strategic and entrepreneurial choices is central to the course. The course will consist of a combination of lectures and group assignments and will contain quantitative components. Students will learn about various methodologies used in practice, e.g. by management consultants, entrepreneurs, venture capitalists, experts carrying out due diligence, and others, and to apply these methodologies in a range of contexts chosen to represent real world analytic challenges facing businesses across a spectrum of activities.
The course will be a combination of lectures, experiential learning activities, application, reflection and class discussions. We will apply concepts and skills associated with the topics and also critically evaluate them. We will assess applications, decisions made, and problematize these. We will also think about other ways to apply/alternative decisions. Students will need to use Excel for exercises, and will be introduced to machine learning and other advanced tools.
Please note that this course has mandatory coursework requirements that must be approved to be able to sit for the exam and pass the course.
It is the student’s own responsibility to obtain any information provided in class.
The examination for this course has been changed starting academic year 23/24. The course now has one ordinary exam element in addition to mandatory coursework requirements that must be approved before you can take the exam. It is not possible to resit the old version of the examination.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
Excel, and possibly other applications, will be used for modeling.
| Mandatory coursework | Courseworks given | Courseworks required | Comment coursework |
|---|---|---|---|
| Mandatory | 5 | 3 | Assignments/exercises for practicing course tools and methods presented in class, and to prepare for exam. |
| Mandatory | 1 | 1 | 75% attendance is required. Students are expected not only to be present in the classroom, but also be prepared to participate in group exercises and discuss the methods and cases specified for the lecture. |
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission other than PDF Exam/hand-in semester: First Semester Weight: 100 Grouping: Group (2 - 4) Duration: 1 Semester(s) Comment: Course project report (term paper), and video recording of student group presentation of the project. The presentation will be recorded and uploaded by the student group. Exam code: GRA 31171 Grading scale: ECTS Resit: Examination when next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | In class or online synchronous lecture plus asynchronous activities. |
Student's own work with learning resources | 60 Hour(s) | Student readings and preparation for lectures. |
Group work / Assignments | 30 Hour(s) | Student work in groups on projects and assignments. |
Examination | 34 Hour(s) | Examination related work. |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.