Executive education 30 ECTS
Analytics and AI for Strategic Management
This master course aims to create professionals who can bridge the gap between decision-makers and data scientists.
What you will learn
- Understand how analytics and AI inform strategic decisions and business value creation
- Translate business problems into data-driven questions and actionable insights
- Bridge the gap between business and data specialists to lead data-informed initiatives
Business digitalization is changing the content and process of strategic analysis. While classical “big picture” analysis skills remain relevant, more executives now need to understand advanced forms of data analytics to articulate and evaluate strategies.
While data science and analytics professionals typically come from either computer science or statistics, and have years of technical training, strategic decision-makers are typically trained and experienced in business management. As strategy-making grows increasingly dependent on analytics, organizational capacity to bridge the distance between decision-makers and data scientists becomes essential.
Analytics and AI for Strategic Management aims to create professionals who can bridge this gap, and become sophisticated data consumers. Through lectures, workshops and real-world cases, you will learn analytics’ foundational concepts and how to apply those to strategic management.
The course offered valuable perspectives on creating value with data. Seeing the full process from A to Z was highly insightful, and the exchange with participants from diverse backgrounds was especially rewarding.
The course completely changed how I approach data and gave me the tools to truly understand and leverage it for better decision-making. It has already sparked several internal projects and significantly improved my daily work.
Admission requirements
- You are at least 25 years old
- You have completed a bachelor's degree
- You have 4 years of full-time work experience (3 years if you already have a master's degree)
Students without a completed bachelor's degree can, in some cases, be admitted based on prior learning and work experience.
Read more about the admission requirements at master’s level.
Take the course or build a degree
This course can be integrated in an Executive Master of Management.
The course awards 30 credits, and a master's degree consists of 90 credits. Should you choose to take more master's level courses later, you can accumulate a complete master's degree. The combination of courses is up to you, based on your learning goals and career plans.
Modules and course content
This course consists of five modules of three to four days each, over the course of two semesters. All modules takes place on campus Oslo.
Please note that dates may be subject to change.
The syllabus is made available as soon as you have been admitted to the course.
The exam consists of a term paper worth 60% of the final grade, and an individual component accounting for 40%. The project assignment is to be completed in groups of two to three people.
Modules fall 2026/ spring 2027
1. Competitive advantage through data (14. - 17. September)
Module Outcome: Awareness of strategic questions potentially answerable with data.
To contextualise the strategic possibilities enabled by analytics, this module begins with an overview of contemporary strategy perspectives. Organisations’ information systems increasingly allow data science to be applied to business problems. Through real examples and exercises, you will explore how data collection has changed in recent years and how this has the potential to affect business strategy and decision-making. You will also explore cases where data science has challenged traditional understandings, introduced new insights, and changed competitive landscapes.
The end of the module includes a “getting started” workshop in which you will choose a term analysis project, present the strategic challenge to your fellow students, and form groups based on the presentations. Between this module and the next, you will develop an initial formulation of your group project.
2. Analytics Concepts and Processes (26. - 29. October)
Analytics Concepts and Processes for Strategic Management
Module Outcome: Understand the analytics process, with an emphasis on data collection and preparation.
Focusing on data acquisition and management, this module introduces analytics as a scientific process involving problem specification, modelling, data collection, analysis, and summary. This prepares you for the subsequent, modelling-focused modules. Examples from strategic management in Module 1, together with your term project proposals, will be used to illustrate the stages of this process.
At the end of the module, each group should develop an outline for each stage of the analytics project they envisage will answer their overarching strategic question. The final half-day will consist of a workshop in which each group presents its data collection method and expected outcome.
3. Implementing Analytics 1 (23. - 25. November)
Implementing Analytics for Strategic Management
Module Outcome: Using algorithms for analytics projects in strategic management.
These two modules emphasise technology for algorithm-based modelling. You will use the Python programming language to illustrate the modelling process. For many of you, this may be the first time you have done any programming, and perhaps the first time in years that you have looked at equations. That is OK. The course uses collaborative exercises and teamwork to support your success.
The modules also introduce automated machine learning in both Python and the cloud-based DataRobot package.
By the end of Modules 3 and 4, you will know enough about foundational analytics algorithms and implementations to conceptualise strategic objectives as data science problems and clearly communicate these problems to data science professionals. Each group will specify the analyses they expect to conduct and the data required to inform their strategic question. The final day of each module will include a mini-workshop in which each group presents the current status of its project.
4. Implementing Analytics 2 (27. - 29. January 2027)
Implementing Analytics for Strategic Management
Module Outcome: Using algorithms for analytics projects in strategic management.
Module 4 is a continuation of module 3.
These two modules emphasise technology for algorithm-based modelling. You will use the Python programming language to illustrate the modelling process. For many of you, this may be the first time you have done any programming, and perhaps the first time in years that you have looked at equations. That is OK. The course uses collaborative exercises and teamwork to support your success.
The modules also introduce automated machine learning in both Python and the cloud-based DataRobot package.
By the end of Modules 3 and 4, you will know enough about foundational analytics algorithms and implementations to conceptualise strategic objectives as data science problems and clearly communicate these problems to data science professionals. Each group will specify the analyses they expect to conduct and the data required to inform their strategic question. The final day of each module will include a mini-workshop in which each group presents the current status of its project.
5. Workshop on Implementing Analytics (14. - 16. April 2027)
Workshop on Implementing Analytics for Strategic Management
Module Outcome: Use Modules 2-4 to implement term projects.
This module integrates the earlier modules as a workshop in which you apply analytics process and procedures to your term projects. You will present in class and work in teams, together with faculty, to refine your strategic management projects.
Webinars between modules
Webinars will occur between modules, mostly to present specific topics or to help students prepare for assignments.
Faculty
John Chandler Johnson
His research formalizes microsociological theories of endogenous social network evolution.
This course is aimed at managers who want to become sophisticated consumers of analytics. The idea is to learn just enough analytics that you know what to ask for and where the pressure points are. The participants will learn from cases, discussions, live examples and assignments.
Time and effort required
All our courses are designed to be combined with a job and a personal life. Still - you'll need to dedicate some time.
You'll spend a total of 17 workdays on modules, and in addition, time should be set aside for the individual exam.
Additionally, you'll need to make time for reading the course material and writing the term paper.
How much time this takes varies widely from student to student, and you can adjust this to fit your other commitments.
Tuition fee
Price per yearThe tuition fee is invoiced per semester and adjusted annually. It includes access to lectures and the standard examination fee.
Want to know more?
We regularly host informational webinars that you can join. There you will meet a guidance counsellor and have the opportunity to ask questions.
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