We provide course about analytics and AI for strategic management in english. This master course aims to create professionals who can bridge the gap between decision-makers and data scientists.
What you will learn:
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.
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.
Modules:
1. module - Competitive advantage through data:
• Module Outcome: Awareness of strategic questions potentially answerable with data. This session begins with an overview of contemporary strategy perspectives. We also explore how data collection has changed in the past few years and how this has the potential to affect business strategy and decision-making.
2. module - Analytics Concepts and Processes:
• Analytics Concepts and Processes for Strategic Management. Module Outcome: Understand the analytics process, with an emphasis on data collection and preparation. This module introduces analytics as a scientific process involving problem specification, modeling, data collection, analysis and summary.
3. module - Implementing Analytics 1:
• Implementing Analytics for Strategic Management. Module Outcome: Using algorithms for analytics projects in strategic management. This is the first of two more technical modules. It will formalize module 2's concepts and implement and visualize them by using the Python programming language.
4. module - Implementing Analytics 2:
• Implementing Analytics for Strategic Management. Module Outcome: Using algorithms for analytics projects in strategic management. Module 4 is a continuation of module 3. By the end of Modules 3 and 4, students will know enough to conceptualize strategic objectives as data science problems and clearly communicate those problems to data science professionals.
5. module - Implementing Analytics 2:
• 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.
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, plus an additional day for the written 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 will be available soon
• The tuition fee is invoiced per semester and adjusted annually
• It includes access to lectures and the standard examination fee
Faculty: John Chandler Johnson and Espen Andersen
John Chandler Johnson holds a PhD in sociology from Stanford University and has extensive international market research and operations consulting experience.
Espen Andersen is the Director of BI Center for Digitization. He has consulted on technology and strategy issues for a wide range of organisations in the United States and Europe, and is a frequent speaker on technology and strategic management topics.
Target audience:
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.
Admission requirements:
• You are at least 25 years old in the year of admission
• Have a completed Bachelor's degree, equivalent of 180 officially recognised credits from a university or university college
• You have two years relevant work experience following completion of a bachelor’s degree
Students without a completed bachelor's degree can, in some cases, be admitted based on prior learning and work experience.
Language:
• This course is given in english
Exam:
The exam consists of a term paper worth 60% of the final grade, and an individual component accounting for 40%. The project assignment may be completed individually or in groups of up to three people.