MBA 2435 Strategic Insight from Machine Learning

MBA 2435 Strategic Insight from Machine Learning

Course code: 
MBA 2435
Strategy and Entrepreneurship
Course coordinator: 
John Chandler Johnson
Course name in Norwegian: 
Strategic Insight from Machine Learning
Product category: 
MBA China
2019 Autumn
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

Digitalization creates high speed, high volume digital activity traces – Big Data. Using recent advances in machine learning, organizations can use these data to automatically build models that predict specific events in real-time. Managers can use machine learning-based analytics to improve a wide variety of tasks ranging from targeted marketing to preventative maintenance to fraud detection. The course emphasizes processes for approaching and defining problems assessable through machine learning. The class will function like a lab in which students get hands-on experience with cutting-edge analytics processes, tools, and techniques. This firsthand experience provides students with insight into how big data analytics can improve business processes and decision-making.

Learning outcomes - Knowledge

Students will leave the program familiar with machine learning’s concepts, vocabulary, and applications. Students will understand what cutting-edge machine learning can and cannot do, and what existing challenges forthcoming technologies may help solve. Through exposure to machine learning tools and techniques, students will learn what data scientists actually do.


Learning outcomes - Skills

Armed with the knowledge of what machine learning does and how data scientists use it, students be better able to collaborate with dedicated data scientists. Aware of machine learning’s possibilities and processes, students will be better prepared to work with and lead cross-functional, data-driven teams.

General Competence

Data science, machine learning, artificial intelligence, cognitive computing, and big data are all evolving rapidly. These rapid technology changes have created a chasm between individuals with functional expertise and individuals with technological/algorithmic expertise. Through exposure to the tasks, techniques, and problems characteristic of machine learning, students will become familiar with what these cutting edge technologies can do. This familiarity will allow students to formulate reasonable data science expectations and to critically assess data science analyses.

Course content

This is an intensive, 4-day course. Before the course begins, students must read a background text to ensure a common vocabulary and familiarity with basic techniques. Most of the course will occur like a workshop, in which we collectively explore tools and reflect on their strengths, weaknesses, and potential strategic application. Because discovery/surprise are so important in this process, most slides and materials will NOT be made available in advance of class.


Day 1: Defining ML and Isolating ML-suitable problems
We will spend day 1 specifying what machine learning (ML) is, what it can do, and how it interfaces with the larger organization. We will answer questions including: What is machine learning (ML)? How is ML distinct from classic scientific inquiry? From other elements of the cognitive computing universe? What are the fundamental techniques/algorithms in ML? With this definition of ML and its techniques, to what types of problems does the toolset lend itself? How does ML interface with the organization’s larger digital ecosystem?

Day 2: Data, Modeling, Model Assessment, and Automated Modeling
This day will present the “nitty gritty” of how machine learning is done – we will crack open the hood, open up the engine, and see how the tool works. We will begin the day reviewing data storage, collection, and transformation. We will then execute some modeling exercises stepwise before a discussion about what constitutes a “good” model. This discussion will cover important technical topics such as overfit, cross-validation, and target leak. Finally, we will introduce automated ML.

Day 3: More on Auto-ML
In day 3, we will introduce tools to conduct machine learning, which we will use to conduct several illustrative machine learning projects. Distinguishing model evaluation from model assessment, we will introduce techniques to assign value to models, and discuss that as a bridge between granular model output and higher-level strategic insight.

Day 4: Sources of Resistance, Other Tools, Other Problems, and Next Steps
On day 4, we will discuss auto-ML tools and techniques other than auto-ML that lend themselves to different types of problems (i.e., other spaces in the cognitive computing universe). We will also discuss sources of cultural and institutional resistance to ML adoption, and strategies to handle such resistance. We will have a final student exercise, in which students apply their learning and present strategic insights from a machine learning project. Finally, we will discuss next steps for ourselves and for industry generally. 

Teaching and learning activities

The course is conducted as a teaching module, where students have classes all day for four subsequent days, a total of 32 hours.

Students will need a local Python installation. The instructor recommends and will support Continuum’s cross-platform Anaconda distribution, but any active Python 3+ environment will work.

The course is a part of a full MBA programme and examination in all courses must be passed in order to obtain a certificate.

Software tools
Software defined under the section "Teaching and learning activities".

Granted admission to the BI-Fudan MBA programme. Please consult our student regulations.

Exam category: 
Form of assessment: 
Written submission
Support materials: 
  • No support materials
3 Hour(s)
3-hour, in-class exam, counts 100% of the final grade.
Exam code: 
MBA 24351
Grading scale: 
Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 4 ECTS credit corresponds to a workload of at least 110 hours.