5-Day Bootcamp at EPFL: September 9 - 13, 2019

Sorry... the course is full and registration is closed.

We will hold the course again this winter. If you would like to be notified when registration re-opens, please email support@dsfm.ch and we will add you to the waiting list.

Courses are held at EPFL in Lausanne, Switzerland.

Why take the DSFM Bootcamp?

The most innovative companies around the world are developing new capabilities in Artificial Intelligence. McKinsey projects that AI will deliver an additional $13 trillion of GDP to the global economy by 2030. Data Science is now a critical skill for every manager in the digital age.

If you need a fast and comprehensive overview to Data Science, join us at EPFL for Data Science for Managers (DSFM). Taught by EPFL Professors onsite at EPFL (not online), the DSFM bootcamp offers a balance between theory, demonstration, and practical experience. Learning in a real classroom environment, participants will receive a combination of lectures, demonstrations, case studies, visualizations, programming exercises, and applied projects. You will quickly learn the tools and vocabulary that you need to collaborate with technical teams and separate AI-reality from AI-hype.

In this course, you will:

      • Learn the foundational concepts, methods, and vocabulary of data science.
      • Learn to identify important business outcomes that can be predicted with AI.
      • Learn to develop real Data Science models to solve real-world problems.
      • Learn how AI is changing many business models.
      • Learn new ways to develop a data-driven strategy.
      • Meet others who are working on similar problems in their own workplace.

Why is Data Science critical for your career ?

Massive amounts of data are generated in all areas of business, science, and government. With a computational mindset, you can greatly improve the efficiency of manufacturing, logistics, supply-chain, engineering, finance, telecommunications, transportation, healthcare, urban planning, and many other domains. To get real results, however, you need to understand how AI methods work, why they work, and when they do not work.

This course helps managers, engineers, and business professionals make better decisions about Data Science projects and to advance their career.

I highly recommend Data Science for Managers! Prof. Younge gives an extensive overview of the field and the course helped me to improve how I work with our Machine Learning engineers. The highly-interactive nature of the demos and teaching assistants also help one to better understand the strengths and limitations of each algorithm. If you need to come up to speed quickly - so you can collaborate with a data science team or spot new opportunities for your business - this course is for you!

Alen Arslanagic, CEO Visium

Course Instructors

Prof. Kenneth Younge

Head of the Technology and Innovation Strategy Lab at EPFL. Affiliated Faculty for the CODEX Center for Computational Law at Stanford University

Professor Younge teaches the Master's course on Data Science for Business, the Doctoral course on Computational Methods for Management Research, the Executive Education course for Data Science for Logistics for IML (the International Institute for the Management of Logistics and Supply Chain), and Executive Education on Technology and Innovation Strategy. His Masters students, PhD students, and post-doc researchers collaborate with a wide range of Swiss and US companies.

Prof. Chris Tucci

Head of the Corporate Strategy and Innovation Lab at EPFL. Former Dean of the College of Management at EPFL

Professor Tucci teaches and conducts research in the area of technology and innovation management (TIM). He focuses on issues of corporate strategy and innovation -- or how large, multi-business firms manage transitions to new technologies, business models, and organizational forms. He teaches in the executive education program at EPFL and continuing education programs at UNIL-EPFL Formation Continue. He is a leading expert on issues of digital transformation and developing a big data strategy.

Topics Covered

DSFM puruses a balance between theory and practice, with visualizations, demonstrations, case studies, programming exercises, and applied problem solving to show the theory in practice. Most managers have forgotten advanced mathematics, and so we emphasize visualizations of mathematical concepts in DSFM, instead of working on complicated proofs.

In addition, most participants are not professional programmers. The practical sessions in DSFM start with basic programming concepts and then build up to review completed solutions for each prediction problem or topic covered. Novice programmers will primarily learn how to read programming code by "backward engineering" solutions, while advanced students will learn the programming APIs to build solutions from the bottom-up. In both cases, however, the primary goal is to give you the skills to read computer programming code at a high level so that you can better understand what is going on, and so you can communicate better with more technical data science professionals.

Concepts covered in the course:

      • Data sampling, measurement, wrangling, normalizing, and standardizing
      • Data description, visualization, and graphing
      • Exploratory data analysis
      • Bias, variance, and the bias-variance tradeoff
      • Model validation and model cross-validation
      • Hyperparameter tuning and the leakage of information
      • Model evaluation and comparison
      • Model weighting of costs and benefits
      • Ensemble learning and meta-learning
      • Predictive labeling and data augmentation

Methods and models covered in the course:

      • Linear models
      • Log-Linear models
      • Non-parametric models, splines and locally-linear models
      • Nearest neighbor and other similarity models
      • Agglomerative clustering and K-means clustering
      • Decision trees, bagging, and boosting
      • Dimension reduction, PCA, t-SNE, and manifold projections
      • Support Vector Machines
      • Natural Language Processing (NLP) and Text as Data
      • Word Embeddings and Topic Modeling
      • Feed-Forward Neural Networks
      • Convolutional Neural Networks
      • Recurrent Neural Networks, LSTMs, Bi-Lateral LSTMs

Business topics covered in the course:

      • Data-driven business models
      • Big Data and the use of Cloud Computing (VMs, containers, map/reduce, Spark, SQL, )
      • Strategic planning for a Digital Transformation
      • The management of Strategic Human Capital in the Data Science domain
      • Brainstorming on your own concrete projects over lunch!

Course Outline

The daily schedule for DSFM is divided into conceptual lectures in the morning, a brainstorming session over lunch, an in-depth demonstration after lunch, short exercises in the early afternoon, and then a mini-project in the late afternoon. We will take time off for breaks, drinks, snacks, discuss amongst participants, and perhaps a bit of sunshine.

Teaching assistants are available throughout the day to provide one-on-one assistance with practical problems. You will leave the course being able to build, evaluate, and work with real data, and real data science models!

1. Monday

 8:30 -  9:30    Welcome       Course Introduction
 9:30 - 10:30    Discussion    Student Introductions
10:30 - 11:30    Setup         Jupyter & Python Fundamentals 
11:30 - 13:00    Lunch         Q & A     
13:00 - 15:30    LECTURE 1     Core Concepts
15:30 - 16:00    Break         Q & A
16:00 - 17:30    Demo 1        Data Exploration & Visualization
17:30 - 18:30    Exercises 1   Data Input and Manipulation
18:30 - 19:00    Project 1     The Birthday Problem

2. Tuesday

 8:30 - 11:30    LECTURE 2     Linear Models
11:30 - 13:00    Lunch         Q & A
13:00 - 14:00    Demo 2        Credit Card Default (Simple)
14:00 - 15:30    Exercises 2   Simple prediction problems   
15:30 - 16:00    Break         Q & A
16:00 - 19:00    Project 2     Credit Card Default (Logit)

3. Wednesday

 8:30 - 11:30    LECTURE 3     Similarity, Clustering, Trees
11:30 - 13:00    Lunch         Q & A
13:00 - 14:00    Demo 3        Customer Segmentation
14:00 - 15:30    Exercises 3   Complex prediction problems
15:30 - 16:00    Break         Q & A
16:00 - 19:00    Project 3     Sales Price for Houses

4. Thursday

 8:30 - 11:30    LECTURE 4     SVMs & Text Analysis
11:30 - 13:00    Lunch         Q & A
13:00 - 14:00    Demo 4        Sentiment in IMDB Reviews 
14:00 - 15:30    Exercises 4   Dimension reduction & text
15:30 - 16:00    Break         Q & A
16:00 - 19:00    Project 4     Pre-screen Funding Applications

5. Friday

 8:30 - 11:30    LECTURE 5     Neural Nets, CNNs, RNNs
11:30 - 13:00    Lunch         Q & A
13:00 - 14:15    Demo 5        Image Recognition
14:15 - 14:30    Break         Q & A
14:30 - 16:30    AI Strategy   Interactive discussion
16:30 - 17:00    Apero         Awarding of Certificates

Register Now!

Sorry... the course is full and registration is closed.

We will hold the course again this winter. If you would like to be notified when registration re-opens, please email support@dsfm.ch and we will add you to the waiting list.

Courses are held at EPFL in Lausanne, Switzerland.

This is a highly-rated course and spots fill quickly!

If you already have a UNIL or EPFL login, you might receive an error. We will then follow-up with you shortly. Need help ?

Registration Fee

4'200 CHF per participant

500 CHF discount for multiple participants from the same company.

Fee includes all textbooks, course materials, lunches, snacks, and refreshments. Please contact us to coordinate invoicing for multiple registrations.


A certificate of attendance will be awarded by the UNIL-EPFL Formation Continue at the end of the course.

Participants are also invited to join the DSFM Alumni Network and attend complimentary networking events.


No prior training in Data Science is required to take the course. However, all examples, demonstrations and exercises will be in Python, so it is important to familiarize yourself with Python before the start of class.

Please complete the 4-hour Python tutorial by DataCamp (or if you have time, the 7-hour Python tutorial by Kaggle) before the start of class. Doing so will be sufficient to prepare you to follow along with the demos, examples, and project solutions. Nevertheless, EPFL graduate students will be available as Teaching Assistants throughout the course to help you with any coding questions that you might have. By the end of the course, many participants are able to go from a very basic knowledge of Python, to being able to work on real problems on their own.

Participants should also:

      • Be familiar with basic linear algebra (although there will be very little math)
      • Be familiar with basic statistics (although we will review the basic linear statistical models at the start of the course)
      • Be conversant in English (the course will be given in English)
      • Bring a a laptop computer (Mac, Windows, Linux, or Chromebook are all fine).

DSFM Alumni Network

Graduates of DSFM have the opportunity to join the DSFM Alumni Network!

We have found that DSFM participants in previous sessions come together as a community and then want to keep in touch. Given the pace of change in Data Science, machine learning, automation, and digital transformation -- the alumni network is a great way to keep in touch with other professionals who are facing the same problems that you are. We therefore organize an annual Lecture and Apéro for the DSFM Alumni Network in the fall and invite graduates to join us at the EPFL College of Management for the event.

Membership in the alumni network is free -- and there is no cost for the Apéro. Just graduate from the course, get your certificate, and then come meet other like-minded professionals once a year to stay up on current trends. We look forward to seeing you at the next Apéro this fall!