5 Day Boot Camp

Build a foundation for Data Science

5 Day Technical Boot Camp

The Boot Camp provides a foundation for all of the core topics in Data Science.

You will learn how to:

      • Design, evaluate, and tune machine learning models
      • Read and interpret programming code in Python
      • Address a broad range of real-world problems and solutions

This course offers a comprehensive balance between theory and practice, with visualizations, demonstrations, exercises, case studies and projects. You’ll learn the most important concepts and models in the morning, and then practice with practicals throughout the afternoon. Along the way, you will learn to read code so that you can collaborate with – and manage – technical teams and real-world projects.

The course is limited to 40 participants to ensure time for one-on-one assistance.

Foundational concepts:

      • Data sampling, measurement, and wrangling
      • Exploratory data analysis
      • Data description, visualization, and graphing
      • Bias, variance, and the bias-variance tradeoff
      • Model validation and model cross-validation
      • Hyperparameter tuning and information leakage
      • Model evaluation and comparison
      • Model weighting of costs and benefits
      • Ensemble learning and meta-learning
      • Predictive labeling and data augmentation
      • Data-driven business models
      • Big Data, Map-Reduce, and Spark
      • Virtual Machines and Cloud Computing
      • Strategic Planning for a Digital Transformation
      • The management of talent and strategic Human Capital

Methods and models:

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

Course Outline

Day 1: Monday

08:30 - 09:30  Welcome                            
09:30 - 10:30  Introductions   
10:30 - 11:30  Jupyter & Python      
11:30 - 13:00  Group Lunch               
13:00 - 15:30  Core Concepts         
15:30 - 16:00  Break            
16:00 - 17:30  Demo: Data Exploration      
17:30 - 18:30  Exercise: Data Wrangling        
18:30 - 19:00  Project: Birthday Problem  

Day 2: Tuesday

08:30 - 11:30  Linear Models         
11:30 - 13:00  Interactive Lunch                 
13:00 - 14:00  Demo 2: Credit Default        
14:00 - 15:30  Exercise: Simple prediction     
15:30 - 16:00  Break            
16:00 - 19:00  Project: Credit Default        

Day 3: Wednesday

08:30 - 11:30  Similarity & Trees    
11:30 - 13:00  Interactive Lunch                 
13:00 - 14:00  Demo: Clustering            
14:00 - 15:30  Exercise: Complex predictions   
15:30 - 16:00  Break              
16:00 - 19:00  Project: Home Sales            

Day 4: Thursday

08:30 - 11:30  SVMs & Text Analysis  
11:30 - 13:00  Interactive Lunch                 
13:00 - 14:00  Demo: Sentiment Analysis    
14:00 - 15:30  Exercise: Dimension reduction   
15:30 - 16:00  Break            
16:00 - 19:00  Project: Pre-screening         

Day 5: Friday

08:30 - 11:30  Neural Nets           
11:30 - 13:00  Group Lunch               
13:00 - 14:15  Demo: Image Recognition     
14:15 - 14:30  Break             
14:30 - 16:30  AI Strategy       
16:30 - 17:00  Apéro          

Bias-Variance Tradeoff

Neural Networks

Basis Expansions

Venue

The DSFM Boot Camp is held on campus at EPFL (the École Polytechnique Fédérale de Lausanne) - part of the Swiss Federal Institute of Technology.

DSFM is aimed for those wanting to return to campus for the 'EPFL experience.' The venue adds to the intensity and ambition of the course by motivating participants to move quickly through a great amount of material in a relatively short amount of time. The five day boot camp covers much of the same material as a challenging, semester-long, masters course at EPFL.

EPFL is also home to over 350 laboratories and research groups, each working at the forefront of science and technology – with a diverse, committed and stimulating research community that is active over a wide spectrum of quantitative and design-focused disciplines.

Preparation

Most managers have forgotten their mathematics, so we emphasize visualizations of mathematical concepts instead of complicated proofs. Moreover, most boot camp participants are not professional programmers. We therefore present basic programming concepts and build from there to complete solutions.

Novice programmers will learn how to read programming code provided in solutions; more advanced students will learn to build those solutions from the bottom-up using scikit-learn APIs. Teaching assistants are available throughout the day to provide one-on-one assistance with practical problems. All participants will leave the course being able to build, evaluate, and work with real data and real models!

No prior training in Data Science is required to take DSFM, and the course is limited to 40 participants to ensure time for one-on-one assistance.

However, to get the most out of the Boot Camp, you will...

      • Be familiar with linear algebra (although we use very little math)
      • Be familiar with statistics (although we will review the basics)
      • Be conversant in English (the course will be given in English)
      • Bring a laptop (Mac, Windows, Linux, or Chromebook)

Python

We recommend that you dedicate 5 to 10 hours of online study with the Python programming language before the start of the Boot Camp course. If you are new to Python, a little bit of preparation will help you to get much more out of the class. You don't need to be a programmer, or to program solutions from scratch in the course, but you will look at real coding examples to see what it does. (And why!) We will email you several suggestions for online preparation in Python when you register.

But do not fear! We will have several highly-qualified EPFL graduate students on-hand to work with you one-on-one to answer questions about the programming code.



Please contact us if you have questions.