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<b>Data Science for Developers (Machine Learning) 2018 </b><br>with Phil Winder <br>2-day public course

Data Science for Developers (Machine Learning) 2018
with Phil Winder
2-day public course

CHF 2,000.00


Group discounts: 3+ people 10% / 6+ people 15%


In this special two-day beginner-intermediate course, you will gain practical data science experience under the guidance of an industry expert. You will learn how to structure a data science project which will significantly increase your chances of producing valuable work.

We will complement this with technical descriptions of a range of techniques, from deriving the decision tree algorithm through to an introduction to deep learning. You will learn different ways of scoring model performance and how to avoid the scourge of data science, overfitting.

The goal of this course is to expose you to as many techniques as possible whilst explaining best practices to avoid common pitfalls. At the end of the two days, you will have the ability to talk about data science with confidence and begin to work on data science projects.


This course is aimed towards developers with some experience of software development and some experience in Python. We will avoid complex mathematics, preferring visualisation and experimentation, but high-school mathematics knowledge is required.

One-to-one help will be provided for those that are less experienced, but I would recommend learning Python before the training. All new code, classes, libraries and frameworks will be explained in full by the instructor.

No Data Science experience is expected.


  • How data science fits within a business context
  • Data science processes and terminology
  • Information and uncertainty
  • Segmentation
  • Modelling
  • Overfitting and generalisation
  • Holdout and validation techniques
  • Optimisation and simple data processing
  • A range of regression techniques
  • A range of Classification techniques (Logistic, SVM, KNN, Decision Trees, Naive Bayes, Gaussian Processes, etc., etc.)
  • A range of Clustering techniques (NN, Hierarchical, K-Means, etc.)
  • Numerical and visual model evaluation
  • Neural networks/Deep learning
  • Stacked denoising autoencoders
  • Convolutional neural networks
  • Ensemble methods
  • In-class competitions if time allows


We run this course every time we hit the 10-people-count! This means that we do not have a fixed date for the course, BUT it also means that you will be guaranteed a smaller class. We value maximum learning for everyone.

When you book the course, please note that the only option is to ask for an invoice. As soon as we have a course date we will send you the invoice.


  • All attendees must bring their own laptop
  • The practical material will be delivered through an online environment (i.e. through your web browser). Offline use requires Docker.
  • All content will be delivered to you by email.


    Phil is an internationally recognised speaker and friend with expertise that lies between cloud-native software and data science. He is a strong believer that we should be avoiding the same mistakes caused by the disintegration of dev and ops in data science. And that we need a new breed of engineers that span data-dev-ops.

    One of Phil’s businesses, Winder Research, provides cloud-native data science consultancytraining and development. He also runs the website which provides free and paid data science training courses and workshops to help the next generation of engineering-focused data scientists.

    Language: English
    Address: Zürich (exact location TBA)
    Required no. of attendees: 10
    Date: Once we reach 10 attendees, we will find a date that fits everyone
    Duration: 2 days. Both days 9:00 to 17:00
    Price: 2500 CHF incl. VAT incl. course materials and meals
    Discounts: 3+ people: 15% / 6+ people: 20%
    We do not provide any refunds when an invoice has been paid. What happens in case you cannot attend the course?
    1) You are welcome to pass on the place to a colleague or
    2) You are welcome to attend a later course in our course calendar.


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