Data Science and Analytics for Developers (Machine Learning, Wednesday)
GOTO Copenhagen 2017

Data Science and Analytics for Developers (Machine Learning, Wednesday)

Wednesday Oct 4
09:00 –
Room 16

Register for this masterclass

In this one-day beginners-level course, you will be introduced to a range of fundamental data science concepts. You will discover how to interrogate data, choose which learning methods suit your problems and how to achieve results quickly.

Throughout the day theory will be complemented by "peer-instruction"; a teaching method that improves your learning experience by asking you to solve examples. This will provide you with valuable experience that you can apply to your own problems.

Who will benefit

This course is aimed towards developers, in which we will delve into the mathematics behind the code as well as developing real life algorithms in Python. One-to-one help will be provided for developers new to Python and all algorithms, frameworks and libraries used will be demonstrated by the instructor.

This is an introductory course, which is suitible for most users with limited development experience. Some experience of Python is helpful, but not necessary. No data science experience is expected.

What you will achieve

The day will comprise of a series of sub-hour theoretical sessions separated by practical exercises. It will cover a range of topics, but it is expected that you will be able to:

  • Discuss the differences between types of learning
  • Describe problems in a way which can be solved with Data Science
  • Understand the difference between regression and classification
  • Solve problems using regression algorithms
  • Solve problems using classification algorithms
  • Learn how to avoid overfitting and appreciate generalisation
  • Develop features within data
  • Describe how and where to obtain data

Topics covered in this training

  • How data science fits within a business context
  • Data science processes and language
  • Information and uncertainty
  • Types of learning
  • Segmentation
  • Modelling
  • Overfitting and generalisation
  • Holdout and validation techniques
  • Optimisation and simple data processing
  • Linear regression
  • Classification and clustering
  • Feature engineering
  • An in-depth practical example demonstrating the day's concepts