(1-day) Data Science and Analytics for Developers (Intermediate)

In this one-day intermediate-level course, you will discover cutting edge machine learning and data science techniques. The focus of this course is to comprehensively provide you with the theoretical and practical aspects of a wide variety of algorithms. You will also discover unsupervised methods and use a range of tools and libraries to perform a variety of tasks.

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 intermediate-level course, and it is expected that you will have had some experience to Python and Data Science. This can be achieved by attending the beginners course.

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:

  • Evaluate models numerically
  • Investigate and assess models visually
  • Have practical experience in industrial statistics
  • Further enhance data pre-processing skills
  • Understand unsupervised learning
  • Gain experience in a wide variety of Machine Learning algorithms

Topics covered in this training

  • Numerical and visual model evaluation
  • Introduction and application of statistics in data science
  • Understand the practical steps to design and deploy models
  • Further experience with real-life messy data
  • Unsupervised Machine Learning
  • A range of Machine Learning models: e.g. Logistic regression, linear and nonlinear SVMs, decision trees, etc.
  • Introduction to tooling, testing and deployment
  • An in-depth practical example demonstrating the day’s concepts