(3-Day) Data Science and Analytics for Developers

An intensive beginner-to-advanced-level training course for developers encompassing all aspects of data science. This comprehensive program provides you with practical experience in a wide range of data science, analytics and machine learning topics.

In this three day comprehensive training course, you will explore many aspects of cutting-edge Data Science. This course combines beginner, intermediate and advanced courses into one efficient program. At the end of this course you will be considered professionally capable of developing and delivering data science products.

A three-day intensive course allows you to fully immerse yourself in data science. You will become confident with all aspects of machine learning. The extra time will allow you to explore more examples and provide more time to learn from the expert tuition.

Throughout each 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.

The three day course can optionally be extended into a 5-day, week long course which allows for further examples and a custom program catered towards your individual requirements. Please ask for further details.

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 a full beginner to advanced level 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

Each 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
  • 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
  • Develop solutions to mine, analyse and classify text
  • Discuss and explain neural networks, deep learning and a range of topologies
  • Employ semi-supervised machine learning to complex problems
  • Use ensemble methods to create cutting-edge machine learning products

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
  • 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
  • Text feature engineering
  • Text mining, representation and learning
  • Neural networks
  • Deep belief networks
  • Stacked denoising autoencoders
  • Convolutional neural networks
  • Semi-supervised machine learning
  • Ensemble methods
  • In-depth practical examples