This is the brand new version of the hugely popular “Data Science for Developers” training. After two years of teaching thousands of Engineers around the world, this training has been rebuilt from the ground up to squeeze in more information, have bigger theories and provide better positioning.
In this intermediate-level course, we will delve into the most important topics in Data Science. The aim is to provide sufficient breadth to give you the appreciation so you can pick and choose to suit your specific problem.
The content matches the tasks and topics that production Engineers face on a day-to-day basis. Indeed, surveys suggest that more than half of an Engineer’s time is spent finding, collecting, organising and cleaning data. Therefore, we spend a significant amount of time learning how to handle and understand data.
Another goal of the intermediate course is to give a broad understanding of as many models as possible in the time available. If you are aware of the major catagories, types and instances of models, then you are better positioned to be able to choose the optimal model for the problem.
As usual, all the lessons have accompanying practical examples to cement the ideas and provide many nights-worth of tinkering fun!
Who will benefit
- Engineers with a basic understanding of Data whom want to take it to the next level
- Beginner-Intermediate Data Scientists wanting industrial experience
- People wanting experience in the production-level Data-Science tasks seen on a day-to-day basis
What you will achieve
The intermediate course is split into several self-contained modules that can be tailored to the audience and the time available. This means the content will vary from course to course and can be run over two days in its entirety.
At the end you will have a thorough understanding and, more importantly, a initial feeling for what works in certain situations and what doesn’t. This takes years to learn, but we present the material in such a way that it makes it easy to consume a huge amount of experience in a short about of time.
Topics covered in this training
* time permitting
- Probability distributions
- Summary statistics
- Sampling *
- Applications: Bayesian thinking and Markov Chains *
- Generalisation and Overfitting *
- In-depth Data Cleaning
- Visualisation 2
- Data availability and consistency
- Types of data
- Corrupted data
- Transforming data
- Scaling data 2
- Feature engineering (derived data)
- Feature selection
- Time series data
- Related topics
- In depth model evaluation *
- Technical numerical evaluation *
- Business numerical evaluation *
- Technical visual evaluation and analysis *
- Business visual evaluation *
- Dimensionality reduction
- Manifold learning
- Overview of models
- Grand challenge *
- Beginner-level Data Science experience. See the beginner-level course.
- Beginner/Intermediate-level Python knowledge (there will be a lot of code)
What You Need to Bring
- A charged laptop. Workshop resources are online and sometimes we have a lack of power sockets.
- A good night’s sleep
- Humility and a smile