Phil Winder: Data Science for Production Engineers
GOTO Academy is excited to host UK-based Phil Winder of Winder Research, for an intensive 2-day Data Science for Production Engineers Masterclass, that will leave you with practical tools for utilising Machine Learning principles in your organisation.
This masterclass combines Intermediate & Advanced knowledge into one efficient program. At the end of this Masterclass you will be considered professionally capable of developing and delivering data science products and immediately start applying what you've learnt.
This is the brand new version of the popular “Data Science for Developers” Masterclass from Winder Research. After two years of teaching thousands of Engineers around the world, this Masterclass has been rebuilt from the ground up to squeeze in more information, have bigger theories and provide better positioning.
Why should you attend?
Because it is focused on outcomes. At the end you will have a thorough understanding and, more importantly, an 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.
Day 2 represents a specialisation in a particular subject. The topics have been chosen to represent either specific production pain-points or advanced applications of technology. Since this may be the first time you learning about these techniques, care will be taken to work from first principals. You will leave with a solid understanding of the topics and be provided with the confidence to use them in production.
Who will benefit
- Engineers with a basic understanding of Data whom want to take it to the next level
- Engineers with an intermediate-level understanding of Data Science who are ready to move into production or more complex problems
- Beginner-Intermediate Data Scientists wanting industrial experience
- People wanting experience in the production-level Data-Science tasks seen on a day-to-day basis
On Day 1 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 Day 1 is to give a broad understanding of as many models as possible in the time available. If you are aware of the major categories, types and instances of models, then you are better positioned to be able to choose the optimal model for the problem.
On Day 2 we look at some of the most complex applications of Data Science in industry. This part of the Masterclass skips past the basics and steps firmly into production-level territory. There are two themes in Day 2.
The first goal is to provide you with the tools necessary to run your models in production. Here we focus on how to (not) interpret your models and provide some very good reasons why you might want to keep it simple stupid (KISS). We’ll also look at some tricks and tools that can speed up your workflow tenfold. Finally, ensembles provide an efficient way to solve complex problems. If you think your problem is too difficult to solve, this is the Masterclass for you.
The second goal is to look at some of the newest and most complicated modelling techniques that are changing the world today. Namely, Deep Learning and Natural Language Processing (NLP). These two fields have ballooned rapidly and the content has required a complete rewrite due to new advances in techniques and technology. If you have data that is complex enough, and that’s a big if, then this section will help you deal with that.
All content is accompanied with practical, hands-on worksheets that demonstrate the content.
Agenda: Day 1
- 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 *
* time permitting
Agenda: Day 2
- Model interpretation *
- Model automation *
- Natural language processing (NLP)
- Text feature engineering
- Latent information and embeddings
- Deep Learning
- From the ground up
- Multi-layer perceptrons
- Convolutional neural networks
- Recurrent neural networks *
- Grand challenge *
* time permitting
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