The Decision Maker’s Handbook to Data Science
Technology

The Decision Makers Handbook to Data Science – An Interview with Stylianos Kampakis

So, tell us a bit about yourself and your background.

My name is Stylianos, but people call me Stelios. I am a data scientist, researcher and entrepreneur, and I have worked in the areas of AI, data science and machine learning for around a decade now.

I have completed an MSc of Informatics in The University of Edinburgh, and I also hold a PhD in Computer Science from University College London. I am also a member of the Royal Statistical Society, and an honorary research fellow at UCL Centre for Blockchain Technologies.

I have worked in many different areas in data science: deep learning and neural networks, recommender systems, natural language processing, statistical modelling, Bayesian statistics, and many more. In the last couple of years, I have also done lots of work in the space of blockchain, especially the design and evaluation of token economies.

What is your current focus?

I am working on a few different things. First of all, I am the CEO of the Tesseract Academy. The Tesseract Academy’s mission is to educate decision makers on deep technical topics, such as data science, blockchain and deep learning. The key here is the focus on non-technical decision makers.

Besides this, I am also involved in the following:

  • CEO at ADAN, the Automated Data Analyst: A product that automates the data science pipeline, unlocking the power of data science to companies that lack the resources to create proper data science teams.
  • Electi consulting: A data science and blockchain boutique consultancy, composed of a network of elite individuals.
  • I regularly blog and express my opinion on various mediums, including The Data Scientist, and academic journals such as the journal of the British Blockchain Association.

So, why did you call the book “The Decision Maker’s Handbook to Data Science?”

The book was born out of the frustration that I saw many decision makers face around understanding data science. There is lots of jargon, lots of buzzwords and lots of confusion in this area. In some cases, I might even say that the confusion is created deliberately by companies that are trying to sell services.

In the end of the day, what a decision maker cares about is whether data science can help a company grow further. Data science can seem complicated at first, but with proper instruction, it is easy to see how it could be applied, what the benefits would be and how the costs, in terms of time and money, should be handled.

The book is based on my experience and the many interactions with clients over the years. It summarises the core concepts that a decision maker needs to know, things such as:

  • What are the different kinds of machine learning, and how can they be applied in common business use cases?
  • How to set up the right culture to absorb the best data scientists?
  • What are the different types of data scientists and which one should I hire?

Why is the tagline “A guide for non-technical executives, managers and founders”?

The focus is exactly on those people that lack the technical background or knowledge to understand the subject in depth. The key here is that you don’t need to be an expert data scientist, in order to understand how to use data science, or structure a data science project.

Most decision makers in big organisations, from CEOs to mid-level managers, care about the impact that a technology can have on things such as profit margins and efficiency. The book speaks their language, and helps them understand how and where data science could be applied, what people should be hired and what steps are required to ensure that the impact is maximized.

Can you tell us some of the benefits of applying data science in an organisation?

There are countless, so it is really difficult to provide a comprehensive list here. However, I can give you some examples:

  • Machine learning can be used to automate tasks done by humans. This has already started happening for simpler tasks (such as object detection), but now, through the help of deep learning, we are moving on to image and video captioning. We also see the same trend with the use of chatbots in customer support.
  • Data science can be a powerful tool in optimising supply and operation chains. The combination of predictive modelling and advanced optimisation algorithms can reduce costs and time and increase margins.
  • Personalisation is another huge benefit of data science. Everything, from product recommendations, to medicine, will be more personalised through the use of big datasets that predict what will suit each and everyone best.

What are some important points that summarise your book?

Here are some of the most important points:

  • It is important to have a data strategy from day 0. I have seen horrible mistakes taking place, because of mistakes that took place in the early stages of data collection. Failing to plan is planning to fail.
  • Don’t follow every new trend in technology. There are great technological innovations coming out all the time: deep learning, blockchain, NoSQL. However, you need to make sure that this is the best solution for you, and you are not simply imitating what everyone is doing.
  • There are different types of data scientists. You need to understand in what problems each one excels, and what challenges your business faces, before you set out to hire someone. Having the right culture is key in hiring and keeping good people.

Finally, the book also contains many examples and use cases, coming from my experience, but there is not enough space here to talk about everything.

Which technologies in the are of AI you believe will come to dominate in the next few years?

I am a big believer in deep learning. Deep learning algorithms become more and more complicated, and their performance becomes better as we feed these algorithms more data. Performance of deep neural networks in computer vision and audio recognition is getting better and better, and starts to surpass human performance. Deep learning will also become better at generating data, from works from art, to voice generation. Many of the things we will see over the next few years will start resembling what we call general AI, that is, AI that thinks a lot like a human.

Another technology I really believe in is blockchain. Blockchain has many interesting use cases, especially around trust, data sharing and incentivisation. There are cases where it is overhyped, but solutions like Hyperledger Fabric, and IPFS are going to disrupt many industries in the next few years.

So, where can we buy the book from?

You can buy the book in .pdf format from my website The Data Scientist. You can also buy it in digital format in many other stores, like Amazon, Google Play Store, iTunes stores, and many others.

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