Have you ever felt confused by terms such as “data science” and “big data”? What is really the difference between AI and machine learning? How can you hire a good data scientist and how do you build a data-driven organisation? Have you ever thought you’d like to use data-science, but you don’t know where to start?
The Decision Maker’s Handbook to Data Science was written specifically for you. It covers all the topics that a non-technical decision maker needs to know in order to use data science within an organisation.
Driven by the author’s 10+ years of experience, the book’s aim is to demystify the jargon and offer answers to all the most common problems and questions that decision makers face when dealing with data. Topics include:
1) Explaining data science. Demystifying the differences between AI, machine learning and statistics.
2) Data management best practices.
3) How to think like a data scientist, without being one.
4) How to hire and manage data scientists.
5) How to setup the right culture in an organisation, in order to make it data-centric.
6) Case studies and examples based on real scenarios.
Data science, machine learning and artificial intelligence are amongst the main drivers of the technological revolution we are experiencing. If you are planning to collect and use data within your company, then the Decision Maker’s Handbook to Data Science will help you avoid the most common mistakes and pitfalls, and make the most out of your data.
About the author:
Dr. Stylianos (Stelios) Kampakis is an expert data scientist, with more than a decade of experience, who is living and working in London, UK. He holds a PhD in Computer Science from University College London, as well as an MSc in Informatics from the University of Edinburgh. He also holds degrees in Statistics, Cognitive Psychology, Economics and Intelligent Systems. He is a member of the Royal Statistical Society and an honorary research fellow in the UCL Centre for Blockchain Technologies.
He has worked with startups that have raised millions worth of funding on problems ranging from recommender systems to deep neural networks. He has also trained executives in data science through The Tesseract Academy (http://tesseract.academy). He often writes about data science, machine learning, blockchain and other topics at his personal blog The Data Scientist (http://thedatascientist.com).