The Decision Maker’s Handbook to Data Science: A guide for non-technical executives, managers and founders By Stylianos Kampakis Ph.D.
About The Book:
The Decision Maker’s Handbook to Data Science By Stylianos Kampakis.
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 if they are to use data science within their 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 Dr. Stylianos (Stelios) Kampakis is a data scientist who is living and working in London, UK. He holds a Ph.D. 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 Technologies1. He has many years of academic and industrial experience in all fields of data science: statistical modeling, machine learning, classic AI, optimization and more. Stylianos’ academic experience ranges across various domains.
Stelios is one of the foremost experts in the area of sports analytics, having done his Ph.D. in the use of machine learning for predicting football injuries. He has also done work in the area of neural networks, computational neuroscience, and cognitive science. He is also doing research in blockchain and more specifically in the area of tokenomics, where he studies topics such as the best mechanisms for handling volatility in token economies and evaluating Initial Coin Offerings (ICOs).
In terms of industrial experience, Stylianos has worked on a wide range of problems. Some examples include using deep learning to analyze data from mobile sensors and radar devices, to recommender systems, to natural language processing for social media data. He has also done work in the areas of econometrics, Bayesian modeling, forecasting, and research design.
He also has lots of experience in consulting for startups, having worked with companies that have raised millions in funding. Stylianos is also very active in the area of data science education. He the founder of The Tesseract Academy2, a company whose mission is to help decision makers understand deep technical topics such as machine learning and blockchain.
Stelios is also teaching “Social Media Analytics”, and “Quantitative Methods and Statistics with R” in the Cyprus International Institute of Management3. Finally, he often writes about data science, machine learning, blockchain and other topics on his personal blog The Data Scientist4. http://tesseract.academy
Did You Know: (Book Articles)
CORE FIELDS OF DATA SCIENCE
Data science has three core fields, namely artificial intelligence, machine learning, and statistics.
Artificial Intelligence is all about replicating human brain function in a machine. The primary functions that AI should perform are logical reasoning, self-correction, and learning. While it has a wide range of applications, it is also a highly complicated technology because to make machines smart, a lot of data and computing power is required.
Machine learning refers to a computer’s ability to learn and improve beyond the scope of its programming. Thus, it relies on creating algorithms that are capable of learning from the data they are given. They are also designed to garner insights and then make forecasts regarding data they haven’t previously analyzed.
There are three approaches to machine learning, namely supervised, unsupervised and reinforcement learning, plus some sub-fields (such as semi-supervised learning). Here, we will be talking only about supervised and unsupervised learning, since this is what is mainly used in business.
Let’s say you want to sort all your photographs based on content.
In supervised learning, you would provide the computer with labeled examples. So, you’d give it a picture of a dog and label it animal. Then you’d feed it a picture of a person and label it human. The machine will then sort all the remaining pictures.
In unsupervised learning, you’d just give the machine all the photos and let it understand the different characteristics and organize your photos.
In reinforcement learning, the machine learns based on errors and rewards. Thus, the machine analyzes its actions and their results.
Statistics is an essential tool in the arsenal of any data scientist because it helps to develop and study methods to collect, analyze, interpret, and present data. The numerous methodologies it uses enable data scientists to:
– Design experiments and interpret results to improve product decision-making
– Build signal-predicting models
– Transform data into insights
– Understand engagement, conversions, retention, leads and more
– Make intelligent estimations
– Use data to tell the story
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