The Decision Maker's Handbook to Data Science
Apress (Verlag)
978-1-4842-5493-6 (ISBN)
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With the second edition of The Decision Maker’s Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists
Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide.
What You Will Learn
Understand how data science can be used within your business.
Recognize the differences between AI, machine learning, and statistics.
Become skilled at thinking like a data scientist, without being one.
Discover how to hire and manage data scientists.
Comprehend how to build the right environment in order to make your organization data-driven.
Who This Book Is ForStartup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.
Dr. Stylianos (Stelios) Kampakis is a data scientist who lives and works 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 many years of academic and industrial experience in all fields of data science like statistical modelling, machine learning, classic AI, optimization and more. Throughout his career, Stylianos has been involved in a wide range of projects: from using deep learning to analyze data from mobile sensors and radar devices, to recommender systems, to natural language processing for social media data to predicting sports outcomes. He has also done work in the areas of econometrics, Bayesian modelling, forecasting and research design. He also has many years of experience in consulting for startups and scale-ups, having successfully worked with companies of all stages, some of which have raised millions of dollars in funding. He is still providing services in data science and blockchain, as a partner in Electi Consulting. In the academic domain, he is one of the foremost experts in the area of sports analytics, having done his PhD in the use of machine learning for predicting football injuries. He has also published papers in the areas neural networks, computational neuroscience and cognitive science. Finally, he is also involved in blockchain research and more specifically in the areas of tokenomics, supply chains and securitization of assets. Stylianos is also very active in the area of data science education. He is the founder of The Tesseract Academy, a company whose mission is to help decision makers understand deep technical topics such as machine learning and blockchain. He is also teaching “Social Media Analytics”, and “Quantitative Methods and Statistics with R” in the Cyprus International Institute of Management, and runs his own data science school in London called Datalyst. He often writes about data science, machine learning, blockchain and other topics at his personal blog: The Data Scientist (thedatascientist.com).
Chapter 1: Demystifying Data Science and All the Other Buzzwords.- Chapter 2: Data Management.- Chapter 3: Data Collection Problems.- Chapter 4: How to Keep Data Tidy.- Chapter 5: Thinking like a Data Scientist (Without Being One).- Chapter 6: A Short Introduction to Statistics.- Chapter 7: A Short Introduction to Machine Learning.- Chapter 8: Problem Solving.- Chapter 9: Pitfalls.- Chapter 10: Hiring and Managing Data Scientists.- Chapter 11: Building a Data-Science Culture.- Chapter 12: Epilogue: Data Science Rules the World.- Appendix A: Tools for Data Science.-
| Erscheinungsdatum | 10.12.2019 |
|---|---|
| Zusatzinfo | 21 Illustrations, black and white; VIII, 156 p. 21 illus. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Schlagworte | Data analytics explained • Data culture • Data Science • Data science for executives • Data science for managers • Data science made easy • Data strategy • How to hire data scientists • How to use machine learning in business • ML vs AI • Types of machine learning |
| ISBN-10 | 1-4842-5493-7 / 1484254937 |
| ISBN-13 | 978-1-4842-5493-6 / 9781484254936 |
| Zustand | Neuware |
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