Welcome to the Data Scientist!
I am an expert data scientist, chartered statistician (by the Royal Statistical Society), AI engineer and blockchain and tokenomics expert.
Me and my team do the following:
Dr Stylianos Kampakis
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What Our Clients Say
Data Science is a multidisciplinary field that uses various techniques, algorithms, processes, and systems to extract meaningful insights and knowledge from structured and unstructured data. It combines elements of statistics, machine learning, data engineering, and domain expertise to uncover patterns, make predictions, and support data-driven decision-making.
Artificial Intelligence refers to the simulation of human intelligence in machines. It involves the development of computer systems that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, solving problems, and making decisions. AI encompasses various subfields, including machine learning, natural language processing, and computer vision.
Data Science and AI are closely connected. Data Science often serves as the foundation for AI development. Data scientists use AI techniques to build predictive models and extract insights from data. AI, in turn, relies on the data collected and processed by data scientists to make intelligent decisions and improve its performance.
The fields of Data Science and AI are continually evolving. Here are some notable trends that are shaping the industry:
Explainable AI (XAI): With the increasing complexity of AI models, there’s a growing emphasis on making AI algorithms more transparent and interpretable. XAI techniques aim to provide insights into how AI models arrive at specific decisions, making them more trustworthy and understandable.
Ethical AI: Addressing ethical concerns is a top priority. Efforts are being made to ensure that AI systems are free from bias and discrimination, promote fairness, and respect privacy. Ethical AI initiatives focus on responsible data collection and usage.
AI in Healthcare: AI is transforming healthcare by enabling quicker and more accurate diagnosis, drug discovery, and personalized patient care. AI-driven solutions assist in medical imaging, drug development, and patient monitoring, ultimately improving healthcare outcomes.
Edge Computing: Edge computing is becoming increasingly important in AI. It involves processing data closer to its source, reducing latency and enabling real-time AI applications. This is crucial for scenarios like autonomous vehicles, IoT devices, and remote sensing.
Reinforcement Learning: Reinforcement learning is a subfield of AI that’s gaining traction, especially in decision-making and robotics. It’s used in training agents to interact with their environments and learn optimal strategies, making it valuable in areas like autonomous robotics and game playing.
These trends reflect the dynamic nature of Data Science and AI, with a strong focus on making AI more responsible, applicable to various domains, and capable of handling real-time data processing and decision-making. Stay updated on these trends to remain at the forefront of these rapidly advancing fields.
The Decision Maker’s Handbook to Data Science: A Guide for Non-Technical Executives, Managers, and Founders
Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don’t realize is that data science is in fact quite multidisciplinary—useful in the hands of business analysts, communications strategists, designers, and more.
With the second edition ofThe 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.
Business Models in Emerging Technologies: Data Science, AI, and Blockchain Paperback
This book is a practical guide to two of the most important emerging technologies: data science/AI and blockchain.
The world of technology progresses so quickly that we often don’t realize how far we’ve come. Over the last 20 years, technologies like data science, artificial intelligence, the Internet of Things, and blockchain have transformed the world of business, industry, and society.
These emerging technologies offer a wide range of opportunities. However, they also create new challenges businesses must face, such as developing new business models, and discovering the best adoption strategies.
This book is a practical guide to two of the most important emerging technologies: data science/AI and blockchain. With broad applicability across all sectors, decision-makers would greatly benefit from understanding these fields.
3. Predicting the Unknown: The History and Future of Data Science and Artificial Intelligence
As a society, we’re in a constant struggle to control uncertainty and predict the unknown. Quite often, we think of scientific fields and theories as being separate from each other. But a more careful investigation can uncover the common thread that ties many of those together. From ChatGPT, to Amazon’s Alexa, to Apple’s Siri, data science, and computer science have become part of our lives. In the meantime, the demand for data scientists has grown, as the field has been increasingly called the “sexiest profession.”
This book attempts to specifically cover this gap in literature between data science, machine learning and artificial intelligence (AI). How was uncertainty approached historically, and how has it evolved since? What schools of thought exist in philosophy, mathematics, and engineering, and what role did they play in the development of data science? It uses the history of data science as a stepping stone to explain what the future might hold.
Predicting the Unknown provides the framework that will help you understand where AI is headed, and how to best prepare for the world that’s coming in the next few years, both as a society and within a business. It is not technical and avoids equations or technical explanations, yet is written for the intellectually curious reader, and the technical expert interested in the historical details that can help contextualize how we got here.
What You’ll Learn
- Explore the bigger picture of data science and see how to best anticipate future changes in that field
- Understand machine learning, AI, and data science
- Examine data science and AI through engaging historical and human-centric narratives
Who is This Book For
Business leaders and technology enthusiasts who are trying to understand how to think about data science and AI