Skip to content

The Data Scientist

the data scientist logo

AI & Data Science

I help executives, entrepreneurs and aspiring data scientists 

learn and use data science, AI and blockchain

AI & Data Science

I help executives, entrepreneurs and aspiring data scientists learn and use data
science, AI and blockchain


Completed Projects


Satisfied Clients


Happy Students


Token Economies


Completed Projects


Satisfied Clients


Happy Students


Token Economies

EssayPro research paper writers




I help identify problems in organisation and fix them using data science and/or blockchain. My work has helped startups raise more than $20million. I have run more than 100 successful seminars and talks for the C-level and companies of all sizes.

I am also an AI educator and mentor, having taught multiple students over the years, and helping them get jobs. Make sure to check out my course Beyond Machine.

About Us

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

Latest Posts

nik_pletikos defi

Event recording: Treasury Management for Web3

The Tesseract Academy recently organised an event where we dived into the world of Web3 treasury management. This session was designed for those keen on understanding the evolving landscape of…
Blurry Growing Digital Graph Interface Over Dark Blue Background

Nifty vs Sensex: Key Differences Explained

Investors and market enthusiasts often hear about Nifty and Sensex when discussing the Indian stock market. These two terms are fundamental to understanding how the market operates, yet they can…
Sign up and get a free copy of the Decision Maker’s Handbook to Data Science!

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.

Ethical considerations are crucial in AI and Data Science to ensure responsible and fair practices. Here are some key aspects to consider:

Bias and Fairness: Algorithms can inadvertently inherit biases from training data. It’s essential to detect and mitigate these biases to prevent discriminatory outcomes. This includes addressing issues related to gender, race, and other sensitive attributes.

Privacy: Protecting individuals’ data is paramount. Adhering to data protection regulations and obtaining informed consent when collecting and processing personal data is essential. Anonymization and encryption techniques help safeguard privacy.

Transparency: Transparency ensures that AI and Data Science processes are understandable and interpretable. This is crucial for building trust and identifying issues. Explainable AI (XAI) techniques are emerging to make AI decisions more transparent.

Accountability: Identifying who is responsible for AI decisions and actions is important. It could be the developers, organizations, or regulators. Establishing clear lines of accountability helps in addressing issues and holding parties responsible.

Data Security: Data breaches and misuse can have severe consequences. Robust security measures must be in place to protect data from unauthorized access and cyberattacks. Encryption, access controls, and regular security audits are some of the tools to ensure data security.

By addressing these ethical considerations, AI and Data Science practitioners can build systems that are more responsible, fair, and trustworthy.


Starting a career in Data Science and AI is an exciting journey. Here are some steps to help you get started:

Learn Programming Languages: Python and R are essential programming languages for Data Science and AI. They offer rich libraries and tools for data analysis, machine learning, and data visualization. Start with online tutorials or courses to build your programming skills.

Study Mathematics and Statistics: A strong foundation in mathematics and statistics is crucial. Concepts like linear algebra, calculus, probability, and statistical analysis are fundamental to understanding the principles behind AI and Data Science algorithms.

Explore Online & offline Courses and Resources:

Our online courses offer:

  1. Beyond Machine: For Aspiring Data Scientists

Are you a software developer, scientist, or engineer looking to dive into the exciting world of data science?

Are you a graduate student with a passion for data and a desire to kickstart your career in this dynamic field?

Are you a manager seeking to elevate your team’s skills and capabilities in data science and AI?

Beyond Machine is the answer you’ve been searching for. Our program brings together the best practices from Massive Open Online Courses (MOOCs), intensive bootcamps, and the depth of university courses, creating a one-of-a-kind learning experience that seamlessly combines online and in-person education.

In this comprehensive course, I share over a decade of my personal experience in data science to provide you with a program that offers unparalleled depth, breadth, and flexibility, all at an unbeatable price. Unlike large corporations, we focus on maintaining small class sizes to prioritize quality over quantity, ensuring you receive the individual attention you deserve.

Additionally, our program includes personalized mentoring directly from me, and we provide support in your job search to help you secure your dream position in the data science field.

Take the first step towards an exciting career in data science – get in touch with us today to discover how Beyond Machine can empower you to succeed!

Work on Personal Projects: Apply your knowledge by working on personal Data Science and AI projects. This hands-on experience is invaluable and allows you to build a portfolio to showcase your skills to potential employers.

Join Communities and Attend Conferences: Engage with the Data Science and AI community. Participate in online forums, attend local meetups, and consider joining platforms like GitHub, where you can collaborate on open-source projects. Attend conferences and workshops to stay updated on industry trends.

2. Tesseract Academy: Data Science For Executives

At Tesseract Academy, we specialize in providing education, consulting, and implementation services tailored for non-technical decision makers and businesses. Our primary objective is to empower leaders with the knowledge and capabilities to harness cutting-edge technologies such as AI, data science, and blockchain, and guide them through the process of practical implementation.

If you fall into any of these categories:

  • An entrepreneur with a vision for the future.
  • A forward-thinking leader committed to staying at the forefront of innovation.
  • A manager seeking to incorporate advanced technologies into your business.
  • Someone who’s genuinely interested in AI or blockchain but may not have a technical background.

Tesseract Academy is your destination. Elevate your skills and knowledge with our comprehensive data science courses, meticulously designed to cater to the specific needs of non-technical professionals. We provide the essential insights and expertise required to navigate the realms of data science, AI, and blockchain confidently.

Join us at Tesseract Academy, where you’ll gain the expertise to make informed strategic decisions and steer your organization toward a future empowered by data-driven innovation.

  1. 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.
  1. 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

Who is This Book For

Business leaders and technology enthusiasts who are trying to understand how to think about data science and AI

Oder them