Dr. Zacharias Voulgaris was born in Athens, Greece. He studied Production Engineering and Management at the Technical University of Crete, shifted to Computer Science through a Masters in Information Systems & Technology, and then to Data Science through a PhD in Machine Learning.
He has worked at Georgia Tech as a Research Fellow, at an e-marketing startup in Cyprus as an SEO manager, and as a Data Scientist in both Elavon (GA) and G2 Web Services (WA). He also was a Program Manager at Microsoft, on a data analytics pipeline for Bing.
Zacharias has authored several books on Data Science, mentors aspiring data scientists and data analytics learners, and maintains a Data Science & AI blog. Currently, he works as a consultant at GLG and as a start-up co-founder.
Mentoring meetings are about 30 minutes long and usually take place on Zoom or Jitsi (I'm also open to alternatives).
My Mentoring Topics
- data analytics
- data science mindset
- data visualization & storytelling
- data strategy
- business side of analytics
- logistics of data science pipeline - ETL, EDA, data modeling, etc.
- programming with Python & Julia
- data engineering (I/O operations, various file types, developing features, etc.)
- heuristics and problem-solving
- A/B testing
- resume and LI profile writing
- project review and tips on how to improve it
- cybersecurity & privacy
Data Strategy - How to Profit from a World of Big Data, Analytics and Artificial Intelligence
Key Insights from the Book Understanding the concept of big data: Marr emphasizes the importance of understanding what big data is and how it works in today’s digital world. The value of data: The book underscores the immense value of data in driving business strategies and informing critical decisions. Role of analytics: Marr explores the role of analytics in decoding the patterns, trends, and associations in big data. Artificial Intelligence (AI) and its impact: The author delves into the potential of AI and how it can be leveraged for better data analysis and strategic decision making. Importance of a data-driven strategy: The book emphasizes the necessity of developing a data-driven strategy for businesses to succeed. Role of data governance: Marr highlights the importance of data governance for maintaining the quality and reliability of data. Practical applications and case studies: The book is replete with real-world applications and case studies that make the concepts of big data, analytics, and AI more relatable and understandable. Data privacy and security: Marr also addresses the critical aspects of data privacy and security in the digital age. The future of data: The author offers insights into the future of data, analytics, and AI, and what it means for businesses of all sizes. Data culture: The book emphasizes the importance of fostering a data culture within an organization. Challenges in implementing a data strategy: Marr discusses potential challenges and offers advice on how to overcome them. An In-depth Analysis of the Book's Content In his book, Bernard Marr provides a comprehensive overview of big data, analytics, and AI, which are increasingly becoming critical to operational excellence and strategic decision-making in businesses. Having worked with these concepts for many years, I can certainly attest to the significance of the insights shared by Marr. The first key takeaway is the explanation of big data. Marr accurately defines it as a large volume of data, both structured and unstructured, that inundates businesses daily. The challenge is not just the volume, but how organizations can harness this data to extract meaningful insights and drive business decisions. The author then delves into the value of data, emphasizing that data in itself is not valuable unless it is analyzed to extract insights. This leads to the exploration of analytics - the systematic computational analysis of data. Marr explains that analytics helps decode patterns, trends, and associations in data, which can inform business strategies. One of the most significant sections of the book is where Marr discusses Artificial Intelligence (AI). AI, he argues, can be leveraged to analyze data more efficiently and accurately than traditional methods. He provides a balanced view, acknowledging both the potential and the risks of AI in data analysis and decision-making. Marr asserts that for businesses to succeed in the digital age, they must develop a data-driven strategy. This involves collecting relevant data, analyzing it to generate insights, and using those insights to inform business decisions. He argues that this approach can lead to more informed decision-making, improved operational efficiency, and increased competitive advantage. The author also highlights the importance of data governance, which ensures the quality, consistency, and reliability of data. This is particularly important in the context of big data, where the volume, variety, and velocity of data can be overwhelming. What sets this book apart is the inclusion of real-world applications and case studies that make the concepts of big data, analytics, and AI relatable and understandable. Marr also addresses the critical aspects of data privacy and security, stressing the need for robust measures to protect sensitive data. The book concludes with a look into the future of data, offering valuable insights into the evolving landscape of big data, analytics, and AI. Marr also discusses the importance of fostering a data culture within an organization and the potential challenges in implementing a data strategy. Overall, "Data Strategy - How to Profit from a World of Big Data, Analytics and Artificial Intelligence" provides a comprehensive guide for anyone interested in understanding the role of data in modern businesses. It is a must-read for business leaders, data professionals, and anyone interested in the transformative power of data.View
Julia for Machine Learning
Key Insights from "Julia for Machine Learning" Introduction to Julia: The book provides a comprehensive introduction to the Julia programming language, emphasizing its relevance and benefits in machine learning. Data Preprocessing: Voulgaris highlights the importance of data preprocessing, including data cleansing, normalization, and standardization in Julia. Machine Learning Algorithms: The book covers a wide range of machine learning algorithms, from linear regression to more complex ones like random forests and support vector machines. Deep Learning: The book delves into deep learning and how Julia can be used to create deep neural networks. Performance Optimization: Voulgaris explains how to optimize the performance of machine learning models using Julia, including parallel computing and GPU utilization. Practical Applications: The book provides real-world examples of machine learning applications using Julia, enabling readers to understand the practical usage of the language. Advanced Topics: The book also discusses advanced topics such as natural language processing (NLP), reinforcement learning, and time series analysis using Julia. Resource Materials: Voulgaris provides several resources and references for further learning and exploration of Julia for machine learning. Hands-on Approach: The book has a hands-on approach, with code snippets and step-by-step guides to implementing machine learning algorithms in Julia. Future of Julia: The book concludes with a discussion on the future directions of Julia in machine learning and data science. An In-Depth Analysis of "Julia for Machine Learning" "Julia for Machine Learning" by Zacharias Voulgaris is a comprehensive guide that provides a deep understanding of the Julia programming language and its application in machine learning. The book commences with an introduction to Julia and its benefits for machine learning. Julia is a high-level, high-performance, dynamic programming language, particularly suitable for numerical and scientific computing. It is designed to address the requirements of high-performance numerical and scientific computing while also being effective for general-purpose programming. Data preprocessing is an essential stage in any machine learning project. Voulgaris emphasizes its importance and provides a thorough guide on how to perform data cleansing, normalization, and standardization using Julia. The book then delves into a wide range of machine learning algorithms, from elementary ones like linear regression to more complex ones like random forests and support vector machines. Each algorithm is explained in detail, followed by a step-by-step guide on how to implement it using Julia. This approach aids in understanding both the theoretical aspect of these algorithms and their practical implementation. Deep learning is another significant focus of the book. Voulgaris explains how Julia can be used to create and train deep neural networks. This section is particularly beneficial for those interested in artificial intelligence and its subfield of deep learning. One of the distinguishing features of the book is the section on performance optimization. Here, Voulgaris explains how to optimize the performance of machine learning models using Julia, including parallel computing and GPU utilization. This knowledge is crucial when dealing with large datasets and complex models, common in the field of machine learning. The practical applications of machine learning using Julia are demonstrated through real-world examples, enabling readers to understand the practical usage of the language. These examples cover a wide range of applications, from predicting house prices to recognizing handwritten digits. The book not only covers the basics but also ventures into advanced topics such as natural language processing (NLP), reinforcement learning, and time series analysis using Julia. These topics are extremely relevant in the current data-driven industry and provide additional depth to the reader's knowledge. Hands-on approach: The book maintains a hands-on approach throughout, with code snippets and step-by-step guides to implementing machine learning algorithms in Julia. This approach allows readers to apply what they learn immediately and reinforces their understanding of the topics. Finally, the book concludes with a discussion on the future directions of Julia in machine learning and data science, providing readers with an understanding of the growth and potential of this language. In conclusion, "Julia for Machine Learning" by Zacharias Voulgaris is a comprehensive guide that is not just a tutorial for Julia but a valuable resource for understanding machine learning. It meticulously combines theory and practice, making it a must-read for anyone interested in machine learning and data science.View
Data Science - Mindset, Methodologies, and Misconceptions
Key Insights from Data Science - Mindset, Methodologies, and Misconceptions The book asserts that the right mindset is critical in data science, emphasizing on curiosity, skepticism, and creativity. Data science is a multidisciplinary field, combining elements of statistics, computer science, and domain knowledge. The book discusses various stages in a typical data science project, from problem formulation to data collection, data cleaning, exploratory data analysis, modeling, and evaluation. Voulgaris emphasizes the importance of good data, stating that even the best models and algorithms can't compensate for bad data. The book explores common misconceptions about data science, such as the belief that data science is all about algorithms or that it's the same as traditional statistics. Voulgaris provides advice on how to foster a culture of data-driven decision making within organizations. The book discusses the role of ethics and responsibility in data science. Voulgaris highlights the importance of storytelling and communication skills in data science. The book provides a comprehensive overview of methodologies and tools used in data science, including Python, R, SQL, and machine learning algorithms. Voulgaris also discusses the future of data science and the emerging trends in the field, such as automated machine learning and explainable AI. An In-Depth Summary and Analysis Data Science - Mindset, Methodologies, and Misconceptions, authored by Zacharias Voulgaris, is a comprehensive guide that attempts to demystify the field of data science. Voulgaris, a seasoned data scientist, delves into the intricate aspects of data science, articulating the mindset, methodologies, and misconceptions surrounding this field. Data science, as the book highlights, is a blend of various disciplines including statistics, computer science, and domain knowledge. This multidisciplinary nature of data science is what makes it a unique and powerful tool for extracting knowledge and insights from data. The book makes it clear that the success of any data science project relies on the integration of these disciplines. The book provides a step-by-step guide on how to execute a typical data science project, from problem formulation to data collection, cleaning, exploratory data analysis, modeling, and evaluation. Each step is crucial and requires a unique set of skills. For instance, data cleaning, often overlooked, is essential as even the best models and algorithms can't compensate for bad data. Common misconceptions about data science are thoroughly addressed in the book. For instance, the belief that data science is all about algorithms, or that it's the same as traditional statistics, is debunked. Data science involves much more than just algorithms; it involves understanding the problem, knowing how to manipulate and analyze data, and being able to interpret the results. Furthermore, while statistics is a component of data science, the latter goes beyond it to include other aspects such as computer science and domain expertise. Voulgaris also touches on the cultural aspect of data science in organizations. He provides advice on how to foster a culture of data-driven decision making within an organization, which is vital for businesses to thrive in today's data-driven economy. The book isn't just about the technical aspects of data science. Voulgaris emphasizes on the role of ethics and responsibility in data science. With increasing concerns about privacy and misuse of data, it's important for data scientists to operate ethically and responsibly. Storytelling and communication skills are also highlighted as key skills in data science. The ability to communicate complex results in a simple and understandable way is what separates a good data scientist from a great one. The book provides a comprehensive overview of methodologies and tools used in data science. It covers various programming languages such as Python, R, and SQL, along with machine learning algorithms. This serves as a practical guide for those interested in getting hands-on experience in data science. Finally, the book discusses the future of data science and the emerging trends in the field. This includes automated machine learning, which aims to automate the process of building machine learning models, and explainable AI, which seeks to make AI models more interpretable and transparent. In conclusion, Data Science - Mindset, Methodologies, and Misconceptions is a must-read for anyone interested in data science. It provides a holistic view of data science, debunking common misconceptions, and highlighting the skills and mindset needed to succeed in this field. The book bridges the gap between theory and practice, providing practical advice and insights based on the author's extensive experience in the field.View
Python Data Science Essentials
Alberto Boschetti, Luca Massaron
Key Facts and Insights from "Python Data Science Essentials" Python is a powerful and flexible programming language, ideal for data science due to its simplicity and wide range of libraries and tools. The book provides a comprehensive introduction to the tools and techniques of data science, such as data wrangling, data visualization, machine learning, and predictive modeling. It helps to understand how to leverage Python’s powerful libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn. The book provides practical examples and real-world case studies to help understand the application of data science concepts. It covers advanced topics like deep learning, natural language processing, and network analysis. It provides guidance on how to clean, transform, and preprocess data, which is crucial for building accurate models. The book emphasizes on the importance of exploratory data analysis and visualization for gaining insights from data. It provides an overview of different machine learning algorithms and their applications. The book outlines the entire data science project lifecycle, from data collection to evaluation of the results. It encourages the practice of reproducible research and provides guidelines on how to document and share your work effectively. It provides hands-on exercises and code snippets to practice and reinforce learning. A Detailed Analysis of "Python Data Science Essentials" "Python Data Science Essentials" by Alberto Boschetti and Luca Massaron is a comprehensive guide that introduces readers to the vast world of data science through the lens of the Python programming language. It covers a broad array of topics, ranging from fundamental elements of Python programming to advanced data science techniques and applications. The book begins by introducing Python as a powerful tool for data science, emphasizing its simplicity and flexibility. Python’s extensive range of libraries and tools makes it an ideal choice for data scientists. This aligns with my own experience as a professor, where I have seen many students and researchers gravitate towards Python due to its ease of use and wide-ranging capabilities. The authors then delve into the core techniques of data science, including data wrangling, data visualization, machine learning, and predictive modeling. They provide a deep dive into Python’s powerful libraries such as NumPy for numerical computing, Pandas for data manipulation and analysis, Matplotlib for data visualization, and Scikit-learn for machine learning. These libraries form the backbone of any data science project in Python, and the authors do a commendable job in explaining them in detail. The importance of data cleaning, transformation, and preprocessing is also well-covered in this book. As any experienced data scientist will attest, these steps are crucial for building accurate models, and I appreciate the authors’ emphasis on this often-overlooked aspect of data science. The book further explores advanced topics like deep learning, natural language processing, and network analysis. These topics are at the forefront of current data science research and applications, and their inclusion in the book makes it a valuable resource for both beginners and experienced practitioners. One of the standout features of this book is its emphasis on the practical application of data science concepts. The authors provide numerous examples and case studies that help readers understand how these techniques can be applied in the real world. This practical focus is supplemented with hands-on exercises and code snippets, which reinforce learning and allow readers to practice the concepts they have learned. The book also outlines the entire data science project lifecycle, from data collection to evaluation of the results. This holistic view of a data science project is invaluable for newcomers, as it provides a roadmap for conducting their own projects. Finally, the authors encourage the practice of reproducible research, providing guidelines on how to document and share work effectively. This is an essential skill for any data scientist, as it ensures that their work can be understood and replicated by others. In conclusion, "Python Data Science Essentials" is a comprehensive and practical guide to data science with Python. It covers a wide range of topics, from fundamental Python programming to advanced data science techniques, making it an invaluable resource for anyone interested in this exciting field.View
The Data Path Less Traveled: Step up Creativity using Heuristics in Data Science, Artificial Intelligence, and Beyond
Dr. Zacharias Voulgaris
Key Insights from The Data Path Less Traveled Data Science is not just about understanding data, it’s about understanding the context: Dr. Voulgaris emphasizes that understanding the context in which data is used is crucial. Data Scientists must be able to understand the business or sector they are working in. Heuristics in Data Science: The book explores the concept of heuristics – rules of thumb or mental shortcuts – in the context of data science. It shows how heuristics can lead to more creative problem-solving. Role of Creativity in Data Science: Dr. Voulgaris argues that creativity is an essential skill in data science. He suggests that creative thinking can help to find unique solutions to complex data problems. Artificial Intelligence (AI) and Heuristics: The book explores the intersection of AI and heuristics, discussing how heuristics can be used in the design and implementation of AI systems. Data Science Ethics: Dr. Voulgaris emphasizes the importance of ethical considerations in data science. He discusses the potential impacts and consequences of data science projects, stressing the need for responsible and ethical data usage. Limitations of Traditional Data Science Methods: The book examines the limitations of traditional data science methods and shows how heuristics can offer alternative approaches. Case Studies: The book offers several real-world case studies that demonstrate the application of heuristics in data science and AI. Future of Data Science: Dr. Voulgaris discusses the future of data science, including the ongoing integration of AI and data science, and the increasing importance of ethical considerations. Tools and Techniques: The book provides a comprehensive overview of current tools and techniques in data science, including programming languages, databases, and machine learning algorithms. AI, Data Science, and Beyond: The book delves into the future of AI and data science, discussing emerging technologies and trends, and how they will shape the field. Summary and Analysis "The Data Path Less Traveled" by Dr. Zacharias Voulgaris is an insightful exploration of the role of heuristics in data science and AI. It argues that while data science is often seen as a purely technical field, it also requires creativity and a deep understanding of the context in which data is used. One of the key insights from the book is the importance of understanding the context in which data is used. This is a point that resonates with me as a seasoned academic in this field. Too often, data scientists focus solely on the data itself, without considering the wider context. This can lead to misinterpretations and missed opportunities. In contrast, when data scientists understand the business or sector they are working in, they can provide more valuable insights and solutions. Dr. Voulgaris also explores the concept of heuristics, arguing that these mental shortcuts can lead to more creative problem-solving in data science. This is an interesting perspective that challenges the conventional view of heuristics as potentially problematic biases. Instead, Dr. Voulgaris shows how heuristics can be harnessed to find unique solutions to complex data problems. This aligns with the concept of "bounded rationality," which suggests that rational decision-making is often constrained by the limits of our knowledge and cognitive abilities. The intersection of AI and heuristics is another key theme in the book. Dr. Voulgaris discusses how heuristics can be used in the design and implementation of AI systems. This is a fascinating area of exploration, as it suggests that AI can be made more effective and efficient by incorporating human-like decision-making processes. Ethics is another important theme in the book. Dr. Voulgaris emphasizes the need for ethical considerations in data science, discussing the potential impacts and consequences of data science projects. This is a crucial point, as the misuse of data can have serious ethical implications. It aligns with the growing recognition of the need for responsible and ethical data usage in the field. The limitations of traditional data science methods are also explored in the book. Dr. Voulgaris shows how heuristics can offer alternative approaches to these limitations, demonstrating the value of creativity and flexible thinking in data science. The book includes several real-world case studies that demonstrate the application of heuristics in data science and AI. These case studies provide practical examples of the concepts discussed in the book, helping readers to understand how these ideas can be applied in practice. Finally, Dr. Voulgaris discusses the future of data science, including the ongoing integration of AI and data science, and the increasing importance of ethical considerations. He also provides a comprehensive overview of current tools and techniques in data science, and discusses emerging technologies and trends. Overall, "The Data Path Less Traveled" is a compelling and thought-provoking exploration of the role of heuristics in data science and AI. It challenges traditional approaches to data science, highlighting the importance of creativity, context, and ethics. It is a valuable resource for anyone interested in these fields.View