Full-Stack Data Scientist: A Step-by-Step Resource Roadmap

1st rule of AI expertise: the path of becoming an AI expert requires dedication and hard work! Unfortunately, over the years, a plethora of AI boot camps have popped up, promising fast-track expertise to their customers but failing to deliver on these promises. As the domain gets saturated and competition increases, such boot camp certifications will be more and more meaningless. Of course, outliers exist but in my opinion, people in this case did not deviate a lot from the first rule after obtaining such certifications. So if you hope for an easy fix, you better look somewhere else because you are likely losing your time reading this post.

In this blog post, I will review a few of the books and courses that I have completed and have helped me to shape my skills as an AI professional (disclaimer: this blog post is NOT an ad for any of the following books or courses). I have selected the ones that have influenced me, some of them I found on my own and some were suggested by dear colleagues and friends.

Before reviewing the resources, I would like to point out that a full-stack data scientist is a generalist role. A generalist data scientist possesses a broad range of skills across the entire data science workflow, from data collection and cleaning to analysis, modeling, deployment, and system design. Unlike specialists who focus on a specific area (e.g., machine learning engineering, data engineering, or business analytics), a generalist data scientist handles end-to-end data science projects and can adapt to various roles and tasks as needed.

More senior roles demand not only tech skills but soft skills as well; therefore, I have created three main categories necessary for someone to excel in this role: i) machine learning knowledge, ii) system design, and iii) soft skills.

Machine Learning Knowledge

Data science is a broad domain and has to do with collecting, processing, analyzing, and visualizing data that result into tangible actions. It uses various techniques, including statistics, data engineering, and machine learning, to make data-driven decisions.

Machine learning, however, is a subset of data science that focuses specifically on developing algorithms that can learn patterns from data and make predictions or decisions without being explicitly programmed. The best definition that I have found so far comes from Tom Mitchell:

Machine Learning is the study of computer algorithms that improve automatically through experience.

Many books and courses can be found in this category which spawns dozens of sub-categories e.g., deep learning, reinforcement learning, unsupervised/semi-supervised/supervised learning, natural language processing, computer vision, and so on. Let's review some of my favorite resources in this category.

• Book: Machine Learning, Tom Mitchell, McGraw Hill, 1997

This was the first book that introduced me to the machine learning domain. This book provides a balance between theory and practice in a way that’s both clear and insightful. It covers key topics like decision trees, neural networks, and reinforcement learning, with plenty of examples to help readers grasp the concepts. While some parts feel a bit outdated given how fast the field has evolved, the book’s focus on fundamentals keeps it relevant. It’s a great starting point for anyone diving into machine learning.

• Book: Pattern recognition and machine learning, Christopher M. Bishop, 2006

Another all-time classic book that is highly regarded with a comprehensive text that bridges the gap between theory and practice in machine learning. What I love about this book, is its well-structure with intuitive explanations, helpful illustrations, and practical exercises that reinforce key concepts. The book covers from basic to complex concepts and will be a great addition to your toolkit.

• Book: Understanding Machine Learning: From Theory to Algorithms, Shai Ben-David and Shai Shalev-Shwartz, 2014

Although you might find this book a bit theoretical, it offers a thorough and insightful grasp when viewing Machine Learning from a purely mathematical point of view. It’s particularly well-suited for those looking to deepen their understanding of the theoretical foundations of machine learning; otherwise, you may find this book hard to read.

• Book: Deep learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2015

Are you interested in learning how neural networks work on a deeper level? The book offers exactly what you need, from the mathematical background e.g., linear algebra, and probability theory to the advanced Deep Learning concepts. One limitation is that it was written before the introduction of the transformer networks, but it does not really matter since this book will help you understand how to train such models (yes, transformer models also use backpropagation for training).

• Course: Deep Learning Specialization, Andrew Ng on Coursera

This specialization course is highly practical and dives into deep learning. Covering key topics like neural networks, convolutional networks, sequence models, and more, the specialization balances theory with hands-on coding exercises, often using python and tensorflow. You do not need to be a python expert but a very basic programming knowledge is required to follow the courses and complete the assignments.

• Course: Natural Language Processing (NLP) Specialization, deeplearning.ai on Coursera

This specialization course is a thorough and hands-on introduction to NLP. It covers essential topics such as sentiment analysis, machine translation, text generation, and attention models, with a strong emphasis on practical projects It is a great resource for anyone aiming to develop or enhance their NLP skills and tackle advanced applications like chatbots, summarization, and beyond.

• Book: LLM Engineer's Handbook, Paul Iusztin, Maxime Labonne, 2024

The book focuses on NLP, emphasizing practical, hands-on approaches over theoretical content. It explores modern techniques for fine-tuning large language models (LLMs), including methods like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), as well as strategies to optimize inference speed. Additionally, it introduces cutting-edge technologies like Retrieval-Augmented Generation (RAG). If you're new to LLM fine-tuning, this book serves as an excellent starting point to begin your journey and build a strong practical foundation.

The Art of System Design

Let's say that you know how to implement the most complex ML/DL models and achieve that stellar accuracy on the test set. Congratulations, you have managed to accomplish 10-20% of a standard AI project! Most of the time the modeling part, even though is the "sexiest" part of the job, can be also the easiest task (by all means, I do not discount the value or complexity of this part).

As a data scientist, you will be asked to access a dataset, do some cleaning, train a model, evaluate it a little bit, and hand it over to another team that will put it into production. As you might suspect, this model will most probably be wrapped in a class and be treated as another module in a larger system. The model itself is a specialized software, whose code was generated based on the provided training data.

As a full-stack data scientist, you need to think in systems, meaning a whole new level of headache including (but not limited!): data extraction/transformation/loading (ETL), model selection/training/evaluation/versioning, system scalability and deployment (also CI/CD if you want more headaches), A/B testing, monitoring and maintenance (data drifting, model retraining), ethical considerations, AI regulations, and compliance. That starts sounding like a job for a whole team, doesn't it? Well, below I review a few books and courses that I studied in the past that scratch the surface of the aforementioned system's aspects.

• Book: Designing Data-Intensive Applications, Martin Kleppmann, 2017

This is a must-read book if you are working with modern data systems that handle data at scale. The book reviews the principles behind scalable, reliable, and maintainable systems, covering topics like databases, distributed systems, stream processing, and batch processing. The content of the book focuses on trade-offs and real-world challenges which makes it highly applicable.

• Book: Machine Learning Design Patterns, Valliappa Lakshmanan, Sara Robinson, Michael Munn, 2020

This is another practical book for tackling common challenges in machine learning workflows. It presents a collection of reusable design patterns for data representation, model training, scalability, and deployment, drawing from real-world experience at Google. Each pattern is explained with clear examples and practical advice, making it easy to apply to your projects. It is great for learning to streamline ML pipelines and build more robust, scalable systems.

• Book: Designing Machine Learning Systems, Chip Huyen, 2022

A modern, practical book for building and deploying machine learning systems at scale. The book focuses on the end-to-end lifecycle of ML systems, covering data management, model development, deployment, monitoring, and infrastructure. It also covers the often-overlooked operational aspects of ML, such as versioning, testing, and continuous integration for machine learning. Furthermore, I really enjoyed the author's writing style which makes the whole reading process exciting. Overall, it is a great book for production-ready systems.

• Course: Software Design and Architecture Specialization, Alberta University on Coursera

This specialization course is for mastering the principles of building robust software systems. It covers key topics like design patterns, architectural styles, and software modeling, with a strong emphasis on applying these concepts through hands-on projects and case studies. The courses are well-structured, with clear explanations and engaging assignments that helped me translate theory into practice. Although the main programming language is Java, the examples and templates which are provided are applicable to any object-oriented programming (OOP) language such as python.

Soft Skills That Matter

Imagine that you have all the knowledge in the world and you are capable of designing systems that serve billions of people daily! What does it matter if you cannot communicate properly to your target audience, right? What if you cannot align simple tasks with colleagues or project requirements with clients due to miscommunication? Well, disaster is the most appropriate word for such cases.

The business markets and also workspaces consist of people who interact with each other on a daily basis. So a word of advice: tech skills will open doors, soft skills will enable you to pass through these doors, NO-MATTER how many AI models you can implement. Early in my career, I had the illusion that a strong software and AI background would be the only thing that would lead me to success. Boy/Girl, was I wrong! Below I include a couple of resources that you might find interesting, and please feel free to contact me with your suggestions!

• Book: Nonviolent Communication, Marshall Rosenberg, 1999

The book is a transformative guide to improving communication and fostering empathy in personal and professional relationships. It introduces a four-step framework: i) observations, ii) feelings, iii) needs, and iv) requests, which helps individuals express themselves honestly and listen to others with compassion. The book is filled with practical examples and exercises, making it easy to apply the principles in real-life situations. It’s a must-read for anyone looking to build deeper connections and resolve conflicts peacefully.

• Book: Soft Skills to Advance Your Developer Career, Zsolt Nagy, 2019

This is another practical guide for developers looking to grow beyond technical expertise and excel in their careers. The book covers essential soft skills like communication, teamwork, time management, and personal branding, offering actionable advice and real-world examples tailored to the tech industry. The writing style is fun to read and makes it easy to apply the lessons to your career. It’s a great resource to learn how to balance technical skills with the interpersonal abilities needed to excel in the tech world.

• Book: Engineers' Survival Guide, Merih Taze, 2021

Finally, this book is a practical and engaging guide for engineers navigating the challenges of the tech industry. The book offers actionable advice on topics like problem-solving, productivity, teamwork, and career growth, all tailored to the unique demands of engineering roles. It’s a nice read for engineers looking to sharpen their skills and advance their careers.

Final Thoughts

Becoming a successful full-stack data scientist is a never-ending process. You have to keep up with AI advances, and production-stable solutions in order to support your users. At the same time, you have to hone your soft skills so that you can communicate effectively, collaborate with others, and navigate workplace dynamics, which are essential for career growth and success beyond technical expertise. And above all, you must practise, practise, practise!

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** If you are interested in other ML use-cases, please contact me using the form (and also include a publicly available dataset for this case, I'm always curious to explore new problems).