But first: what's a data scientist?

Data science is an interdisciplinary field that combines statistics, mathematics, AI, programming, analytics, and storytelling to extract value from data. It serves as a bridge between data and the boardroom, connecting the business and technical worlds.

A data scientist applies statistical methods and uses a wide range of tools and techniques to analyze and prepare data. They also explain the meaning behind the results to various stakeholders, which is why a skilled data scientist can effectively communicate with individuals at different levels of expertise.

What makes a great data scientist in 2026

• Soft skills: strong communication skills, customer empathy, and the ability to simplify complex concepts for stakeholders.

• AI-first approach: proficiency with LLMs, RAG, agents, and modern AI tools rather than coding everything from scratch.

• Cloud platform expertise: deep knowledge of at least one major cloud platform (Azure, AWS, or Google Cloud) with formal certification.

• Service-oriented approach: leveraging APIs and managed services instead of building custom implementations for everything.

Most importantly: the mindset

In the age of AI where tools can do the work for you, understanding and knowledge in general become more important than ever. The design and architecture is where the human value is today, enabling higher quality output and less token usage.

Additionally, the more AI adoption grows, the more tokens are consumed. Unoptimized agents that are not designed for specific tasks will not only produce poor quality results but also be extremely expensive. This is why strategic thinking, problem framing, and solution architecture are the skills that distinguish exceptional data scientists in 2026.

The tech stack

1. Generative AI and modern tools

Since GenAI has forever changed how data scientists work, AI skills are now a non-negotiable.

Large Language Models (LLMs)

To work effectively with LLMs, you need to understand their capabilities, limitations, and appropriate use cases:

• Learn prompt engineering techniques to get reliable and accurate outputs from models.

• Explore tools like Claude Code for AI-assisted development.

RAG (Retrieval-Augmented Generation)

RAG involves techniques for enriching LLMs with specific organizational data. This includes building knowledge bases that integrate with AI systems, as well as understanding when RAG is preferable to fine-tuning.

AI agents

Working with AI agents requires designing and orchestrating autonomous agents for specific tasks. Don’t forget multi-agent systems for complex workflows!

2. Cloud platforms

Cloud expertise is no longer optional. Data scientists must understand how to leverage managed services rather than building everything from scratch.

Here are the key concepts you need to master:

• Using MLOps as a Service (Azure ML, SageMaker, Vertex AI)

• Leveraging pre-built APIs instead of custom implementations

• Understanding scalability, cost management, and security in cloud environments

• Deploying and monitoring models in production

Additionally, obtaining certification in at least one of these platforms will greatly strengthen your expertise (and credibility):

• Microsoft Certified: Azure Fundamentals

• AWS Certified Cloud Practitioner

• Google Cloud Professional Data Engineer

3. Foundational programming

Yes, AI tools can generate code. But you still need to understand programming fundamentals to review, debug, and guide AI outputs.

📚 Book rec: Automate the Boring Stuff with Python. A great book to start coding with Python, beginning with the foundational concepts of programming. It is very hands-on and practical if you do the exercises while reading it.

📚 Book rec: Data Science from Scratch First Principles with Python by Joel Grus. This book is focused on introducing basic Python, and practical coding concepts of what you may do day-to-day as a Data Scientist.

🎥 Must-watch: Corey Schafer - YouTube. A channel that covers a range of very basic (python installation, data types etc.) to more complex Python topics (e.g. building a web app).

4. Machine Learning fundamentals

Understanding ML concepts remains important, even as you increasingly use pre-built services and APIs. Before going into data science module details, here is a free course that we recommend: Machine Learning by Stanford University | Coursera. This alone is not enough in 2026, but it will give you a sound foundation to build on.

📚 Book rec: The Hundred-Page Machine Learning Book by Andriy Burkov. The author does an excellent job of giving an overview of what ML is about. Some math and coding examples are present, and it contains quite a lot of details for such a short book.

📚 Book rec: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Comprehensive reference on Machine Learning with practical examples using common Python libraries.

🎥 Must-watch: StatQuest with Josh Starmer - YouTube. Understand the essence hidden behind the complicated formulas used in data science and advanced statistics classes.

5. Practical experience and projects

Practice makes perfect: apply your knowledge through hands-on projects!

Build projects that showcase AI integration and cloud deployment, and/or participate in Kaggle competitions for real-world challenges. Don’t forget to document your work on GitHub, emphasizing problem-solving approach and business impact.

The business skills

1. Understanding problems and defining objectives

Your main goal as a data scientist is to derive actionable insights from data. The technical solution is only valuable if it solves the right problem. These skills differentiate good data scientists from great ones.

📚 Book rec: Cracked it!: How to solve big problems and sell solutions like top strategy consultants. This book trains you in how to tackle any challenging problem efficiently and sell its solution. Based on business case examples, it shows how to state, structure, and resolve problems and to be solution/results oriented.

For business cases situations, we recommend reading some very well-known business frameworks used by consulting firms to better structure your thoughts (no need to learn them by heart!):

• Case interview frameworks: a comprehensive guide – IGotAnOffer

• Case Interview Frameworks: Ultimate Guide | Management Consulted

2. Communication and stakeholder management

What is even more important than the solution or model you've created is how you share it with others. Data science is just as much about communicating your findings with clients or colleagues as it is about technical execution.

As a result, you need to master the following skills:

• Translating technical concepts for non-technical audiences

• Understanding business context and framing analyses around business problems

• Active listening and empathy to understand real stakeholder needs

• Managing expectations and explaining AI limitations clearly

📚 Book rec: Storytelling with Data: A Data Visualization Guide for Business Professionals. A mandatory reading at Agilytic. We see significant improvements in the quality of the visualizations and presentations of our colleagues after they have read it.

Bonus skills to strengthen your expertise

1. Collaboration tools

Data science is highly collaborative, and several people can work on the same code. In this context, understanding version control and teamwork tools such as Git and GitHub is essential.

🎥 Must-watch: Git and GitHub for Beginners - Crash Course - YouTube. This video shows in real-time the purpose of GitHub with practical examples.

💻 Website: git - the simple guide - no deep shit! (rogerdudler.github.io)

2. Business intelligence tools

Business intelligence (BI) uses existing data to inform decisions about a company's current state. Power BI and Tableau are the leading tools for data visualization and reporting.

🎥 Must-watch:  Power BI Tutorial From Beginner to Pro ⚡ Desktop to Dashboard in 60 Minutes ⏰ - YouTube.

🎥 Must-watch: Tableau Basics | Tableau Essential Training

3. System fundamentals

Computer science allows us to produce quality analyses and make great discoveries each day. Understanding how a computer works, especially when an incomprehensible error appears on the screen, can save you a lot of time and effort in the configuration of some tools or even in debugging.

🎥 Must-watch: MIT Open Course. Divided into 10 lectures/online chapters, this course is a great resource to learn about topics useful for developers, but usually not tackled in formal education, like shell tools, editors (vim...), command-line, Git, and debugging.

Interested in a data science career at Agilytic?

Did you know that Agilytic has current data scientist job openings?

We are always looking for new colleagues to help take our data science and engineering practice to the next level and contribute to the entrepreneurial project.

We value a work environment that allows you to do great work, improve your skillset in data science and, most importantly, be happy while doing it.

Sounds interesting?

Apply today!