Data Science First

A detailed, up-to-date walkthrough for implementing language models in data science applications In Data Science First: Using Language Models in AI-Enabled Applications, the Chief AI Officer at Intersect AI, John Hawkins, sets out the critical challenge facing data scientists today: how to effectively integrate powerful language models into their workflows while adhering to data science principles that ensures your data generates reliable conclusions. Hawkins provides a practical roadmap for leveraging these revolutionary tools while maintaining the analytical rigor that separates successful implementations from costly failures. This guide skips hype and jargon, focusing instead on nine proven strategies for applying language models in real-world data science projects. From exploiting semantic vectors and few-shot prompting to synthetic data generation and developing agentic AI applications, Data Science First presents concrete design patterns that remain relevant despite rapidly evolving technologies. Each approach is illustrated with detailed case studies, including complaint processing and resume filtering, demonstrating how to evaluate model performance, handle failure modes, and deliver measurable business value. Data Science First is perfect for data scientists interested in enhancing their traditional statistical and machine learning skills with modern AI capabilities. It’s also a must-read for software engineers building language model-powered products and technical managers interested in deploying these tools reliably. Proven, practical techniques for integrating language models into your data science workflows Data Science First: Using Language Models in AI-Enabled Applications, by Intersect AI’s Chief AI Officer John Hawkins, explains how practicing data scientists can integrate language models in data science workflows without abandoning essential principles of reliability, accuracy, and efficacy. Hawkins offers crystal-clear guidance on when, where, and how data scientists can integrate language models into their existing workflows without exposing themselves or their companies to unnecessary risks. This guide walks you through strategic design patterns for incorporating language models into real-world data science projects. It avoids strategies and techniques that rely heavily on proprietary tools that are likely to evolve very quickly (or could disappear entirely) in the near future. Instead, the author presents foundational methodologies that will remain valuable regardless of how individual platforms or services change. The book combines sound theory with practical case studies that cover common data science projects in the education, insurance, telecommunications, media and banking industries. Including customer churn analysis, customer complaint routing and document processing, demonstrating how language models can enhance rather than replace traditional data science methods. You’ll find:

• Three chapters providing a solid grounding in the ideas, principles and technologies that are used for data science with language models
• Nine chapters that discuss specific patterns for integrating language models into data science workflows, including semantic vector analysis, few-shot prompting, retrieval-based applications, synthetic data generation and AI agent development
• Real-world case studies discussing applications like fraud detection, customer churn, translation, document classification and sentiment analysis, with concrete business applications
• Comprehensive evaluation methods and testing frameworks are discussed in the context of language model applications in enterprise environments
• Practical code examples and implementation guidance using popular tools like HuggingFace, OpenAI, Google Gemini, as well as more development frameworks like LangChain, and PydanticAI
• Strategic insights for balancing model accuracy, interpretability, and business requirements while avoiding common pitfalls in AI deployment

An authoritative resource for data scientists and software engineers interested in using modern AI tools to build data-driven applications, Data Science First is a strategy guide for professionals navigating the discipline of data science as it is disrupted by generative AI. Whether you're looking to improve existing workflows or develop entirely new AI-powered solutions, you’ll discover how to use language models in ways that consistently add value. JOHN HAWKINS is the Chief AI Officer at Intersect AI, an organization that builds bespoke AI solutions to solve real workplace problems for companies in industries like insurance, media and healthcare. He leads the company’s data science initiatives, working with clients directly to analyze their workflow processes and design people centred AI systems.