#140 - The Power of 3 (Math Nerds, Professors, and Authors) w/ Hala Nelson
Aug 21, 2023
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Hala Nelson, math nerd, professor, and author, discusses her journey from abstract algebra to real-life applications of math. The speakers talk about their motivation to write a book bridging the gap between math and AI. They give advice for pursuing a career in AI and large language models, emphasizing the importance of interdisciplinary education. They also discuss their new book and its practical applications for AI projects. The chapter on bridging the gap between data engineering and data analysis is highlighted. The importance of feedback and reviewers in the book writing process is emphasized. The hosts discuss the beginnings of their podcast and the value of meaningful conversations.
Understanding the entire data science stack is crucial, bridging the gap between hardware, infrastructure, databases, machine learning, AI, and business value.
Interdisciplinary education in data science is key, integrating domain-specific expertise and collaborating with industry professionals.
Focus on understanding the principles and concepts of data science, rather than getting caught up in specific tools or technologies.
Deep dives
The importance of understanding the whole data science stack
In this podcast episode, Hala Nelson, a math professor at James Madison University, discusses the importance of understanding the entire data science stack. She emphasizes the need to bridge the gap between hardware and infrastructure, databases and data engineering, machine learning and AI models, and the business value of data science. Hala shares her own experiences working on real-world projects with the fire department, transportation department, and military, and how these experiences shaped her approach to teaching data science principles. She also highlights the need for simplicity and relevance in explaining complex concepts to learners. Hala discusses her upcoming book, which aims to provide a comprehensive understanding of the data science stack and its practical applications.
The value of interdisciplinary education in data science
Hala Nelson discusses the importance of interdisciplinary education in data science. She explains how the fields of academia and industry have diverged, making it crucial for data science students to acquire knowledge from various departments like computer science, math, and statistics. Hala emphasizes the value of integrating domain-specific expertise in data science projects and engaging with industry professionals to understand real-world problems and goals. She also highlights the need for ongoing conversations and collaboration between academia and the tech industry to bridge the gap between theoretical knowledge and practical skills. Hala encourages students to curate their education by seeking knowledge from different fields and understanding how they intersect in the data science field.
Writing a comprehensive and practical data science book
Hala Nelson shares her motivation for writing a new data science book. She explains that her goal is to provide a comprehensive understanding of the entire data science stack, from hardware and infrastructure to databases, data engineering, machine learning, and AI models, and the business value of data science. Hala emphasizes the importance of simplicity and clarity in explaining complex concepts, ensuring that the book resonates with readers and helps them think critically about data science problems. She also discusses the iterative writing process, involving reviewers early on to receive feedback and improve the content. Hala aims to create a book that lasts, providing readers with a foundation in data science principles and techniques that they can use for a long time.
Applying principles over specific tools in data science
Hala Nelson encourages learners to focus on understanding the principles and concepts in data science, rather than getting caught up in specific tools or technologies. She emphasizes the importance of thinking critically about the functionalities and business value of different data science approaches. Hala discusses how data science models aim to learn deterministic functions or probability distributions, and that the differences between various models lie in assumptions, optimization techniques, or function structures. She encourages learners to see beyond the labels and focus on the functionality and practical application of different models. Hala highlights that understanding these principles can provide a strong foundation and the ability to adapt to changing technologies and tools.
The benefits of simplicity and relevance in teaching data science
Hala Nelson shares her approach to teaching data science and emphasizes the importance of simplicity and relevance in education. She believes that math and AI can be made more approachable by focusing on the broader picture and key concepts, rather than getting lost in technical details. Hala describes her goal of helping students connect the dots between different domains and understanding how math, AI, and data science apply to real-world problems. She aims to help students gain confidence in their analytical and problem-solving abilities through a clear, concise, and practical teaching approach. Hala highlights the value of understanding the business value and context of data science projects to create meaningful and impactful solutions.
Hala Nelson joins the show to chat writing books, teaching math, and much more. It's not often we get three math nerds, professors, and authors in the same conversation, and this is a lot of fun. Enjoy!
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