Overview: 

As data science and statistics become increasingly embedded in scientific research and industry, the role of computing is evolving. Today, data scientists must go beyond traditional statistical software and spreadsheets—they need to build data science products that power real-time predictions, business decisions, and analytical tools. This short course, led by Alex Reinhart, will provide an introduction to essential software engineering skills for data scientists and statisticians. Participants will explore best practices for writing code that is reliable, scalable, and maintainable—whether for research applications, industry use cases, or product development. Attendees will gain practical insights into building robust data science solutions, making their work more efficient and impactful. Whether you're an academic researcher or an industry professional, this course will equip you with the computing skills necessary to take your data science work from code to product.

Registration Fees:

NISS Affiliates: Contact Megan Glenn at mglenn@niss.org for the promo code to use to activate your free affiliate ticket for this event!

General Admission: $35.00 USD

Instructor: 

Alex Reinhart, Associate Teaching Professor in Statistics & Data Science at Carnegie Mellon University

Abstract:

Short Course title: From Code to Products - Software Engineering for Data Science

Statisticians and data scientists increasingly rely on computing. We use computers to analyze data, but as data science and statistics are embedded into more areas in science and industry, the kinds of computing we need are changing. Now, beyond spreadsheets and statistical software, data scientists need to know how to build data science products: software to deliver predictions, recommendations, or business decisions, often in real time. Instead of writing code to implement an analysis and generate results for a report, we write code that becomes part of products, data pipelines, or analysis tools used by others. In this short course, we'll discuss the computing skills statisticians need, both in industry and in academic research. Topics will include software design and organization, testing, version control, automation, and other software engineering tools, including examples in R and Python. 

About the Instructor

Alex Reinhart is an Assistant Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University. His teaching and research focus on statistical methods, data science, and statistical applications in real-world settings. He is particularly interested in statistical inference, uncertainty quantification, and improving the accessibility and reproducibility of statistical research. Reinhart holds a Ph.D. in Statistics from Carnegie Mellon University, where his research explored statistical techniques for uncertainty quantification in nuclear detection. He is also the author of Statistics Done Wrong: The Woefully Complete Guide, a book that highlights common statistical misconceptions and pitfalls in scientific research. In addition to his teaching and research, Reinhart is committed to statistical education and mentorship, guiding students in applying rigorous statistical methods to practical problems. His work bridges the gap between theoretical statistics and applied problem-solving, contributing to advancements in data science and statistical practice.

About the Moderator

Coming Soon!

About the NISS New Researchers Network

The National Institute of Statistical Sciences (NISS) is a national organization that works on issues related to information and quantitative analysis. The goal of the NISS New Researchers Network is to create connections among undergraduate and graduate students from different academic institutions and early-career professionals within the NISS Affiliates Program. The NISS New Researchers Network fosters collaboration and support among early-career statisticians, graduate students and undergraduate students offering resources and opportunities for professional growth.