Data Science in Higher Education: A Step-by-Step Introduction to Machine Learning for Institutional Researchers

by Jesse Lawson

Be the change your institution needs. What are leaders in research saying about Data Science in Higher Education ? "Where has this book been all these years? This is THE starting point for researchers looking for a leg up in today's college environment. Two parts discussion, one part methodology, and one part witty humor. I love it!" "Buy this book for your analysts. They and your college will thank you." "This is the only book on data science specific for higher education research that covers both theory and practice. I'm not a programmer at all, and I found this book very enjoyable. You wont regret it -- I know I don't!" "When our department was tasked with coming up with a predictive 'machine-learning' model, we hired Jesse to help us. His charisma and knowledge are unmatched, and this book only helps to breathe fresh life into issues in research today that are all too often swept under the rug." Discover the tools to take your institution to the next level! Data Science in higher education is the process of turning raw institutional data into actionable intelligence. With this introduction to foundational topics in machine learning and predictive analytics, ambitious leaders in research can develop and employ sophisticated predictive models to better inform their institution's decision-making process. You don't need an advanced degree in math or statistics to do data science. With the open-source statistical programming language R , you'll learn how to tackle real-life institutional data challenges (with actual institutional data!) by going step-by-step through different case studies. Topics "Forget the textbooks. This is a book on data science written for institutional researchers *by* an institutional researcher. You need this book." ------------------------------------------ Data Science is the art of carefully picking through that pile of book pages and putting together a complete book. It's the art of developing a narrative for your data, so that all the raw information that your institution warehouses and reports in bar charts and histograms is replaced with actionable intelligence. Here's what we So how do we do it? It starts with a solid perspective and great research tools. With Data Science in Higher Education you'll learn about and solve real-world institutional problems with open-source tools and machine learning research techniques. Using R, you'll tackle case studies from real colleges and develop predictive analytical solutions to problems that colleges and universities face to this day.

226 pages, Paperback

First published September 6, 2015

About the author

Bestselling science writer, Data Science in Higher Ed. Action thriller author & Austin Film Festival quarter finalist!

Reviews

[Tommy /|\ · Rating 2 out of 5]

Being an individual in the Institutional Research field over the last five years, this book was quite an interesting read. At least for the first two chapters. After that, Lawson delves into the snooze-worthy field of detailing regression formulas. Lawson spends an extremely large amount of pages discussing his own personal forays down one reporting rabbit hole or another. Institutional Research definitely has a large component in reporting, but even Lawson states in the first chapter that the reporting component tends to overwhelm the research component within the function of IR. Compounded the problematic position of lamenting about reporting, and then taking side-trips into that same area throughout the book - is that this tome is self-published. And Good Gods does it show. Spelling mistakes, glaring grammatical errors plague this book throughout and are extremely distracting, especially coming from an individual in a field where precision is a paramount perspective to have. This book lost a star and a half on that issue alone. My take? Spend your money elsewhere...