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

I highly recommend this book as a general introduction to data science for institutional researchers and assessment folk. The perspective was down-to-earth and humorous, at the same time that the information was very useful. The sample projects are accompanied by narrative about realistic institutional research scenarios that helps you to understand the analysis. I am going to be recommending this book to my IR colleagues who are interested in the topic. The explanations were accessible enough that both my undergraduate research assistant and I were able to work through the sample problems with the datasets provided online. There were a few times when I got stuck and had to slog through things---usually because I am not an expert coder by any means---but this is to be expected in my experience, and there weren't any cases where I couldn't figure out in the end what I was doing wrong. I feel like now I have some good ideas and hands-on experience and could move on to similar projects with my college's data.In addition to the technical information, I also found a lot to agree with in Lawson's analysis of the higher education data landscape. I did find the last chapter a little overly specific to policy issues of California community colleges and accreditation, topics which are not relevant to me in my current position. One thing that the book could benefit from is some editorial attention; although well written in terms of language and style, there were a few rather distracting typos and similar errors that some may find objectionable (I was able to look past them, since I am rather ADHD myself and prone to the same sort of thing).

I am an institutional researcher and I accidentally bought this book, not knowing that it was written for institutional researchers! To the value of my 'knowledge investment', this has been one of the best books written for IR. The writing itself is a stellar example of how we IR professionals ought to first asked data-driven questions, followed by dissemination of the knowledge discovered. While reading the book, I felt that I was communicating with the author. The case studies that he sited were very pragmatic and appropriate! I highly recommend it to all of my IR colleagues, with or without prior knowledge of data science!

This text brings about a new viewpoint and strategic efforts to utilize data to drive effective decision making.