Data Science in Higher Education is now officially in print, and I couldn’t be more relieved to finally get in print all the lessons learned over the years. My research for this book has led me to a very grim picture of higher education research and its role in institutional decision-making. With advancements in computational statistics technology and lower barriers to entry for all kinds of research tools, it is my hope that this monograph can start a much-needed discussion on the role of data science in institutional decision-making and the importance of an analytic mindset in higher education administration.
You can purchase it from Amazon! Click here: Data Science in Higher Education: A Step-by-Step Introduction to Machine Learning for Institutional Researchers
What are researchers and leaders 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.
- Simple, Multiple, & Logistic Regression Techniques, and Naive Bayes Classifiers
- Best Practices for Data Scientists in Higher Education
- Narrative-style stories, gotchas, and insights from actual data science jobs at colleges and universities
“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 know:
- Data science can and should be an integral part of college and university operations.
- Institutional effectiveness should be working side-by-side with faculty and educators to collect, clean, and mine through data of current and past students' behaviors in order to better empower counseling and advisement services (whether virtual or otherwise).
- Data itself should be considered an asset to an institution, and the data mining process a necessary function of institutional operations.
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.