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

Sinopsis

Over 1,000 copies sold!

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."

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 include:

• 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

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?

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