Christopher Brooks

Christopher Brooks is a Research Assistant Professor in the School of Information and Director of Learning Analytics and Research in the Office of Digital Education & Innovation at the University of Michigan. His research focus is on the design of tools to better the teaching and learning experience in higher education, with a particular interest in understanding how learning analytics can be applied to human computer interaction through educational data mining, machine learning, and information visualization.

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Python Project: pillow, tesseract, and opencv (Coursera)

This course will walk you through a hands-on project suitable for a portfolio. You will be introduced to third-party APIs and will be shown how to manipulate images using the Python imaging library (pillow), how to apply optical character recognition to images to recognize text (tesseract and py-tesseract), and [...]
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Introduction to Data Science in Python (Coursera)

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and [...]
Average: 5 ( 3 votes )

Introduction to Machine Learning in Sports Analytics (Coursera)

In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. Building on the previous courses in the specialization, students will apply methods such as support vector [...]
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Applied Plotting, Charting & Data Representation in Python (Coursera)

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms [...]
Average: 2 ( 4 votes )

Predictive Modeling in Learning Analytics (edX)

Learn how predictive models in educational data mining and learning analytics are used to identify at-risk students. This course will introduce you to the tools and techniques of predictive models as used by researchers in the fields of learning analytics and educational data mining.
Average: 4 ( 1 vote )