The University of Texas at Arlington is offering a free online course on Feature Engineering for Improving Learning Environments. This course is intended for those who have a bachelor’s degree and are interested in developing learning and data science skills for employment in education, corporate, nonprofit, and military sectors.
This course focuses on developing better features to create better models.
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( votes)Course At A Glance
Length: 3 weeks
Effort: 5-7 hours pw
Subject: Data Analysis & Statistics
Institution: University of Texas at Arlington and edx
Languages: English
Price: Free
Certificate Available: Yes, Add a Verified Certificate for $99
Session: Closed
Providers’ Details
The University of Texas at Arlington is one of the nation’s most dynamic centers of higher learning, setting the standard for educational excellence in the thriving North Texas region it calls home. An academic centrepiece in the heart of the Dallas-Fort worth Metroplex for nearly 120 years, UT Arlington was founded in 1895 as a private liberal arts institution.
About This Course
How can data-intensive research methods be used to create more equitable and effective learning environments? In this course, you will learn how data from digital learning environments and administrative data systems can be used to help better understand relevant learning environments, identify students in need of support, and assess changes made to learning environments.
Why Take This Course?
This course pays particular attention to the ways in which researchers and data scientists can transform raw data into features (i.e., variables or predictors) used in various machine learning algorithms.
Learning Outcomes
- How to transform and visualize data using R
- How to apply selected machine learning algorithms (e.g., logistic regression and decision trees) to regression and classification tasks in R
- Strategies for applying data-intensive research workflows for feature engineering and model building
Instructors
Andrew E. Krumm
Andrew is a Senior Education Researcher and Director of the Improvement Analytics group at SRI International. He received his Ph.D. in learning technologies from the University of Michigan in 2012. Andrew’s research focuses on applying data-intensive research techniques within research-practice partnerships.
Requirements
University highly recommends that you take the previous course in this series before beginning this course:
Predictive Modeling in Learning Analytics
This course is intended for those who have a bachelor’s degree and are interested in developing learning and data science skills for employment in education, corporate, nonprofit, and military sectors. Experience with programming and statistics will be beneficial to participants.
How To Join This Course
- Go to the course website link
- Create an edX account to SignUp
- Choose “Register Now” to get started.
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