Big Data in Education
- Offered byedX
Big Data in Education at edX Overview
Big Data in Education
at edX
Learn how and when to use key methods for educational data mining and learning analytics on large-scale educational data.
Duration | 9 hours |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Beginner |
Credential | Certificate |
Big Data in Education at edX Highlights
Big Data in Education
at edX
- Accredited Web Development Specialization
- Learn from eminent experts
- Career Building Sessions
- Self paced Course
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Big Data in Education at edX Course details
Big Data in Education
at edX
Skills you will learn
What are the course deliverables?
- Key methods for educational data mining
- How to apply methods using standard tools such as RapidMiner
- How to use methods to answer practical educational questions
- Add a verified certificate for INR 2128
More about this course
- Education is increasingly occurring online or in educational software, resulting in an explosion of data that can be used to improve educational effectiveness and support basic research on learning.In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will learn about the methods being developed by researchers in the educational data mining, learning analytics, learning at scale, student modeling, and artificial intelligence in education communities, as well as standard data mining methods frequently applied to educational data. You will learn how to apply these methods, and when to apply them, as well as their strengths and weaknesses for different applications.
Big Data in Education at edX Curriculum
Big Data in Education
at edX
Key methods for educational data mining
How to apply methods using Python's built-in machine learning library, scikit-learn
How to apply methods using standard tools such as RapidMiner
How to use methods to answer practical educational questions
Big Data in Education at edX Entry Requirements
Big Data in Education
at edX
Eligibility criteria
- Basic knowledge of statistics, data mining, mathematical modeling, or algorithms is recommended. Experience with programming is not required.
Conditional Offer
- Not mentioned
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