Courses for Science Olympiad

759 free courses & lectures available for Science Olympiad

Data science professional project 378x225

Microsoft Professional Capstone : Data Science

by Graeme Malcolm on Edx Edx

Duration: 14 hours

Solve a real-world data science problem in this capstone project for the Microsoft Professional Program in Data Science

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About Instructor(s):
Graeme Malcolm (Senior Content Developer),
Microsoft Learning Experiences

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Machine Learning for Data Science and Analytics

by Professor Ansaf Salleb-Aoui... on Edx Edx

Duration: 42.5 hours

Learn the principles of machine learning and the importance of algorithms.

Average ( 2 Reviews )

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About Instructor(s):
Professor Ansaf Salleb-Aouissi (Department of Computer Science),
Columbia University
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Programming for Data Science

by Dr. Katrina Falkner, Dr. Ni... on Edx Edx

Duration: 90 hours

Learn how to apply fundamental programming concepts, computational thinking and data analysis techniques to solve real-world data science problems.

Very Good ( 6 Reviews )

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About Instructor(s):
Dr. Katrina Falkner (Head - School of Computer Science),
University of Adelaide
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Improvement Science in Education

by Donald J. Peurach, Anthony ... on Edx Edx

Duration: 24 hours

Learn how to apply principles and practices of improvement science to improve educational practice, raise student performance, and reduce achievement gaps.

4.5

Very Good ( 1 Reviews )

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About Instructor(s):
Donald J. Peurach (Associate Professor of Educational Policy, Leadership, and Innovation in the School of Education),
University of Michigan
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Computer Science Essentials for Software Development

by Chris Murphy, Sampath Kanna... on Edx Edx

Duration:

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About Instructor(s):
Chris Murphy (Associate Professor of Practice, School of Engineering and Applied Science),
University of Pennsylvania
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Data Science: R Basics

by Rafael Irizarry on Edx Edx

Duration: 12 hours

In this first course of eight in the HarvardX Data Science series, we learn the basic building blocks of R. The series will cover data wrangling with dplyr, visualization with ggplot2, probability, inference, regression and machine learning.