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shionguha/cosc3570-introdatascience-fa18
By shionguha
This repository contains code, data and other resources for COSC/MATH 3570: Introduction to Data Science at Marquette University for Fall 2018. The official description of the course is as follows: This course is a first introduction to the new and rapidly evolving field of data science. We will understand the philosophy and basic concepts of ...
This repository contains code, data and other resources for COSC/MATH 3570: Introduction to Data Science at Marquette University for Fall 2018. The official description of the course is as follows: This course is a first introduction to the new and rapidly evolving field of data science. We will understand the philosophy and basic concepts of doing data science through an active learning process. The student will learn how to use popular programming and data analysis platforms to clean, organize and analyze data from a modern machine learning perspective. Students are expected to read and practice specific lessons before coming to class. In class emphasis will be placed on practical interpretation, inference, visualization and navigating biases.
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This repository contains code, data and other resources for COSC/MATH 3570: Introduction to Data Science at Marquette University for Fall 2018. The official description of the course is as follows: This course is a first introduction to the new and rapidly evolving field of data science. We will understand the philosophy and basic concepts of ...
This repository contains code, data and other resources for COSC/MATH 3570: Introduction to Data Science at Marquette University for Fall 2018. The official description of the course is as follows: This course is a first introduction to the new and rapidly evolving field of data science. We will understand the philosophy and basic concepts of doing data science through an active learning process. The student will learn how to use popular programming and data analysis platforms to clean, organize and analyze data from a modern machine learning perspective. Students are expected to read and practice specific lessons before coming to class. In class emphasis will be placed on practical interpretation, inference, visualization and navigating biases.
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