Difference between revisions of "Data Science"

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(Created page with "{{Incomplete}} {{EventLinksBox |active=yes |type=Inquiry |cat=Lab |2020thread=[https://scioly.org/forums/viewtopic.php?f=285&t=15673 2020] }} '''Data Science''' is a Divisio...")
 
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'''Data Science''' is a [[Division C]] trial event held in [[Texas]] for the [[2020]] season. It is centered around statistics and programming knowledge.
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'''Data Science''' is a [[Division C]] trial event held in [[Texas]] for the [[2020]] season, founded by [[User:dkarkada]], a member of [[ATX Science Olympiad]]. It is centered around statistics and programming knowledge.
 
==The Event==
 
==The Event==
 
The event takes the form of a written test portion and a coding challenge portion. Competitors must bring writing utensils, but computers will be provided. The written test will cover Python syntax and statistics, as well as feature extraction. The coding challenge portion will take the form of a Jupyter Notebook with pre-existing code.  
 
The event takes the form of a written test portion and a coding challenge portion. Competitors must bring writing utensils, but computers will be provided. The written test will cover Python syntax and statistics, as well as feature extraction. The coding challenge portion will take the form of a Jupyter Notebook with pre-existing code.  

Revision as of 04:18, 27 September 2019

Data Science
This event is an event held in the current season.

Type Inquiry
Category Lab
Forum Threads

Data Science is a Division C trial event held in Texas for the 2020 season, founded by User:dkarkada, a member of ATX Science Olympiad. It is centered around statistics and programming knowledge.

The Event

The event takes the form of a written test portion and a coding challenge portion. Competitors must bring writing utensils, but computers will be provided. The written test will cover Python syntax and statistics, as well as feature extraction. The coding challenge portion will take the form of a Jupyter Notebook with pre-existing code.

Python Syntax

Statistics

Feature Extraction

Using Jupyter

Scoring

The written test is weighted more heavily, being 60% of the final score. The coding challenges are 40% of the final score, and will be graded based on both correctness and using organized code.