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LO1 Critically apply fundamental concepts and techniques in data science

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LO1 Critically apply fundamental concepts and techniques in data science

Learning Outcomes On successful completion of this component a student will have demonstrated competence in the following areas:

  • · LO1 Critically apply fundamental concepts and techniques in data science
  • · LO2 Utilize state-of-the-art tools to design data science applications for various types of data
  • · LO3 Analyze and interpret large datasets and deliver appropriate reports on them
 

Task Overview: The objective of this assignment is to analyse a dataset concerning bike rentals. The dataset can be downloaded from Blackboard. It is based on the real data from Capital Bikeshare Company that maintains a bike rental network in Washington DC. The dataset has one row for each hour of each day in 2011 and 2012, for a total of 17,379 rows. It contains features of the day (workday, holiday) as well as weather parameters such as temperature and humidity. The range of hourly bike rentals is from 1 to 977. The bike usage is stored in the field ‘cnt’. Our task is to develop a prediction model for the number of bike rentals such that Capital Bikeshare can predict the bike usage in advance.

You need to write a report that discusses how you complete the task, and go into sufficient depth to demonstrate knowledge and critical understanding of the relevant processes involved. 100% of available marks are through the completion of the written report, with clear and separate marking criteria for each required report section. Notably, a distinct and significant report section on discussing and critiquing the analysis and implementation processes you carried out for your data solution is required.

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  • Title: LO1 Critically apply fundamental concepts and techniques in data science
  • Price: £ 119
  • Post Date: Fri, 03 Jan 2025 18:41:29 +0000
  • Category: Assignment
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LO1 Critically apply fundamental concepts and techniques in data science LO1 Critically apply fundamental concepts and techniques in data science
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