Classification Range

Typically, the work should meet these requirements:

First Class (>=70%)

Outstanding/excellent work with correct codes and results. An outstanding work should demonstrate coding proficiency with high efficiency and based on advanced techniques. Evidence of independent research into methods used and a thorough justification of applications of these methods.

Upper Second (60-69%)

Good work with few mistakes. Some minor tasks have not been carried out or are not completely correct. Coding with  good  efficiency.  Evidence  of  good knowledge of the core concepts, with good explanations and justifications.

Lower Second (50-59%)

Demonstrates knowledge of core concepts but with some mistakes. Explanations and justifications of methods used are logical but limited in depth. Coding with average efficiency. Most tasks have been carried out with sufficient accuracy.

Third (40-49%)

Some parts of the assignment are missing and/or have partially correct results. Most tasks have not been carried out with sufficient accuracy. Results may not be correct or technically sound. Mistakes in application of knowledge and shows some misunderstandings. Explanations and justifications of methods used are not clear or logical. Coding might be inefficient.

Pass (35-39%)

Some significant part of the assignment is missing and/or has partially correct results. Gaps in knowledge and many mistakes, little evidence of understanding. Methods used are not well explained or justified. Coding is notably inefficient.

Fail (0-34%)

Many aspects of the assignment are missing, or there are large gaps in knowledge and significant mistakes, also showing limited understanding. Lack of logical explanations behind the methods used.

Task 1 – Data Preprocessing, Exploratory Data Analysis, and Python Classes

Using the cardata.csvfile within the CS2PP22_Assessment_Task1.ipynbJupyter notebook, you will execute several components of the data science process and design and implement a class structure that controls and compiles data about a fictional sporting event by writing Python code to perform the outlined sub-tasks detailed in the notebook. Working through this notebook, you will read, write, and manipulate data to extract specific features, design and implement functional routines, and design and implement an algorithm to select an optimal subset from a larger dataset.

Some sub-tasks will ask you to provide a written explanation of the justification behind it your coding choices. Code and written responses should be presented in a set of well-formatted code and Markdown cells at appropriate points in your Jupyter notebook. This work will require the production and submission of additional files; details about these files and how they should be submitted are provided in the notebook and the Assignment Submission Requirements.

Task 2 – Twitter Data Analysis

Using the CS2PP22_Assessment_Task2.ipynb Jupyter notebook, you will extract data from the social media platform, Twitter, and use the data as the basis for implementing components of the data science process to build and test a regression model. You will need to extract at least 300 tweets (perhaps, the 300 most recent tweets) from at least 3 Twitter accounts.

Visualise the results concisely and discuss the reasons why one might prefer the use of one of your tested methods over another. As in Task 1, written responses should be provided in