• Weekly coding tasks: Tasks where the student must write a Python program to produce precisely described output. The program will be run automatically against test cases. Grade is based on participation. These are started in tutorial, and completed in students’ own time. Late work is not accepted for these assessments. When special consideration is approved for a task, the appropriate consideration should be “mark adjustment” based on estimating a grade using the average from other tasks or the final exam.
• Weekly quizzes: Each quiz consists of multiple-choice questions related to the lecture, lab, or tutorial content from the previous week. Done in students own time. Late work is not accepted for these assessments. When special consideration is approved for a task, the appropriate consideration should be “mark adjustment” based on estimating a grade using the average from other tasks or the final exam.
• Practice Python coding test A: Done in student’s own time. There are 5 separate tasks; for each, the student must extend a Python code skeleton so that it calculates precisely described output from data in a file. Each task is graded automatically, by comparing the output produced on several different inputs to what is described. This carries no weight in final grade, but is intended to accustom students to the setting in preparation for the later coding test, and to trigger remedial learning for any student who does not succeed in the practice. Late work is not accepted for this assessment. When special consideration is approved, the appropriate consideration should be “No action”.
• Practice Python coding test B: Done in student’s own time. The student must produce Python code that calculates precisely described output from data in a file. This assessment is graded automatically, by comparing the output produced on several different inputs to what is described. This carries no weight in final grade, but is intended to accustom students to the setting in preparation for the later coding test, and to trigger remedial learning for any student who does not succeed in the practice. Late work is not accepted for this assessment. When special consideration is approved, the appropriate consideration should be “No action”.
• Python coding test A: Held during a scheduled lecture timeslot, or an alternative scheuled timeslot (for cases of special consideration, or where the lecture timeslot is unsuitable). There are 5 separate tasks; for each, the student must extend a Python code skeleton so that it calculates precisely described output from data in a file. Each task is graded automatically, by comparing the output produced on several different inputs to what is described. Late work is not accepted for this assessment. When special consideration is approved, the appropriate consideration should be “New or varied assessment”.
• Python coding test B: Held during a scheduled lecture timeslot, or an alternative scheuled timeslot (for cases of special consideration, or where the lecture timeslot is unsuitable). The student must produce Python code that calculates precisely described output from data in a file. This assessment is graded automatically, by comparing the output produced on several different inputs to what is described. Late work is not accepted for this assessment. When special consideration is approved, the appropriate consideration should be “New or varied assessment”.
• Project Stage 1: This is the first part of a group project (the students in a group must all be attending the same scheduled lab session). This stage involves finding data from a domain of interest for the students, data cleaning and importing to a tool, and doing a very simple analysis from some of the data. A report is required that describes the dataset (including the metadata associated to it), how it was obtained, and how it was processed by the tool. If this stage is missed or badly done, the group can be given a clean data set, for a domain chosen by the instructor, to use in the rest of the project. It is crucial that each group manages its internal working effectively, and use mechanisms to detect problems and report them to the coordinator early. When special consideration is approved, the appropriate consideration should be “extension of time”.
• Project Stage 2: The group will use computational tools to explore the data, and report on both what was done and what was found (using appropriate summaries and charts). It is crucial that each group manages its internal working effectively, and use mechanisms to detect problems and report them to the coordinator early. When special consideration is approved, the appropriate consideration should be “extension of time”.
• Project Stage 3: The group will use computational tools to produce a predictive model for some aspect of the data, and evaluate this model; deliverable is a report on both what was done and what was found. It is crucial that each group manages its internal working effectively, and use mechanisms to detect problems and report them to the coordinator early. When special consideration is approved, the appropriate consideration should be “extension of time”.
• Exam: An online exam, with multiple-choice, short-answer, longer (eg half-page) discussions. Covering knowlede, conceptual content, skills, and experiences. Late work is not accepted for this assessment. When special consideration is approved, the appropriate action should be “replacement exam”.

Detailed information for each assessment can be found on Canvas.

Minimum requirement: It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

At the completion of this unit, you should be able to: