COMP SCI 7210 - Foundations of Computer Science - Python A
North Terrace Campus - Semester 1 - 2021
General Course Information
Course Code COMP SCI 7210 Course Foundations of Computer Science - Python A Coordinating Unit School of Computer Science Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Course Description This course will develop your coding and problem-solving skills with a focus on data and data science. You will learn algorithm design as well as fundamental programming concepts such as data, selection, iteration and functional decomposition, data abstraction and organisation. You will build fundamental software development skills including the use of the Python programming language and tools, debugging, testing and fundamentals of good programming practice, style and design.
Course Coordinator: Dr Rita Garcia
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesUpon completion of this course/subject, students will be able to:
1.Use the Python programming language to construct basic programs
2.Evaluate real world data using Python tools
3.Translate real world data to computer representation
4.Interpret the language of data science and programming
5.Manipulate data using Python tools to create visual data representations
6.Express the language of data science and programming.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1-5 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
Required ResourcesZhang, Y. (2015). An Introduction to Python and Computer Programming(1st ed. 2015. ed., Lecture Notes in Electrical Engineering, 353).Lee, K., & Mackie, I. (2014).
Python Programming Fundamentals(2nd ed. 2014 ed., Undergraduate Topics in Computer Science). London: Springer London.Jake VanderPlas. (2016).
Python Data Science Handbook: Essential Tools for Working with Data(1st ed.). O'Reilly Media, Inc.
Nelli, F., (2018), Python Data Analytics With Pandas, NumPy, and Matplotlib (Links to an external site.), (2nd ed.), Springer, New York.
Texts other than the "Python Data Science Handbook" are available to students as e-books through the Library. The Data Science Handbook is available through the library on a limited (short term loan) basis as an e-book or a personal copy can be purchased.
Online LearningAll materials are available from MyUni and it is possible to work through most of the course activities off-site. Attendance can be negotiated with the course coordinator at the start of the course.
Learning & Teaching Activities
Learning & Teaching ModesThis course is structured around supported active learning labs. Assessment, discussions and support occur face to face. The materials are available online. Attendance at all sessions is not mandatory and can be negotiated with course coordinator during the first week.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.This course assumes a commitment of 20-25 hours per week over 6 weeks.
Learning Activities Summaryeach week of the six weeks, learning activities follow the pattern:
Lessons, practice (in class or in own time) and text readings
Face to Face discussion session and Peer sharing
Further lessons, practice (in class or in own time) and text readings
Research and Reflection (progress report related to project)
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment SummaryWeekly programming practicals: 30%
Weekly concept quizzes: 20%
Project: 50% (project is a hurdle requirement and students must receive a 50P or higher on the project to pass the course)
Late work attracts a 25% mark penalty for each day late.
Assessment DetailAssessment 1 - A (practical assignments)
In this assessment, you will be required to demonstrate your ability to apply what you have learnt each week in the creation of programs to solve a problem.
Due date: Sunday 11:59pm end of each week.
Percentage of grade: 30%
Assessment 1 - A (online quizzes)
In this assessment, you will be required to demonstrate your knowledge of the concepts, structure, and application of the code you used in your practical work.
Due date: Start of week Tuesday 11:59pm.
Percentage of grade: 20%
Assessment 2 - (project)
In this assessment, you will be required to identify a data set to work with (either your own or one of the recommended data sets) and build a Python program to extract and visualise information about the data set. The purpose of this assessment is for you to demonstrate your ability to apply what you have learned throughout the course in the creation of a document including programs to answer questions about data and a video explaining your work.
Due date: Sunday 11:59 pm end of week 6
Percentage of grade: 50%
SubmissionAll submissions must be made through MyUni. Follow the assessment links for each assessment item.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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