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CSC2410 Computational Thinking with Python

Semester 2, 2023 Online
Units : 1
School or Department : School of Mathematics, Physics & Computing
Grading basis : Graded
Course fee schedule : /current-students/administration/fees/fee-schedules

Staffing

Course Coordinator:

Overview

Computational thinking is a core skill across many cross disciplinary fields. Future professionals in management roles as well as data analysts need to understand fundamental computational approaches to problem solving. The topics in this course are intended to introduce students not merely to the coding of computer programs, but algorithmic thinking, data management, the methodology of computer programming, and the principles of good program design including modularity, encapsulation and abstraction. The Python language is used because of its extensive application libraries and its effectiveness and popularity as a modern programming language.

This course covers fundamental computational problem solving concepts, tools and methodologies. Students will learn how to select an appropriate data type and apply the most appropriate technical processes for a given computational problem. They will also learn how to develop modular code which conforms to the basic principles and practices of software engineering.

Course learning outcomes

On successful completion of this course students should be able to:

  1. Effectively conduct program designs including modularity, encapsulation and abstraction;
  2. Differentiate between available data types and demonstrate their appropriate problem application;
  3. Apply available libraries to solve problems.

Topics

Description Weighting(%)
1. Overview of Python syntax, semantics and control structures 10.00
2. Functions 20.00
3. Programming with objects 5.00
4. Data structures 25.00
5. File I/O 10.00
6. Libraries 15.00
7. Principles and practices of software engineering 10.00
8. Ethics of software engineering 5.00

Text and materials required to be purchased or accessed

Downey, Allen B 2017, Modeling and Simulation in Python, Green Tea Press.
(Download from: Permission is granted to copy, distribute, transmit and adapt this work under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.)

Student workload expectations

To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.

Assessment details

Approach Type Description Group
Assessment
Weighting (%) Course learning outcomes
Assignments Practical Tech and/or scntific artefact 1 No 20 1,2,3
Assignments Practical Tech and/or scntific artefact 2 No 30 1,2,3
Examinations Non-invigilated Time limited online examinatn No 50 1,2
Date printed 9 February 2024