University of Technology Sydney

42047 Data Processing Using Python

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2022 is available in the Archives.

UTS: Information Technology: Computer Science
Credit points: 3 cp

Subject level:

Postgraduate

Result type: Grade and marks

There are course requisites for this subject. See access conditions.

Description

The subject focuses on the basics of python programming with practical application to data processing and analysis. Students will learn basic programming concepts, simple visualization, object-oriented programming techniques, and how to write custom programs using iPython notebooks. Additionally, this subject introduces the usage of the Numpy package to pre-process data, and machine learning techniques will be introduced to facilitate further exploration of the Python language capabilities.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Write custom programs using the Python language for data analysis.
2. Source data from multiple sources and manipulate data for analysis and visualisation.
3. Apply statistical tests and data visualisation techniques to analyse data and interpret the results.

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

  • Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
  • Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)

Teaching and learning strategies

This subject will be delivered in 4 intensive collaborative sessions with a focus on hands-on tutorial approaches, designed to both learn about and immediately practice techniques for programming for data analysis. Within the interactive sessions, there will be several opportunities for testing the student’s ability to apply the new skills to a choice from a set of specified basic problems. These problems will help to provide students with initial low-stakes feedback on their progress within the class in early weeks, but will also form the basis for the quiz assessments encountered after each of the intensive sessions.

The face-to-face sessions will be supported by several online collaborative sessions, focused on both solving remaining technical problems, discussing the design and efficiency of chosen program designs, and maintaining peer-to-peer collaboration within the cohort.

Finally, students will describe the techniques they use and how they inter-relate as they move forward to apply the skills to a more open project, where they will analyse an existing dataset, to solve a defined business analytics problem.

Content (topics)

  • Introduction to programming in python (Installation and iPython notebooks)
  • Interacting with Python
  • Comparison operators and Python statements
  • Dictionaries in Python
  • Methods and functions
  • Introduction to data visualization and libraries
  • Object Oriented programming
  • Exception handling
  • Reading data from Numpy arrays
  • Reading data from multiple sources
  • Data visualisation with matplotlib
  • Applying linear regression to data
  • 3D visualisation and introduction to machine learning

Assessment

Assessment task 1: Quiz/Test

Intent:

To ensure that the student has a firm understanding of python programming basics. This will facilitate the learning of advanced topics.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2 and 3

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1 and D.1

Type: Quiz/test
Groupwork: Individual
Weight: 30%

Assessment task 2: Assignment

Intent:

To ensure the student has a firm understanding of the Python programming syntax and can design, implement and execute a data processing task independently.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2 and 3

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1 and D.1

Type: Laboratory/practical
Groupwork: Individual
Weight: 70%
Length:

1000 words, alongside a code project that implements the analysis and visualisation.

Minimum requirements

To pass this subject, students must achieve an overall mark of 50% or greater.

Required texts

Please check Canvas for more details.

Recommended texts

Please check Canvas for more details.

References

Please check Canvas for more details.