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MA5800 - Foundations for Data Science

Credit points: 3
Year: 2019
Student Contribution Band: Band 2
Administered by: College of Science and Engineering

This subject will provide students with an overview of data science as a discipline as well as an introduction to a number of topics that play fundamental roles across various subjects in this area. Students will learn different forms of representing and pre-processing data for further analysis and visualisation. They will also learn principles of algorithm analysis that will allow them to assess and compare the scalability of different algorithms to be studied across other subjects in the realm of data science. Core elements of this subject include: An Introduction to Data Science and Big Data; Data Types and Representation; Essentials on Data Visualisation of Tabular Data; Data Pre-Processing; Data Wrangling and Tidying; Algorithm Analysis; Case Studies; Software Practice (R).

Learning Outcomes

  • explain what data science is about and the areas that play major roles within the realm of data science;
  • explain and exemplify the most common forms of data types and representations;
  • identify and describe at a conceptual level a core collection of simple yet powerful techniques for data visualisation in the realm of data science;
  • conceptually describe and apply a core collection of elementary techniques for data pre-processing;
  • interpret and explain, at a conceptual level, results of algorithm analyses;
  • apply common data representation and data pre-processing techniques, such as wrangling and tidying, using the software package and language R.

Availabilities

Townsville, Internal, Study Period 83
Census Date 16-May-2019
Coord/Lect: Mr Sourav Das.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 39 hours tutorials
  • assessment and self-directed study
Assessment: quizzes or tests (20%); assignments (60%); computational laboratories/log book (20%).

Cairns, Internal, Study Period 83
Census Date 16-May-2019
Coord/Lect: Mr Sourav Das.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 39 hours tutorials
  • assessment and self-directed study
Assessment: quizzes or tests (20%); assignments (60%); computational laboratories/log book (20%).

JCU Online, External, Study Period 83
Census Date 16-May-2019
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 65 hours - Online resources including readings, screencasts, embedded quizzing
  • assessment and self-directed study
Method of Delivery: Online - JCU
Assessment: quizzes or tests (20%); assignments (60%); computational laboratories/log book (20%).

Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.