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CP1407 - Introductory Machine Learning and Data Science

Credit points: 3
Year: 2023
Student Contribution Band: Band 2
Administered by: College of Science and Engineering

Data Science is the study of the generalizable extraction of knowledge from data. Being a data scientist requires an integrated skill set spanning mathematics, statistics, machine learning, databases and other branches of computer science along with a good understanding of the craft of problem formulation to engineer effective solutions. This subject will introduce students to this rapidly growing field and equip them with some of its basic principles and tools as well as its general mindset. Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, utilising various machine learning algorithms for predictive modeling and descriptive modeling, data product creation and evaluation

Learning Outcomes

  • Describe what data science is and the skill sets needed to be a data scientist;
  • Describe the data science process and how its components interact;
  • Explain in basic terms what Machine Learning means and the significance of Machine Learning in data science;
  • Identify differences in various machine learning algorithms, principles and application purposes of each algorithm;
  • Apply basic tools to carry out data analysis using exemplar machine learning algorithms.

Subject Assessment

  • Written > Examination (centrally administered) - (40%) - Individual
  • Assignment - (40%) - Individual
  • Performance/Practice/Product > Practical assessment/practical skills demonstration - (20%) - Individual.

Availabilities

Cairns, Trimester 2, Internal
Census Date 22-Jun-2023
Coord/Lect: Dr Euijoon Ahn.
Workload expectations:

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

  • 20 hours seminars
  • 10 hours online activity
  • 20 hours specialised
  • assessment and self-directed study

JCU Brisbane, Trimester 2, Internal
Census Date 22-Jun-2023
Coord/Lect: Dr Iti Chaturvedi.
Workload expectations:

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

  • 20 hours seminars
  • 10 hours online activity
  • 20 hours specialised
  • assessment and self-directed study

JCU Singapore, Study Period 52, Internal
Census Date 03-Aug-2023
Workload expectations:

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

  • 20 hours seminars
  • 10 hours online activity
  • 20 hours specialised
  • assessment and self-directed study

Townsville, Trimester 2, Internal
Census Date 22-Jun-2023
Coordinator: Dr Euijoon Ahn
Lecturer: Dr Iti Chaturvedi.
Workload expectations:

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

  • 20 hours seminars
  • 10 hours online activity
  • 20 hours specialised
  • assessment and self-directed study

Trimester 2, External
Census Date 22-Jun-2023
Coord/Lect: Dr Euijoon Ahn.
Workload expectations:

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

  • 40 hours online activity
  • 10 hours online Seminars
  • assessment and self-directed study

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.