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James Cook University Subject Handbook - 2022

For subject information from 2025 and onwards, please visit the new JCU Course and Subject Handbook website.

CP1407 - Introductory Machine Learning and Data Science

Credit points:03
Year:2022
Student Contribution Band:Band 2
Administered by:College of Science and Engineering (pre 2023 PCS)

Subject Description

    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

Note that 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.

Availabilities

Cairns, Trimester 2, Internal

Census date:Thursday, 23 Jun 2022
Study Period Dates:Monday, 30 May 2022 to Saturday, 27 Aug 2022
Coordinator(s):
DR Iti Chaturvedi
Lecturer(s):
DR Euijoon Ahn
Workload expectations:The student workload for this 3 credit point subject is approximately 130 hours.
  • 50 Hours - Other - Combined Lectures; Practicals; Lecturer directed activities

JCU Brisbane, Trimester 1, Internal

Usually available in even-numbered years.

Census date:Thursday, 10 Mar 2022
Study Period Dates:Monday, 14 Feb 2022 to Saturday, 14 May 2022
Coordinator(s):
DR Iti Chaturvedi
Lecturer(s):
DR Iti Chaturvedi
Workload expectations:The student workload for this 3 credit point subject is approximately 130 hours.
  • 50 Hours - Other - Combined Lectures; Practicals; Lecturer directed activities

JCU Singapore, Study Period 52, Internal

Census date:Thursday, 04 Aug 2022
Study Period Dates:Monday, 11 Jul 2022 to Friday, 14 Oct 2022
Coordinator(s):
DR Iti Chaturvedi
Lecturer(s):
DR Randy Zhu
Workload expectations:The student workload for this 3 credit point subject is approximately 130 hours.
  • 50 Hours - Other - Combined Lectures; Practicals; Lecturer directed activities

Townsville, Trimester 2, Internal

Census date:Thursday, 23 Jun 2022
Study Period Dates:Monday, 30 May 2022 to Saturday, 27 Aug 2022
Coordinator(s):
DR Iti Chaturvedi
Lecturer(s):
DR Iti Chaturvedi
Workload expectations:The student workload for this 3 credit point subject is approximately 130 hours.
  • 50 Hours - Other - Combined Lectures; Practicals; Lecturer directed activities

Trimester 2, External

Census date:Thursday, 23 Jun 2022
Study Period Dates:Monday, 30 May 2022 to Saturday, 27 Aug 2022
Coordinator(s):
DR Iti Chaturvedi
Lecturer(s):
DR Iti Chaturvedi
Workload expectations:The student workload for this 3 credit point subject is approximately 130 hours.
  • 50 Hours - Other - Combined Lectures; Practicals; Lecturer directed activities