Fundamentals of Data Science

Unit code: NIT2151 | Study level: Undergraduate
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Footscray Park
NIT1102 - Introduction to Programming
(Or equivalent to be determined by unit coordinator)
Overview
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Overview

Fundamentals of Data Science is a unit designed to introduce the students to fundamental data science techniques, including discrete mathematics, statistical analysis, data visualisation, data structures and algorithms using a high-level programming language. This unit will also take students through the fundamental processes of data analysis for business use including requirement analysis, data manipulation, data preparation, model evaluation, and basic data mining for future predictions. Ethical considerations, policies, and legislations for data science will be introduced. The unit will provide hands-on experience with case studies to basic data analysis methods and programming paradigms.

Learning Outcomes

On successful completion of this unit, students will be able to:

  1. Discuss review the functions of and solve problems related to data administration in organisations;
  2. Apply fundamental theoretical foundations and basic mathematics for exploratory data analysis and visualisation;
  3. Manipulate contemporary data analytics and its use in real-world scenarios;
  4. Apply fundamental techniques to collect and process big data; and
  5. Discuss data security and privacy regulations and ethical constrains.

Assessment

For Melbourne campuses

Assessment type: Test
|
Grade: 20%
Open book online test (fundamental programming knowledge and basic discrete mathematics skills)
Assessment type: Project
|
Grade: 40%
Group Project based assessment (comprehensive data analysis process with given business case study)
Assessment type: Case Study
|
Grade: 40%
Scenario-based in-class problem solving (Individual assessment with given scenarios and answer questions by the knowledge acquired from learning)

Required reading

Recommended readings will be made available on VU Collaborate.

As part of a course

This unit is studied as part of the following course(s):

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