'Big Data' phenomenon is an emerging force in the global business world. It is characterised by five Vs: Volume, Velocity, Variety, Veracity and Value. It increasingly makes data sets too large to store and analyse beyond the ability of traditional relational database technology. This unit (NIT2202) provides fundamentals related to the technology and the core concepts behind big data problems, applications, and systems. It provides an introduction to the most common open-source software framework to increase the potential for data to transform our world. Students will develop comprehensive understanding of the challenges that organisations are facing for managing 'Big Data' and the technological solutions for efficient and strategic decision making.
Unit details
Location:
Study level:
Undergraduate
Credit points:
12
Unit code:
NIT2202
Prerequisites
NIT1102 - Introduction to Programming or
NIT1201 - Introduction to Database Systems
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Learning Outcomes
| 1. | Analyse and illustrate Big Data challenges to the business world; | ||
| 2. | Explain the impact of Big Data's five V's (volume, velocity, variety veracity and value) using real world examples; | ||
| 3. | Apply architectural components and programming models of commonly used Big Data; | ||
| 4. | Create a technological solution using open-source software framework. |
Assessment
Melbourne campuses
Students studying under the VU Block Model.
| Assessment type | Description | Grade |
|---|---|---|
| Test | Test 1 (1 hour theoretical knowledge test) | 20% |
| Test | Test 2 (1 hour theoretical knowledge test) | 20% |
| Laboratory Work | Practical Lab work | 30% |
| Case Study | Group assignment and presentation | 30% |
Other locations
| Assessment type | Description | Grade |
|---|---|---|
| Test | Knowledge Test (1 hour) | 25% |
| Laboratory Work | Weekly Practical Lab Work | 25% |
| Examination | Final Written Examination (3 hours) | 50% |
Required reading
Big data fundamentals: Concepts, drivers & techniques
Erl, T., Khattak, W., & Buhler. P. (2016)
Prentice Hall: Boston, MA
Hands-On Data Science with Anaconda
Yan, Y. & Yan, J. (2018)
Packt Publishing.
Where to next?
As part of a course
This unit is studied as part of the following courses. Refer to the course page for information on how to apply for the course.
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