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July 2016
The Hong Kong Polytechnic University
Hong Kong Community College
Subject Description Form
Subject Code
CCN3163
Subject Title
Introduction to Big Data Analytics
Level
3
Credit Value
3
Medium of
Instruction
English
Pre-requisite /
Co-requisite/
Exclusion
Pre-requisite
Objectives
This subject aims to provide students with the knowledge of current challenges,
methodologies and technologies in processing big data. Emphasis will be
placed on the students’ understanding of the rationales behind the technologies
and the students’ ability to analyse big data using professional software
packages.
Intended Learning
Outcomes
Upon completion of the subject, students will be able to:
CCN2041 Applied Computing
(a)
(b)
(c)
(d)
(e)
Subject Synopsis/
Indicative Syllabus
understand the current challenges in processing big data
aware of the technologies available for handling big data
understand how big data are generated in different industries
understand the ideas behind data mining methods targeted for big data
analyse big datasets through the use of application software
Basic Concepts and Issues in Handling Big Data
Industries that generates big data; Types of big data; Challenges in processing
big data; The curse of dimensionality.

Technologies and Infrastructures
Parallel computing; Map-Reduce; Distributed data.

Clustering and Mining of Similar Items
Similarity measures; Near-neighbor search; Similarity of text documents;
Clustering of similar items; Strategies for clustering.
Mining of Data Streams
Common sources of data streams; Sampling from data streams; Obtaining
summary statistics from data streams.
Frequent Itemsets
Common sources of market-basket data; Association rules; Support and
confidence of association rules; Efficient algorithms for mining association
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July 2016
rules from large dataset.
Link Analysis and Social Network
Basic concepts and applications of PageRank; Representations of social
network; Identification of communities in social network.

Advertising on the Web and Direct Marketing
Targeted vs untargeted advertising; On-line vs off-line algorithm; Adwords
bidding; Predictive models for direct marketing; Evaluation of market
campaign.
Teaching/Learning
Methodology
Lectures are used to introduce concepts, challenges and mythologies in
processing big data. Real life examples will be used to enhance students’
understanding in the subject matter. Tutorials will be a combination of
demonstration of data analysis and hands-on activities in analysing big data.
Assessment Methods A variety of assessment tools will be used to develop and assess students’
achievement of the subject intended learning outcomes.
in Alignment with
Intended Learning
Specific assessment
%
Intended subject learning outcomes to
Outcomes
methods/tasks
weighting be assessed
a
b
c
d
e
✓
✓
✓
✓
✓
✓
1. Continuous
Assessment*
40
Test
16
✓
✓
Assignment 1
10
✓
✓
Assignment 2
10
Participation
4
✓
✓
✓
✓
✓
2. Final Examination
60
✓
✓
✓
✓
✓
Total
100
*Continuous assessment items and/or weighting may be adjusted by the subject team
subject to the approval of the College Programme Committee.
To pass this subject, students are required to obtain Grade D or above in both
the Continuous Assessment and Final Examination.
Student Study
Effort Expected
Class contact
Hours

Lecture
26

Tutorial
13
Other student study effort

Self-study
52

Continuous Assessment
39
2
July 2016
Total student study effort
Reading List and
References
130
Recommended Textbook
Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014), Mining of massive
datasets. (2nd ed.), Cambridge University Press.
References
Baesens, B. (2014), Analytics in a big data world: The essential guide to data
science and its applications, Wiley.
White, T. (2015), Hadoop: The definitive guide. (4th ed.), O’Reilly Media.
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