Download The Hong Kong Polytechnic University Hong Kong Community

June 2015
The Hong Kong Polytechnic University
Hong Kong Community College
Subject Description Form
Subject Code
CCN1039
Subject Title
Information Processing and Quantitative Methods
Level
1
Credit Value
3
Medium of
Instruction
English
Pre-requisite /
Co-requisite/
Exclusion
Exclusion
CCN1008 Mathematics and Statistics for College Students
CCN1028 Elementary Statistics
Objectives
This subject equips students with basic concepts on some quantitative
methods and information processing skills. It emphasises the application
of information processing skills and quantitative techniques to solve
practical real-life problems.
Intended Learning
Outcomes
Upon completion of the subject, students will be able to:
(a) understand the mechanism of information processing.
(b) acquire the skills needed to manipulate different kinds of data using
computers.
(c) understand some basic quantitative knowledge and skills.
(d) tackle practical quantitative knowledge related problems with the
help of computers effectively and efficiently.
(e) demonstrate the abilities of logical and analytical thinking.
Subject Synopsis/
Indicative Syllabus
Computer Basics
Basic concepts of computers; Computer hardware; Computer software;
Computer networks; Popular Internet or mobile applications.
Information Processing Skills
Spreadsheet and its uses; Database and its uses; Presentation software
and its uses; Data manipulation and presentation techniques.
Nature of Statistics
Descriptive and inferential statistics; Types of data; Data collection
methods; Sampling techniques.
Descriptive Statistics
Data organisation; Frequency analysis; Graphical representation; Central
tendency; Variability; Position; Use of descriptive statistics for data
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organisation.
Normal Distributions
Properties of normal distribution; Standard normal distribution; Sampling
distribution of sample mean; Central limit theorem; Use of normal
distributions.
Decision Analysis
Probability; Decision making under certainty/risk/uncertainty; Decision
tables; Decision trees.
Teaching/Learning
Methodology
Both quantitative methods and the basic concepts on computing will be
the emphasis of lectures. In addition, students will acquire the necessary
information processing skills to tackle the quantitative knowledge related
problems in daily life. Group discussions and activities may be arranged
to stimulate students’ interest or their awareness of practical implications
of some concepts taught. Individual hands-on lab exercises will facilitate
students’ IT learning and enhance their logical and analytical thinking
abilities.
Assessment
Methods in
Alignment with
Intended Learning
Outcomes
A variety of assessment tools will be used to develop and assess students’
achievement of the subject intended learning outcomes.
Specific assessment
methods/tasks
%
weighting
Intended subject learning
outcomes to be assessed
a
b
c
d
e
9
9
9
9
9
9
9
Continuous Assessment*
50
ƒ Test
25
ƒ Individual Assignment
10
ƒ In-class Assignments
15
9
9
9
9
9
Final Examination
50
9
9
9
9
9
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
ƒ Lecture
26
ƒ Tutorial
13
Other student study effort
ƒ Self-study
52
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Reading List and
References
ƒ Coursework
40
Total student study effort
131
Recommended Textbook
Shelly, G. B. & Vermaat, M. E. (2010) Discovering computers 2011:
Brief. (1st ed.), Course Technology.
Bluman, A, G. (2012) Elementary Statistics: A step by Step Approach A
Brief Version. (6th ed.), McGraw-Hill.
References
Triola, M. F. (2009) Elementary statistics using Excel. (4th ed.), Addison
Wesley.
Dretzke, B. (2008) Statistics with Microsoft Excel. (4th ed.), Prentice
Hall.
Harnett, D. L. & Horrell, J. F. (1998) Data, statistics, and decision
models with Excel. Wiley.
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