City University of Hong Kong

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City University of Hong Kong

Information on a Course

offered by Department of Management Sciences with effect from Semester A in 2014 / 2015

Part I

Course Title: Analytics using SAS Course Code: MS3251

Course Duration: One semester Credit Units: 3

Level: B3

Medium of Instruction: English Prerequisites: Nil

Precursors: MS2200 Business Statistics

Equivalent Courses: MS3217 SAS Programming Exclusive Courses: Nil

Part II Course Aims

 Provide students with concepts and knowledge of analytics using SAS

 Develop students’ analytics technique to access data, manipulate data and do

statistical reporting.

 Prepare students for a position in managing business activity for data

management, database marketing in the commercial and government sectors.

Course Intended Learning Outcomes (CILOs)

No. CILOs Weighting (if

applicable)

DEC-related dimension 1. Discuss the relevant concepts of

analytics in data management 10% Ability 2. Apply data handling techniques to

produce SAS dataset from raw data file and different sources;

10% Accomplishment

Proposed

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3. Discuss the methods of data manipulation including variable

selection, observation selection, outlier handling, missing value handling and so on

60% Attribute

4. Produce statistical and summary

reports 20% Ability

Teaching and Learning Activities (TLAs)

(These are indicative of likely activities and tasks designed to facilitate students’ achievement of the CILOs. Final details will be provided to students in their first week of attendance in this course.)

CILO No.

TLAs Hours/week (if

applicable) 1-4 1. Lecture:

Concepts and general knowledge of analytics using SAS are explained

Introduce the methods of data manipulation and statistical reporting.

1-4 2. Tutorial:

Students perform in-class hand-on exercise so that learning difficulties can be identified and tackled Constructive Alignment of CILOs and TLAs

TLA 1 TLA 2 Hours/week (if applicable) CILO 1 CILO 2   CILO 3   CILO 4   Assessment Tasks

(These are indicative of likely tasks designed to assess how well the students achieve the CILOs. Final details will be provided to students in their first week of attendance in this course.)

CILO No.

Types of Assessment Tasks (ATs)

Assessment Details Weighting (if

applicable) 1-4 1. Assignment Assignment is designed to enforce

students’ understanding of the knowledge of analytics using SAS

20% 1-4 2. Mid-term Test The mid-term test is designed to

assess students’ understanding of the key concepts and logical algorithm of analytics using SAS

30%

1-4 3. Written

Examination (3 hours)

The exam is designed to assess students’ professional knowledge of data management using SAS as well as the ability to apply them to solve business problems

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Constructive Alignment of CILOs and Assessment Tasks AT 1 AT 2 CILO 1   CILO 2   CILO 3   CILO 4  

Grading of Student Achievement: Refer to Grading of Courses in the Academic Regulations (Attachment) and to the Explanatory Notes.

AT1: Assignment Letter Grade Grade Point Grade Definitions A+ A A- 4.3 4.0 3.7

Excellent Strong evidence of original thinking; good organization, capacity to analyse and synthesize; superior grasp of subject matter; evidence of extensive

knowledge base. B+ B B- 3.3 3.0 2.7

Good Evidence of grasp of subject, some evidence of critical capacity and analytic ability; reasonable understanding of issues; evidence of familiarity with literature. C+ C C- 2.3 2.0 1.7

Adequate Student who is profiting from the university experience; understanding of the subject; ability to show some evidence of familiarity with literature. D 1.0 Marginal Sufficient familiarity with the subject

matter to enable the student to progress without repeating the course.

F 0.0 Failure Little evidence of familiarity with the subject matter; weakness in critical and analytic skills; limited or irrelevant use of literature.

AT2: Mid-term Test Letter Grade Grade Point Grade Definitions A+ A A- 4.3 4.0 3.7

Excellent Strong evidence of understanding the key concepts and definitions of the learned subject; capacity to analyse and synthesize; superior grasp of subject matter; evidence of extensive knowledge base.

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B+ B B- 3.3 3.0 2.7

Good Evidence of grasp of subject, some evidence of critical capacity and analytic ability; reasonable understanding of issues; evidence of familiarity with literature. C+ C C- 2.3 2.0 1.7

Adequate Student who is profiting from the university experience; understanding of the subject; ability to show some evidence of familiarity with literature. D 1.0 Marginal Sufficient familiarity with the subject

matter to enable the student to progress further.

F 0.0 Failure Little evidence of familiarity with the subject matter; limited or irrelevant use of literature.

AT3: Written Examination Letter Grade Grade Point Grade Definitions A+ A A- 4.3 4.0 3.7

Excellent Strong evidence of original thinking; good organization, capacity to analyse and synthesize; superior grasp of subject matter; evidence of extensive knowledge base. B+ B B- 3.3 3.0 2.7

Good Evidence of grasp of subject, some evidence of critical capacity and analytic ability; reasonable understanding of issues; evidence of familiarity with literature. C+ C C- 2.3 2.0 1.7

Adequate Some evidence of grasp of subject, little evidence of critical capacity and analytic ability; reasonable understanding of issues.

D 1.0 Marginal Sufficient familiarity with the subject matter to enable the student to progress without repeating the case report. F 0.0 Failure Little evidence of familiarity with the

subject matter; weakness in critical and analytic skills; limited or irrelevant use of literature.

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Part III

Keyword Syllabus

1. Concepts of analytics using SAS

Introduction to SAS Foundation and logical algorithm 2. SAS Basic

Concepts and component of SAS system; Raw data handling; SAS dataset creation; Produce simple statistical reports;

3. Basic analytics using SAS

Add more information to all or selected observations; Variables selection; Observations selection; Outlier handling; Missing value handling; Calculate across observations; Make use of SAS functions;

4. Modifying and combining data

Multiple datasets handling; Combine SAS datasets; Create a sample of data; 5. Producing Statistical and Summary Reports

Generate statistical reports using FREQ, MEANs, and REPORT procedures. Delivery output of reports in a variety of formats;

Recommended Reading

1. Cody, Ronald P. and Smith, Jeffrey K. 2006, Applied statistics and the SAS

programming language, Fifth edition. Addison Wesley.

2. Cody, Ron 1996, The SAS Workbook. Cary, NC: SAS Institute Inc.

3. Delwiche, Lora D. and Susan J. Slaughter 2003, The Little SAS Book: A Primer,

Third Edition. Cary, NC: SAS Institute Inc.

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References

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