City University of Hong Kong

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1 ity University of Hong Kong Information on a ourse offered by Department of Management Sciences with effect from Semester in 2014 / 2015 Part I ourse Title: nalytics using SS ourse ode: MS3251 ourse Duration: One semester redit Units: 3 Level: 3 Medium of Instruction: English Prerequisites: Nil Precursors: MS2200 usiness Statistics Equivalent ourses: MS3217 SS Programming Exclusive ourses: Nil Part II ourse ims Provide students with concepts and knowledge of analytics using SS 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. ourse Intended Learning Outcomes (ILOs) No. ILOs Weighting (if DErelated dimension 1. Discuss the relevant concepts of analytics in data management 10% bility 2. pply data handling techniques to produce SS dataset from raw data file and different sources; 10% ccomplishment 1

2 3. Discuss the methods of data manipulation including variable selection, observation selection, outlier handling, missing value handling and so on 4. Produce statistical and summary reports 60% ttribute 20% bility Teaching and Learning ctivities (TLs) (These are indicative of likely activities and tasks designed to facilitate students achievement of the ILOs. Final details will be provided to students in their first week of attendance in this course.) ILO TLs No Lecture: oncepts and general knowledge of analytics using SS are explained Introduce the methods of data manipulation and statistical reporting Tutorial: Students perform in-class hand-on exercise so that learning difficulties can be identified and tackled Hours/week (if onstructive lignment of ILOs and TLs TL 1 TL 2 ILO 1 ILO 2 ILO 3 ILO 4 Hours/week (if ssessment Tasks (These are indicative of likely tasks designed to assess how well the students achieve the ILOs. Final details will be provided to students in their first week of attendance in this course.) ILO No. Types of ssessment Tasks (Ts) ssessment Details ssignment ssignment is designed to enforce students understanding of the knowledge of analytics using SS Mid-term Test The mid-term test is designed to assess students understanding of the key concepts and logical algorithm of analytics using SS Written The exam is designed to assess Examination (3 hours) students professional knowledge of data management using SS as well as the ability to apply them to solve business problems Weighting (if 20% 30% 50% 2

3 onstructive lignment of ILOs and ssessment Tasks T 1 T 2 ILO 1 ILO 2 ILO 3 ILO 4 Grading of Student chievement: Refer to Grading of ourses in the cademic Regulations (ttachment) and to the Explanatory Notes. T1: ssignment + Strong evidence of original thinking; good organization, capacity to analyse and synthesize; superior grasp of subject matter; evidence of extensive knowledge base. + Student who is profiting from the university experience; understanding of the subject; ability to show some evidence of familiarity with without repeating the course. subject matter; weakness in critical and analytic skills; limited or irrelevant use of T2: Mid-term Test + 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. 3

4 + Student who is profiting from the university experience; understanding of the subject; ability to show some evidence of familiarity with further. subject matter; limited or irrelevant use of T3: Written Examination + Strong evidence of original thinking; good organization, capacity to analyse and synthesize; superior grasp of subject matter; evidence of extensive knowledge base. + Some evidence of grasp of subject, little issues. without repeating the case report. subject matter; weakness in critical and analytic skills; limited or irrelevant use of 4

5 Part III Keyword Syllabus 1. oncepts of analytics using SS Introduction to SS Foundation and logical algorithm 2. SS asic oncepts and component of SS system; Raw data handling; SS dataset creation; Produce simple statistical reports; 3. asic analytics using SS dd more information to all or selected observations; Variables selection; Observations selection; Outlier handling; Missing value handling; alculate across observations; Make use of SS functions; 4. Modifying and combining data Multiple datasets handling; ombine SS datasets; reate a sample of data; 5. Producing Statistical and Summary Reports Generate statistical reports using FREQ, MENs, and REPORT procedures. Delivery output of reports in a variety of formats; Recommended Reading 1. ody, Ronald P. and Smith, Jeffrey K. 2006, pplied statistics and the SS programming language, Fifth edition. ddison Wesley. 2. ody, Ron 1996, The SS Workbook. ary, N: SS Institute Inc. 3. Delwiche, Lora D. and Susan J. Slaughter 2003, The Little SS ook: Primer, Third Edition. ary, N: SS Institute Inc. 4. ody, Ron 2007, Learning SS by Example. ary, N: SS Institute Inc. 5