《BusinessAnalytics》教学大纲（本科）.docx

Business Analytics教学大纲课程英文名Business Analytics课程代码I0203Z03学分3总学时48理论学时48实验/实践学时0课程类别专业课课程性质必修先修课程高数、线性代数、概率论与数理统计、 统计学、基础会计适用专业金融工程开课学院经济与管理学院执笔人审定人制定时间2018年4月一、课程地位与课程目标（一）课程地位The aim of this module is to build and developed students9 understanding and application of mathematics and statistics, appropriate for business disciplines at the undergraduate level. The module begins by reviewing various techniques for collecting and presenting data. Some important measures and techniques for making sense of raw data are then introduced which are widely used by businesses when analysing both their internal and external environments.This module supports a range of numerical skills and techniques commonly developed within the modules "Introduction to Accounting and Finance, and "Foundations of Finance both in a theoretical manner and a practical approach through the use of Microsoft Excel. This module also provides mathematics and statistics skills which are integral to the business finance and economics modules which students will take in future semesters as part of their ongoing development. Students will be helped to develop further their hands-on skills in using Microsoft Excel in computer workshops to handle both small and large business data. This is considered to be an appropriate approach to learning in which relevant statistical techniques and methods will be explored in a practice based manner in keeping with that of other business and finance modules where possible.Careful attention will also be paid throughout the module to the interpretation and application of the various quantitative methods and techniques to the solution of real-world business problems.（二）课程目标This module, like all modules at Anglia Ruskin, is taught on the basis of achieving intended learning outcomes. On successful completion of the module, the student will be expected to be able to demonstrate the following:Knowledgeand LO 1.Identify and assess the usefulness of key numerical,understandinggraphical and statistical indicators of business activity.LO 2.Demonstrate an awareness of how these variousquantitative indicators can be used by businesses to better inform their decision makingIntellectual, practical, LO 3. Apply statistical packages and spreadsheets to support theaffective and transferable skillsaffective and transferable skillsquantitative analysis of raw data.The assessment is based on meeting these learning outcomes, shown explicitly in section 4, where the assessment task is linked to these learning outcomes.二、课程目标达成的途径与方法课程目标达成的途径主要通过课堂教学，辅以课外作业的形式。三、课程目标与相关毕业要求的对应关系课程目标课程目标对毕业要求的支撑程度（H、M、L）毕业要求3毕业要求4毕业要求7课程目标1MML课程目标2HHM课程目标3MMH四、课程主要内容与基本要求The table below indicates how the module will be delivered. However, this schedule is indicative and may be subject to change.WeekLecture1Introduction2Data Presentation and CollectionoFrequency distributionoFrequency tableso Discrete or continuous datao HistogramsoFrequency polygonoFrequency curveo Cumulative frequency curves Bar charts and Pie charts3Measures of Central Locationo Ungrouped and grouped data o Discrete and continuous data o Notationo Mean, Median and Mode: ungrouped datao Mean, Median and Mode: grouped data Applications4Measures of Dispersion1. Measures of dispersion: ungrouped data Range Interquartile range Semi-interquartile range Mean deviation Variance Standard deviation2. Measures of dispersion: grouped data Range Interquartile range Semi-interquartile range Mean deviation Variance Standard deviation Coefficient of variation5Linear and Non-linear Relationships and Graphs Linear Functions Linear Equations Linear Graphs Application of Linear Relationships Non-Linear Functions and Graphs Regression and Correlation6Time Series and Forecasting7Cambridge: Employability Week (NO TEACHING)Chelmsford: Time Value of Money1. Investment appraisal2. Payback Period3. Average Rate of Return (ARR)4. Net Present Value (NPV)5. Internal Rate of Return (IRR)8Cambridge: Time Value of Money1. Investment appraisal2. Payback Period3. Average Rate of Return (ARR)4. Net Present Value (NPV)5. Internal Rate of Return (IRR)Chelmsford: Employability Week (NO TEACHING)9 Normal Distribution and Probability1. Normal distribution2. Standard normal distribution and the Z statistic Application of the Z statistic to probability issues10 Hypothesis Testing (Large Sample)11 Hypothesis Testing (Student t test)12 Chi-Square Test13 Revision (Please check Date, Time and Room via your timetable)五、课程学时安排教学内容教学内容学时数学生任务对应课程目标1Introduction21、22Data Presentation andCollection4作业123Measures of CentralLocation4作业22、34Measures of Dispersion435Linear and Non-linearRelationships and Graphs6作业31、26Regression and Correlation42、37TimeSeriesandForecasting418Time Value of Money4作业41、29Normal Distribution andProbability4作业5310Application of the Z statistic to probability issues41、2、311Hypothesis Testing4作业61、2、312Chi-Square Test42、3六、实践环节及基本要求序号实验项目学时基本要求实验性质实验类别七、考核方式及成绩评定考核内容考核方式评定标准（依据）占总成绩比例过程考核含到课率及平时作业等上课点名考勤、对作业进行成绩评定20-50%期末考核闭卷对期末试卷进行评定50-80%考核类别考试成绩登记方式百分制八、推荐教材与主要参考书1、推荐教材1 Dinabandhu Bag 著，Business Analytics，London： Routledge Press, 2016 年 10 月版。2 Wolfgang Jank 著，（Business Analytics for Managers，Springer, 2011 年 8 月版。2、主要参考书1 Fausto Pedro Garcia Marquez; Benjamin Lev 著，（Advanced Business Analytics，Springer, 2015 年2月版。2 Liebowitz, Jay 著：（Business Analytics: A Introduction, Auerbach Publications, 2014 年 1 月。