cse 422

Lovely Professional University, Punjab

Course Code Course Title Course Planner Lectures Tutorials Practicals Credits

CSE422 BUSINESS ANALYTICS 14597::Robin Prakash Mathur 3.0 0.0 0.0 3.0

Course Category Courses with Placement focus

TextBooks

Sr No Title Author Edition Year Publisher Name

T-1 Fundamentals of Business Analytics R. N. Prasad, Semma Acharya

1st 2011 Wiley

Reference Books

Sr No Title Author Edition Year Publisher Name

R-1 Data Mining: Concepts and Techniques

Jiawei Han, Micheline Kamber, Jian Pei

3rd 2011 Morgan Kaufmann Publishers

R-2 Data Warehousing, Data Mining, & Olap

Alex Berson, Stephen Smith

3rd Tata McGraw Hill, India

Other Reading

Sr No Journals articles as Compulsary reading (specific articles, complete reference)

OR-1 http://www.dashboardinsight.com/articles/new-concepts-in-business-intelligence/unstructured-data-101-practical-applications.aspx ,

OR-2 http://www.webopedia.com/TERM/E/ERP.html ,

OR-3 http://www.baldrige21.com/Baldrige%20Model.html ,

Relevant Websites

Sr No (Web address) (only if relevant to the course) Salient Features

RW-1 http://www.information-management.com/issues/20030201/6287-1.html The problem with unstructured data

RW-2 http://queue.acm.org/detail.cfm?id=1103832 semi structure data

RW-3 http://www.research.ibm.com/UIMA/UIMA%20Architecture%20Highlights.html IBM’s Unstructured Information Management Architecture (UIMA)

RW-4 http://www.dwreview.com/OLAP/Introduction_OLAP.html About Online Analytical Processing

RW-5 http://www.dwreview.com/DW_Overview.html About data warehouse

RW-6 http://publib.boulder.ibm.com/infocenter/iisinfsv/v8r0/index.jsp?topic=/com.ibm.swg.im.iis.productization.iisinfsv.overview.doc/topics/cisoiausingmonitor.html

data quality

RW-7 http://communication.howstuffworks.com/data-integration1.htm data integration basics

RW-8 http://mike2.openmethodology.org/wiki/Data_Profiling_Concept data profiling

RW-9 http://technet.microsoft.com/en-us/library/ms173745.aspx reporting services

RW-10 http://technet.microsoft.com/en-us/library/ms173745.aspx data mining basics

Audio Visual Aids

Sr No (AV aids) (only if relevant to the course) Salient Features

AV-1 http://www.viennaadvantage.com/index.php?option=com_content&view=article&id=97&Itemid=148

Fully open source ERP and CRM on the cloud demo video

Software/Equipments/Databases

Sr No (S/E/D) (only if relevant to the course) Salient Features

SW-1 http://download.101com.com/pub/TDWI/Files/BI%20Framework%202007-v5r.zip BI Framework

SW-2 http://www.pentaho.com/ Business Analytics tool

Week Number

Lecture Number

Broad Topic(Sub Topic) Chapters/Sections of Text/reference books

Other Readings,Relevant Websites, Audio Visual Aids, software and Virtual Labs

Lecture Description Learning Outcomes Pedagogical ToolDemonstration/ Case Study / Images / animation / ppt etc. Planned

Week 1 Lecture 1 Business view of IT applications(Core Business Processes)

T-1:chapter 1.1 Business enterpriseorganization, its function

core areas of organization

Lecture cum demonstrations

Lecture 2 Business view of IT applications(Baldridge Business Excellence Framework)

T-1:chapter 1.2 Baldridge Business ExcellenceFramework

model to represent business enterprise


Lecture 3 Business view of IT applications(Key purpose of using IT in Business)

T-1:chapter 1.3 IT in business Application of IT in organization


Week 2 Lecture 4 Business view of IT applications(Enterprise Applications)

T-1:chapter 1.5 terms like ERP and CRM enterprise application architecture


Lecture 5 Types of Digital Data(Structured Data)

T-1:chapter 2.1 2.3 RW-1 Digital data and characteristics of structured data

different types of digital data


Lecture 6 Types of Digital Data(Unstructured Data)

T-1:chapter 2.4 RW-3 characteristics of unstructured data

how to manage and store unstructured data


Detailed Plan For Lectures

LTP week distribution: (LTP Weeks)

Weeks before MTE 7

Weeks After MTE 6

Spill Over 2

Week 3 Lecture 7 Types of Digital Data(Semi-Structured Data)

T-1:chapter 2.5 RW-2 characteristics of semi structured data

how to manage and store semi structured data


Lecture 8 Types of Digital Data(Difference between Semi-structured,structured and unstructured data)

T-1:chapter 2.6 Difference between Semistructured,structured and unstructured datan

Difference between Semistructured,structured and unstructured data


Lecture 9 Introduction to OLTP and OLAP(Data Models of OLTP and OLAP)

T-1:chapter 3.1 3.2 RW-4 about OLAP and OLTP systems

advantages and challenges in systems


Week 4 Lecture 10 Introduction to OLTP and OLAP(Advantages and disadvantages of OLTP and OLAP)

T-1:chapter 3.4 Advantages and disadvantages of OLTP and OLAP

Advantages and disadvantages of OLTP and OLAP


Lecture 11 Introduction to OLTP and OLAP(Different OLAP Architectures)

T-1:chapter 3.3 ROLAP, MOLAP,HOLAP

multidimensional data Lecture cum demonstrations

Lecture 12 Introduction to OLTP and OLAP(Role of OLAP Tools in BI Architecture)

T-1:chapter 3.6 role of OLAP tools ETL process Lecture cum demonstrations

Week 5 Lecture 13 Introduction to OLTP and OLAP(OLAP Operations on Multidimensional Data)

T-1:chapter 3.8 slice, dice, drill down operations

OLAP operations Lecture cum demonstrations

Lecture 14 Getting started with Business Intelligence(Using Analytical Information for Decision Support)

T-1:chapter 4.1 Business intelligence role of BI in decision making


Lecture 15 Getting started with Business Intelligence(Definition and need of Business Intelligence)

T-1:chapter 4.2 4.3 Business intelligence role of BI in decision making


Week 6 Lecture 16 Getting started with Business Intelligence(Evolution of BI and Role of DSS, EIS, MIS and Digital Dashboards)

T-1:chapter 4.4 4.5 Role of DSS,EIS MIS how DSS,EIS MIS helps in decision making


Lecture 17 Getting started with Business Intelligence(BI Value Chain)

T-1:chapter 4.6 4.7 Transformation, storage, delivery

Transformation, storage, delivery


Lecture 18 Test, Test, Live project 1

Week 7 Lecture 19 BI Definitions and Concepts(BI Component Framework)

T-1:chapter 5.1 business layer,operational layer, implementation layer

terms like ROI,ROA,TCO,TVO


Lecture 20 BI Definitions and Concepts(BI Users and Applications)

T-1:chapter 5.2 users of BI technology solutions and business soltions


BI Definitions and Concepts(BI roles and responsibilities)

T-1:chapter 5.3 BI team members responsibility of BI team members


Lecture 21 BI Definitions and Concepts(Skill of BI Professional)

T-1:chapter 5.6 specialization in BI skill set of members Lecture cum demonstrations

BI Definitions and Concepts(Popular BI Tools)

T-1:chapter 5.8 tools under business analytic

tools names Lecture cum demonstrations

MID-TERM

Week 8 Lecture 22 Basics of Data Integration(Need for Data Warehouse)

T-1:chapter 6.1 RW-5 definition of data warehouse

characteristics of warehouse


Lecture 23 Basics of Data Integration(Datamarts and ODS)

T-1:chapter 6.3 6.4 RW-7 data marts and ODS knowledge of datamarts and ODS


Lecture 24 Basics of Data Integration(Ralph Kimball’s Approach Vs. W.H. Inmon’s Approach)

T-1:chapter 6.5 models for building warehouse

building strategy of warehouse


Week 9 Lecture 25 Basics of Data Integration(Goals of Data Warehouse and ETL process)

T-1:chapter 6.6 6.8 ETL process stages of warehouse construction


Lecture 26 Basics of Data Integration(Data Integration and related Technologies)

T-1:chapter 6.9 6.10 RW-8 schema integration and instance integration

technologies of data integration


Lecture 27 Basics of Data Integration(Data Quality and Data Profiling)

T-1:chapter 6.11 6.12 RW-6 data quality and profiling data integrity Lecture cum demonstrations

Week 10 Lecture 28 Multidimensional Data Modeling(Data modeling basics and types of data model)

T-1:chapter 7.1 7.3 data models conceptual,logical , physical data model


Lecture 29 Multidimensional Data Modeling(Data Modeling Techniques)

T-1:chapter 7.4 normalization techniques

normalization techniques


Lecture 30 Multidimensional Data Modeling(Fact Table,Dimension Table and typical dimensional models)

T-1:chapter 7.5 to 7.7 facts,dimensions and models

star,snowflake and fact constellation


Week 11 Lecture 31 Multidimensional Data Modeling(Dimensional Modeling Life Cycle)

T-1:chapter 7.8 phases of data modelling phases of data modelling


Lecture 32 Measures, Metrics, KPIs and Performance Management(Measurement System Terminology)

T-1:chapter 8.1 8.2 data , measure,indicators,metrics

measurement terminology


Lecture 33 Measures, Metrics, KPIs and Performance Management(Role of Metrics, and Metrics Supply Chain)

T-1:chapter 8.3 performance management

role of metrics Lecture cum demonstrations

Week 12 Lecture 34 Test, Test, Live project 2

Lecture 35 Measures, Metrics, KPIs and Performance Management(Fact-Based Decision Making and KIPS)

T-1:chapter 8.4 Fact Based Decision Making and KIPS

Fact Based Decision Making and KIPS


Measures, Metrics, KPIs and Performance Management(KPI Usage in Companies and Origin of Business Metrics and KPIs)

T-1:chapter 8.5 8.6 KPI usages KPI usages Lecture cum demonstrations

Lecture 36 Basics of Enterprise Reporting(Report Standardization and Presentation Practices)

T-1:chapter 9.1 9.2 Report Standardization Presentation Practices and Report Standardization


Basics of Enterprise Reporting(Enterprise Reporting Characteristics in OLAP World)

T-1:chapter 9.3 enterprising reporting enterprising reporting Lecture cum demonstrations

Week 13 Lecture 37 Basics of Enterprise Reporting(Balanced Scorecard)

T-1:chapter 9.4 scorecard knowledge management


Basics of Enterprise Reporting(Dashboards)

T-1:chapter 9.5 OR-1 dashboard visualization technique Lecture cum demonstrations

Lecture 38 Future of Business Intelligence(Understanding BI and Mobility)

T-1:chapter 10.1 BI mobility BI mobility Lecture cum demonstrations

Future of Business Intelligence(BI and Cloud Computing)

T-1:chapter 10.2 OR-2 cloud computing concepts of cloud computing


Lecture 39 Future of Business Intelligence(Business Intelligence for ERP Systems)

T-1:chapter 10.3 Future of BI Future and trends of BI Lecture cum demonstrations

Future of Business Intelligence(Social CRM and BI)

T-1:chapter 10.4 Basics of CRM Basics of CRM Lecture cum demonstrations

SPILL OVERWeek 14 Lecture 40 R-1:chapter 9 Outlier Detection about outlier and noise

in dataLecture cum demonstrations

Lecture 41 R-1:chapter 10 RW-10 Data Mining Trends ResearchFrontiers Data mining application

future trends in data mining


Scheme for CA:Component Frequency Out Of Each Marks Total Marks

Test, Test, Live project 2 3 10 20

Total :- 10 20

Details of Academic Task(s)

AT No. Objective Topic of the Academic Task Nature of Academic Task(group/individuals/field

work

Evaluation Mode Allottment / submission Week

Test 1 Class test Syllabus of class test 1 will be from week 1 to week 5 Individual Written test 4 / 6

Test 1 Class test 2 Syllabus of class test 2 will be from from week 7 to week 10 Individual Written test 9 / 11

Live project 1 Live project on business analytics

To hone up the decision making skills, increase practical knowledge of students about the given organization and its competitive scenario. The student can take any organization data and make analysis of that in tools like pentahoo, SPSS, MS Excel.

Group Live project 5 / 12

cse 422

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