bi

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Hindusthan College of Engineering and Technology
An Autonomous Institution Affiliated to Anna University | Approved by AICTE, New Delhi
Accreditedwith ‘A’ Grade by NAAC | Accredited by NBA (ECE, MECH, EEE, IT & CSE)
Valley Campus, Pollachi Highway, Coimbatore 641 032.| www.hicet.ac.in
Course Information Sheet (CIS)
1. Academic Year : 2021 – 22 EVEN Semester
2. Name of Course Coordinator :R.Gayathri
3. Department : Computer science and Engineering
4. Programme : B.E(CSE)
5. Class and semester : III/VI
6. Course code and title : 19CS6301 Business Intelligence- Data Warehousing and Analytics
7. Regulations : R2019
8. Course Category : PE
9. Contact hours : 45
10. Type of course : Theory
11. Credit : 3
12. Course Attainment level :Level 1: 66-75% ; Level 2 : 76-85% ; Level 3: >85%
13. Course pre-requisites : 19CS5251 Data Mining
13. Course Learning Objectives (CLO) :
1. To study about Transaction Processing and Analytical applications.
2. To demonstrate Business Intelligence framework.
3. To demonstrate Data Warehouse implementation and methodology.
4. To apply a business scenario, identify the metrics, indicators to achieve the business goal
5. To apply application of concepts using open source/MS Office
14. Course Outcomes (COs) :
Upon successful completion of this course, the student will be able to:
CO1 - Understand difference between Transaction Processing and Analytical applications and
describe the need for Business Intelligence
CO2 - Demonstrate to understand technology and processes associated with Business
Intelligence framework
CO3 - Demonstrate to understand Data Warehouse implementation methodology and project
life cycle
CO4 - Formulate given a business scenario, identify the metrics, indicators and make

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recommendations to achieve the business goal
CO5 - Demonstrate application of concepts using open source/MS Office.
15. Syllabus:
UNIT I - INTRODUCTION TO BUSINESS INTELLIGENCE 9
CO1
Introduction to digital data and its types – structured, semi-structured and unstructured, Introduction to
OLTP and OLAP (MOLAP, ROLAP, HOLAP)
UNIT II - BUSINESS INTELLIGENCE PROCESS AND FRAMEWORK 9
CO2 BI Definitions & Concepts, BI Framework, Data Warehousing concepts and its role in BI, BI
Infrastructure Components – BI Process, BI Technology, BI Roles & Responsibilities, Business
Applications of BI, BI best practices.
UNIT III - BASICS OF DATA INTEGRATION (EXTRACTION TRANSFORMATION
LOADING)
9
CO3 Concepts of data integration, needs and advantages of using data integration, introduction to common
data integration approaches, Meta data - types and sources, Introduction to data quality, data profiling
concepts and applications, introduction to ETL using Pentaho data Integration (formerly Kettle)
UNIT IV - INTRODUCTION TO MULTI-DIMENSIONAL DATA MODELING 9
CO4 Introduction to data and dimension modeling, multidimensional data model, ER Modeling vs. multi-
dimensional modeling, concepts of dimensions, facts, cubes, attribute, hierarchies, star and snowflake
schema, introduction to business metrics and KPIs, creating cubes using Microsoft Excel
UNIT V - BASICS OF ENTERPRISE REPORTING 9
CO5 A typical enterprise, Malcolm Baldrige - quality performance framework, balanced scorecard, enterprise
dashboard, balanced scorecard vs. enterprise dashboard, enterprise reporting using MS Access / MS
Excel, best practices in the design of enterprise dashboards
Total Instructional Hours - 45
16. Text books and Reference books:
T1: “Fundamentals of Business Analytics” by R.N.Prasad and Seema Acharya, Wiley 2011.
T2: “Data Strategy: How To Profit From A World Of Big Data, Analytics And The Internet Of
Things” by Bernard Marr.
R1: Business Intelligence by David Loshin, Second Edition, Elsevier, 2012.
R2: Business intelligence for the enterprise by Mike Biere, IBM Press, 2003.
R3: Business intelligence roadmap by Larissa Terpeluk Moss, Shaku Atre, Addison-Wesley
Professional, 2003.
R4: “Data Analytics For Beginners: Your Ultimate Guide To Learn And Master Data Analysis. Get
Your Business Intelligence Right – Accelerate Growth And Close More Sales” by Victor Finch.

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Video Links:
5
17. Course plan:
S.
No
Name of the Topic
No of
Hours
Cumul.
Hours
Teaching
Methods
Teaching
Aids
Text/
Referen
ce
books
UNIT I - INTRODUCTION TO BUSINESS INTELLIGENCE
GROUP I
1 Introduction to digital data and its types 1 1 Lecture
Video
T1
GROUP II
2 Structured 1 2
Lecture
PPT,
Video,
Animation
s
T1, R2
3 Semi-Structured 1 3
4 Unstructured 1 4
5 Introduction To OLTP 1 5
GROUP III
6 Introduction to OLAP 1 6
Lecture,
Quiz
Power
point
presentatio
n,
Video
T1, T2
7 MOLAP 1 7
8 ROLAP 1 8
9 HOLAP 1 9
Scheduled completion of Unit I : 9 hours
UNIT II - BUSINESS INTELLIGENCE PROCESS AND FRAMEWORK
GROUP I
10 BI Definitions & Concepts 1 10
Lecture Power
point
presentatio
n
T1, T2
11 BI Framework 1 11
12 Data Warehousing concepts and its role in BI 1 12
13 BI Infrastructure Components 1 13
Flipped
Class
T1
GROUP II
14 BI Process 1 14 Group
Discussi
on /
PPT/
Blackboar
d
15 BI Technology 1 15

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S.
No
Name of the Topic
No of
Hours
Cumul.
Hours
Teaching
Methods
Teaching
Aids
Text/
Referen
ce
books
16 BI Roles & Responsibilities 1 16 Lecture
/ Quiz
T1,R2
17 Business Applications of BI 1 17
18 BI best practices 1 18
Scheduled completion of Unit II : 9 hours
UNIT III - BASICS OF DATA INTEGRATION (EXTRACTION TRANSFORMATION LOADING
GROUP I
19 Concepts Of Data Integration 1 19
Lecture Video T1
20
Needs And Advantages Of Using Data
Integration
1 20
21
Introduction To Common Data Integration
Approaches
1 21
22 Meta data 1 22
GROUP II
23 Types And Source 1 23
Lecture,
Quiz
Video T1, R1
24 Introduction to data quality 1 24
25 Data Profiling Concepts And Applications, 1 25
Group III
26
Introduction To ETL Using Pentaho Data
Integration (Formerly Kettle)
1 26
Flipped
Class
Room
Power
Point
Presentati
on
T1
27
Introduction To ETL Using Pentaho Data
Integration (Formerly Kettle)
1 27
Scheduled completion of Unit III : 9 hours
UNIT IV - INTRODUCTION TO MULTI-DIMENSIONAL DATA MODELING
GROUP I
28
Introduction To Data And Dimension
Modeling, 1 28
Flipped
class
room
PPT R1
29 Multidimensional Data Model, 1 29
Lecture Power
point
presentatio
T1, R1
30
ER Modeling Vs. Multi-Dimensional
Modeling,
1 30
31 Concepts Of Dimensions, 1 31
32 Facts, Cubes, Attribute 1 32

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S.
No
Name of the Topic
No of
Hours
Cumul.
Hours
Teaching
Methods
Teaching
Aids
Text/
Referen
ce
books
n
Group Ii
33 Hierarchies, Star 1 33
Lecture Video,
Online
reference
video
T1,R1,
R2
34 Snowflake Schema 1 34
35 Introduction To Business Metrics And Kpis 1 35
36 Creating Cubes Using Microsoft Excel 1 36
Scheduled completion of Unit IV : 9 hours
UNIT V - BASICS OF ENTERPRISE REPORTING
GROUP I
37 A Typical Enterprise 1 37
Lecture Video
T1, R3
38 Malcolm Baldrige 1 38
GROUP II
39
Quality Performance Framework
1 39
Lecture Video,
Online
reference
video
T1,T2
40
Balanced Scorecard
1 40
41 Enterprise Dashboard 1 41
42
Balanced Scorecard Vs. Enterprise
Dashboard, 1 42
43
Enterprise Reporting Using MS Access / MS
Excel
1 43
44
Best Practices In The Design Of Enterprise
Dashboards
1 44
GROUP III
45
Best Practices In The Design Of Enterprise
Dashboards
1 45
Lecture,
Group
Discussio
n
Video T1,R3
Scheduled completion of Unit V : 9 hours

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18. Weightage of unit contents:
Factors considered,
F1 - Number of periods allotted for teaching the unit and weightage per hour is equal 1.
F2 - Usefulness of the content matter of the unit in the students’ learning point of view and
its weightage equal to 1 if useful, otherwise zero.
F3 - Usefulness of the content matter of the unit in understanding other units of the same
subject and its weightage equal to 1 if useful, otherwise zero.
F4- Usefulness of the content matter of the unit in understanding other subjects prescribed
for the programme and its weightage equal to 1 if useful, otherwise zero.
Topic F1 F2 F3 F4 A1
(Weightage)
A2
(%)
UNIT I - INTRODUCTION TO BUSINESS INTELLIGENCE
Introduction to digital data and its types
9
1 1
18 19.6
Structured, Semi-Structured, Unstructured 1 1
Introduction To OLTP and OLAP 1
MOLAP 1 1 1
ROLAP 1
HOLAP 1 1 1
UNIT II - BUSINESS INTELLIGENCEPROCESS ANDFRAMEWORK
16 17.4
BI Definitions & Concepts 1
BI Framework, Data Warehousing concepts and its role
in BI
1
BI Infrastructure Components, BI Process , BI
Technology
1 1
BI Roles & Responsibilities , Business Applications of
BI
1
UNIT III - BASICS OF DATA INTEGRATION (EXTRACTION
TRANSFORMATION LOADING
21 22.8
Concepts Of Data Integration, Needs And Advantages
Of Using Data Integration, 1
Introduction To Common Data Integration Approaches,
Meta Data - Types And Sources,
1 1
Introduction To Data Quality, Data Profiling Concepts
And Applications,
1 1
Introduction To ETL Using Pentaho Data Integration
(Formerly Kettle)
1 1
UNIT IV - INTRODUCTION TO MULTI-DIMENSIONAL DATA
MODELING
19 20.7
Introduction To Data And Dimension Modeling,
Multidimensional Data Model, 1
ER Modeling Vs. Multi-Dimensional Modeling, ,
Concepts Of Dimensions, Facts, Cubes, Attribute,
1

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Hierarchies, Star And Snowflake Schema
Introduction To Business Metrics And Kpis, Creating
Cubes Using Microsoft Excel 1 1
UNIT V - BASICS OF ENTERPRISE REPORTING
18 19.6
A Typical Enterprise, Malcolm Baldrige - Quality
Performance Framework, Balanced Scorecard,
Enterprise Dashboard,
9
1 1
Balanced Scorecard Vs. Enterprise Dashboard, 1
Enterprise Reporting Using MS Access / MS Excel,
Best Practices In The Design Of Enterprise
Dashboards
1 1
Total 92 100%
A1 – Total weightage
A2 – % of Weightage
19. Mapping syllabus with Bloom’s Taxonomy LOT and HOT:
Lower Order Thinking
R Remembering
Students are expected to Recall the information through Recognizing,
listing, describing, retrieving, naming, finding
U Understanding
Students are expected to Explain an ideas or concepts through
Interpreting, summarizing, paraphrasing, classifying, explaining
Ap Applying
Students are expected to Use the information in another familiar
situation through Implementing, carrying out, using, executing
Higher Order Thinking
A Analyzing
Students are expected to Break the information into parts to explore
understandings and relationships through Comparing, organizing,
deconstructing, interrogating, finding
E Evaluating
Students are expected to Evaluate the Justifying a decision or course of
action through Checking, hypothesizing, experimenting, judging
C Creating
Students are expected to Generate new ideas, products, or ways of
viewing things through Designing, constructing, planning, producing,
inventing.
UNIT I - INTRODUCTION TO BUSINESS INTELLIGENCE (Weightage 26%)
Sl.No Name of the Topic Process verb Types of thinking
1 Introduction to digital data and its types Explain, Discuss
Understanding
CO1
2 Structured, Semi-Structured, Unstructured
Explain, Discuss,
Explore, Depreciate
Applying
CO1
3
Introduction To OLTP and OLAP-
MOLAP,ROLAP& HOLAP
Expose, Infer,
Examine
Analyzing
CO1
R U Ap A E C Total
Type of thinking in Nos 0 1 1 1 0 0 3
Weightage,% 0 6.53 6.53 6.53 0 0 19.6
UNIT II - BUSINESS INTELLIGENCE PROCESS AND FRAMEWORK (Weightage 19%)
Sl.No Name of the Topic Process verb Types of thinking

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1
BI Definitions & Concepts- BI Framework, Data
Warehousing concepts and its role in BI
Define, Explain,
Discuss,
Describe
Understanding
CO2
2
BI Infrastructure Components, BI Process , BI
Technology
Classify,
Compare, Build,
Define, Develop,
Explain, Relate,
Utilize
Applying
CO2
3
BI Roles & Responsibilities , Business
Applications of BI
Analyze,
Examine,
Classify
Analyzing
CO2
R U Ap A E C Total
Type of thinking in Nos 0 1 1 1 0 0 3
Weightage,% 0 5.8 5.8 5.8 0 0 17.4
UNIT III BASICS OF DATA INTEGRATION (EXTRACTION TRANSFORMATION LOADING
(Weightage 21%)
Sl.No Name of the Topic Process verb Types of thinking
1
Concepts Of Data Integration, Needs And
Advantages Of Using Data Integration,
Introduction To Common Data Integration
Approaches, Meta Data - Types And Sources
Classify,
Compare, Build,
Define, Develop,
Explain, Relate,
Utilize
Applying
CO3
2
Introduction To Data Quality, Data Profiling
Concepts And Applications, Introduction To ETL
Using Pentaho Data Integration (Formerly Kettle)
Analyze,
Examine,
Classify, Infer,
Expose,
Categorize
Analyzing
CO3
R U Ap A E C Total
Type of thinking in Nos 0 0 1 1 0 0 2
Weightage,% 0 0 11.4 11.4 0 0 22.8%
UNIT IV INTRODUCTION TO MULTI-DIMENSIONAL DATA MODELING (Weightage 17%)
Sl.No Name of the Topic Process verb Types of thinking
1
Introduction To Data And Dimension Modeling,
Multidimensional Data Model,
Define, Explain,
Discuss,
Describe
Understanding
CO4
2
ER Modeling Vs. Multi-Dimensional Modeling, ,
Concepts Of Dimensions, Facts, Cubes, Attribute,
Hierarchies, Star And Snowflake Schema
Classify,
Compare, Build,
Define, Develop,
Explain, Relate,
Utilize
Applying
CO4
3
Introduction To Business Metrics And Kpis,
Creating Cubes Using Microsoft Excel Analyze,
Examine,
Classify
Analyzing
CO4

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R U Ap A E C Total
Type of thinking in Nos 0 1 1 1 0 0 3
Weightage,% 0 6.9 6.9 6.9 0 0 20.7%
UNIT V UNIT V - BASICS OF ENTERPRISE REPORTING
(Weightage 17%)
Sl.No Name of the Topic Process verb Types of thinking
1 A Typical Enterprise, Malcolm Baldrige - Quality
Performance Framework, Balanced Scorecard,
Enterprise Dashboard,
Classify,
Compare, Build,
Define, Develop,
Explain, Relate,
Utilize
Applying
CO5
2 Balanced Scorecard Vs. Enterprise Dashboard,
Enterprise Reporting Using MS Access / MS Excel,
Best Practices In The Design Of Enterprise
Dashboards
Analyze,
Examine, Classify
Analyzing
CO5
R U Ap A E C Total
Type of thinking in Nos 0 0 1 1 0 0 2
Weightage,% 0 0 9.8 9.8 0 0 19.6%
R U AP A E C TOTAL
UNIT 1 0 6.53 6.53 6.53 0 0 19.6%
UNIT 2 0 5.8 5.8 5.8 0 0 17.4%
UNIT 3 0 0 11.4 11.4 0 0 22.8%
UNIT 4 0 6.9 6.9 6.9 0 0 20.7%
UNIT 5 0 0 9.8 9.8 0 0 19.6%
TOTAL 0 19.23 40.43 40.43 0 0 100 %
Lower Order Thinking 59.6 %
Higher Order Thinking 40.4%
20. Mapping course outcome with Bloom’s Taxonomy LOT and HOT:
R U Ap A E C
CO1   
CO2   
CO3  
CO4   
CO5  

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21. Mapping Course Outcome (CO) with Program Outcomes (PO) and Program
Specific Outcomes (PSO):
Program Outcomes Descriptions
PO1 Engineering knowledge Apply the knowledge of mathematics, science, engineering
fundamentals, and an engineering specialization to the
solution of complex engineering problems.
PO2 Problem analysis Identify, formulate, research literature, and analyze
complex engineering problems reaching substantiated
conclusions using first principles of mathematics, natural
Sciences, and engineering sciences.
PO3 Design/development of
solutions
Design solutions for complex engineering problems and
design system components or processes that meet the
specified needs with appropriate consideration for the
public health and safety, and the cultural, societal, and
environmental considerations.
PO4 Conduct investigations of
complex problems
Use research-based knowledge and research methods
including design of experiments, analysis and interpretation
of data, and synthesis of the information to provide valid
conclusions.
PO5 Modern tool usage Create, select, and apply appropriate techniques, resources,
and modern engineering and IT tools including prediction
and modeling to complex engineering activities with an
understanding of the limitations.
PO6 The engineer and society Apply reasoning informed by the contextual knowledge to
assess societal, health, safety, legal and cultural issues and
the consequent responsibilities relevant to the professional
engineering practice
PO7 Environment and
sustainability
Understand the impact of the professional engineering
solutions in societal and environmental contexts, and
demonstrate the knowledge of, and need for sustainable
development.
PO8 Ethics Apply ethical principles and commit to professional ethics
and responsibilities and norms of the engineering practice.
PO9 Individual and team work Function effectively as an individual, and as a member or
leader in diverse teams, and in multidisciplinary settings.
PO10 Communication Communicate effectively on complex engineering activities
with the engineering community and with society at large,
such as, being able to comprehend and write effective
reports and design documentation, make effective
presentations, and give and receive clear instructions.
PO11 Project management and
finance
Demonstrate knowledge and understanding of the
engineering and management principles and apply these to
one’s own work, as a member and leader in a team, to
manage projects and in multidisciplinary environments
PO12 Life-long learning Recognize the need for, and have the preparation and ability

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to engage in independent and life-long learning in the
broadest context of technological change.
PSO1 An ability to apply, design and develop principles of software engineering, networking
and database concepts for computer-based systems in solving engineering problems
PSO2 An ability to understand, design and code engineering problems using programming
skills.
PO&PSO PO
1
PO
2
PO
3
PO
4
PO
5
PO
6
PO
7
PO
8
PO
9
PO
10
PO
11
PO
12
PSO
1
PSO
2
CO1 3 2 2 3 2
CO2 3 2 2 3 2
CO3 1 2 2 3
CO4 3 2 2 3
CO5 1 3 3 3 2 3
3 High 2 Moderate 1 Low
22. Mapping with Programme Educational Objectives (PEOs):
Programme Educational Objectives:
1. To acquire knowledge in the latest technologies and innovations and an ability to
identify, analyze and solve problems in computer engineering.
2. To be capable of modeling, designing, implementing and verifying a computing system
to meet specified requirements for the benefit of society.
Course PEO1 PEO2
19CS6301 Business
Intelligence- Data
Warehousing and
Analytics
Moderate level High level
3 High level 2 Moderate level 1 Low level
23. Course assessment: (Direct Assessment Method)
Internal test: 15 Marks
Objective To Identify What Students Have Learned and also to identify students strength
and weakness
Product Answer scripts
Frequency Monthly
Format Part –A 6 x 2 = 12 Marks
Part – B 2 x 14 = 28 Marks
Part –C 1 x 10= 10 Marks
Total marks= 50
Duration : 1 Hour and 30 Minutes

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Evaluation Based on answer given in the scripts
criteria Pass mark – 50%
Minimum pass percentage: 50%
If not, remedial action will be taken.
Assignment: 5 marks
Objective To enhance students' understanding of a particular reading
Product Hand written assignment/tutorial sheets
Frequency After completing one unit
Format Important questions from each units
Evaluation Based on rubrics
Criteria No. of assignments: 3
Submit on or before the due date
Attendance: 5 marks
Objective To make all students to attend the class throughout the course
Product Record of class work
Frequency All working days
Format Record of class work format
Evaluation Based on attendance earned by the students
Criteria Marks will be awarded according to attendance percentage of students.
91 and above 5
86 – 90 4
81 – 85 3
75 – 80 2
Less than 75 0
End semester exam: 75 marks
Objective To assess the each student’s knowledge of the course
Product Result analysis
Frequency Every Semester
Format Part –A 10 x 2= 20 marks
Part –B 5 x 14= 70 marks
Part – C 1 x 10 = 10 Marks
Total marks= 100
Duration : 3 Hours
Evaluation Based on answer given in the scripts
Criteria Minimum pass percentage: 50%
If not, remedial action will be taken.
24. Course assessment: (Indirect Assessment Method)
Course Exit Survey: Course Exit Survey consists of few critical questions that evaluate
the level of students’ satisfaction level with curriculum and course being taught.

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Prepared by, Checked by,
Course Coordinator Head of the Department
(Name and Dept.)
Approved by,
Dean (Academics) PRINCIPAL