2CSE50E13: Business Intelligence

Learning Outcomes: 
After learning the course the students should be able to
Design and implement OLTP, OLAP and Warehouse concepts
Design and develop Data Warehouse using Various Schemas & Dimensional modelling
Use the ETL concepts, tools and techniques to perform Extraction, Transformation, and Loading of data
Report the usable data by using various reporting concepts, techniques/tools, and use charts, tables for reporting in BI
Use Analytics concepts like data mining, Exploratory and statistical techniques for predictive analysis in Business Intelligence
Demonstrate application of concepts in BI

Lab Work:

Unit 1: Understand the benefits of IBM Cognos Insight, Drag and drop files to import data, Filter  data and discover associations using Explore Points, Perform a Guided Import from a file, Perform a Guided Import from a relational data source, Refreshdata

Unit 2: Analyze data from different perspectives, Insert totals, Calculate data, Explore chart types, Explore chart options, Determine the optimal chart type to use for your analysis, Add content by  using widgets, Organize your workspace with tabs and action buttons, Improve appearance by applyingthemes

Unit 3: Understand data entry colors and fonts, Control appearance and behavior using formatting, Annotate and calculate data in cells, Send or upload files to other users, Publish a workspace, Export and print data, End to End Workshop

Syllabus: 
Unit NoTopics
Important Concepts

Understanding the field of business intelligence in a global world - Understanding the BI process and choosing –Place and tasks of the study of private and public intelligence The practice of private and public intelligence: the choice of means -Strategies of information gathering, The distinction between intelligence, information and data, Information asymmetry and competitive advantage

Dimensional Modelling and dw Design

Star schema, Snow flake schema, and Fact Constellation schema, Grain of dimensional model, transactions, Recurring Snapshots, Accumulating Snapshots, Dimensions (SCD types, conformed dimensions)Clickstream Source Data (Google Analytics as a Clickstream Data Source), Facts  (additive, semi-additive, non-additive), Hierarchy in dimensions, parent child relationships, Many-Many Dimensional relationship, Multi Valued Dimensions and DimensionAttributes

ETL

Data Quality, Data profiling, Data enrichment, data duplication, ETL Architecture and what is ETL, Extraction concept and Change data capture, Transformation concept, lookups, time lag, formats, consistency, Loading concept, Initial and Incremental loading, late arriving facts, What is Staging, Data marts, Cubes, Scheduling and dependency matrix

Reporting

Metadata Layer, Presentation Layer, Data Layer, Use of different layers    and overall  Reporting  architecture,  Various  report   elements  such  as Charts, Tables, prompts Data aggregation: Table based, Materialized views, Query rewrite, OLAP, MOLAP, Dashboards, Ad-hoc reports, interactivity in  analysis (drill down, drill up), Security: report level, data level (row, column),Scheduling

Analytics

Analytics concepts and use in Business Intelligence, Exploratory and statistical techniques:- Cluster analysis, Data visualization, Predictiveanalysis :- Regression, Time series, Data Mining :- Hierarchical clustering, Decision tree Text analytics :- Text mining, In-Memory Analytics and In-DB Analytics, Case study: GoogleAnalytics

Recent Trends

Big data like HIVE, PIG and DW appliances like Netezza, Teradata, Smart Change data capture using log based techniques, Real time BI, Operational  BI, Embedded BI, Agile BI, BI on cloud, BI applications (Case study on BI tools like: QlikView, Pentaho, Tableau, MyReport, Spotfire, OR any other BI tool)

Text Books: 
Name : 
Data Warehouse
Author: 
Reema Thareja
Publication: 
Oxford University Press
Name : 
Data Mining: concepts and techniques
Author: 
Jiawei Han, Micheline Kamber, Jian
Publication: 
Elsevier/Morgan Kaufmann
Edition: 
2nd Edition
Name : 
The Data Warehouse Toolkit
Author: 
Ralph Kimball, Margy Ross
Publication: 
Wiley
Edition: 
3rd edition
Reference Books: 
Name: 
Building the Data Warehouse
Author: 
William Inmon
Publication: 
Wiley publication
Edition: 
4th edition
Name: 
Decision Support And Data Warehouse Systems
Author: 
Efrem G. Mallach
Publication: 
McGraw-Hill Education
Edition: 
1st Edition
Name: 
Business Intelligence
Author: 
Efraim Turban, Ramesh Sharda, Dursun Delen, David King
Publication: 
Prentice Hall
Name: 
Business Modeling and Data Mining
Author: 
Dorian Pyle
Publication: 
Elsevier Publication MK
Syllabus PDF: 
AttachmentSize
PDF icon ELECTIVE I (BI) .pdf174.66 KB
branch: 
BDA
Course: 
2014
2016
Stream: 
B.Tech