Course Curriculam
Course Code: IT402 Credit Units L T P/S SW AS/DS FW No. of PSDA Total Credit Unit
Course Level UG 3 0 0 2 0 0 0 4
Course Title Data Mining and Business Intelligence
Course
Description :
Course Objectives :
SN
Objectives
.
The purpose of this course is to introduce the basic data mining technologies and their use for business intelligence. The objective of this course is to
1 teach the students how to analyze the business needs for knowledge discovery in order to create competitive advantages and to apply data mining
technologies appropriately in order to realize their real business value
Pre-Requisites : General
SN. Course Code Course Name
Course Contents / Syllabus :
SN. Module Descriptors / Topics Weightage
Introduction: DM and KDD process Integration of a data mining system with a database or a data warehousing
Introduction to
understanding, Supervised and unsupervised learning. BI and DW architectures and its types - Relation between
Data Mining
1 BI and DW - OLAP (Online analytical processing) definitions - Difference between OLAP and OLTP - Dimensional 25.00
and Business
analysis – data cube representations, Drill-down and roll-up - slice and dice or rotation - OLAP models - ROLAP
Intelligence
versus MOLAP - defining schemas: Stars, snowflakes and fact constellations, case studies.
Data Pre-Processing: What kinds of data can be mined, Data Cleaning: Missing Values, Noisy Data,(Binning,
Data Clustering, Regression),Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube
2 15.00
Preprocessing Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept
hierarchy generation, case studies.
Association Association rules: Introduction to market basket analysis, Large Item sets, Basic APRIORI AND FP Tree
3 and Clustering Algorithms Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitioned Algorithms. 20.00
algorithm Hierarchical Clustering Based Methods-DBSCAN, case studies.
Classification What is Classification & Prediction, Issues regarding Classification and prediction, Decision tree, Bayesian
4 and Classification, Classification by Back propagation, K-nearest neighbor classifiers, support vector machine, 20.00
Predictions regression (linear and logistic regression) , case studies.
Data Mining BI Architecture, Introduction to Business analytical tool (Power BI, LIS) spread sheets, concept of dashboard,
for Business OLAP, decision engineering, Data mining for business Applications like Balanced Scorecard, Fraud Detection,
5 20.00
Intelligence Click stream Mining, Market Segmentation, Retail industry, Telecommunications Industry, Banking & Finance and
Applications CRM etc, case studies.
Course Learning Outcomes :
SN. Course Learning Outcomes
1 Examine the types of the data to be mined and present a general classification of tasks and primitives to integrate a data mining system.
2 Discover interesting patterns from large amounts of data to analyze and extract patterns to solve problems, make predictions of outcomes
3 Apply and analyze data mining for Business Intelligence Application.
4 Understand business problems by identifying opportunities to derive business value from data.
5 Identify the data mining techniques and how they can be applied to extract relevant business intelligence.
Pedagogy for Course Delivery :
SN. Pedagogy Methods
1 Understanding of basic concepts of Database Management System and Algorithms and Data Structures
Theory /VAC / Architecture Assessment (L,T & Self Work): 100.00 Max : 100
Attendance+CE+EE : 5+35+60
SN. Type Component Name Marks
1 Attendance 5.00
2 End Term Examination (OMR) 60.00
3 Internal MID TERM EXAM 15.00
4 Internal PRESENTATION 10.00
5 Internal INTEGRATED PROJECT 10.00
Lab/ Practical/ Studio/Arch. Studio/ Field Work Assessment : 0.00 Max : 100
N/A
List of Professional skill development activities :
No.of PSDA : 2
SN. PSDA Point
1 Integrated Project
2 Case Based Presentations
Text & References :
SN. Type Title/Name Description ISBN/ URL
• Data Warehousing Fundamentals for IT
1 Book Professionals, Paulraj Ponniah, Willey 2nd
Edition.
• Business Intelligence: Practices,
2 Book Technologies, and Management- Rajiv
Sabherwal , Irma Becerra-Fe
• Data Warehousing, Reema Thareja,
3 Book
Oxford University Press, 2009 Edition
• Data Mining: Concepts and Techniques,
4 Book J.Han, M.Kamber, Academic Press,
Morgan Kanf man Publishers
• Data Warehousing, Data Mining & OLAP,
5 Book Alex Berson and Stephen J. Smith, Tata
McGraw-Hill Edition,
• Data Mining, VikramPudi and P. Radha
6 Book Krishna, Oxford University Press, 2009
Edition
