GURU NANAK INSTITUTE OF MANAGEMENT STUDIES

Overbooking

SERVEQUAL Model

Types of service 

Corporate finance is a subfield of finance that deals with how corporations address funding sources, capital structuring, accounting, and investment decisions. Corporate finance is also often concerned with maximizing shareholder value through long- and short-term financial planning and implementing various strategies.

Management is the process of coordinating and overseeing the work of individuals and teams to achieve organizational goals effectively and efficiently

An Introductory course for Talent Acquistion

Parallel Database Distributed Database and
ORDBMS: Architecture for Parallel Databases,
Types of Distributed Databases, Distributed DBMS
Architecture, Storing Data in a Distributed
DBMS.ORDBMS: Structured Data Types, Operations
on Structured Data, Objects,
Inheritance, Object oriented versus Object relational
database.
Self-Learning Topics: Mapping OODBMS to
ORDBMS.

Data warehousing and OLAP:
Data warehouse: Introduction to DW, DW
architecture, ETL Process, Top-down and bottom-up
approaches, characteristics and benefits of data mart.
Dimensional Modeling: Star, snowflake and fact
constellation schema. OLAP in the data warehouse:
Major features and functions, OLAP models-ROLAP
and MOLAP, Difference between OLAP and OLTP
Self-Learning Topics: Study any one DW
implementation

Data Mining and Preprocessing:

Introduction to data mining, Knowledge discovery-
KDD process.

Data Preprocessing: Types of attributes, Data
Cleaning - Missing values, Noisy data, data
integration and transformations.
Data Reduction - Data cube aggregation,
dimensionality reduction, data compression,
Numerosity reduction, discretization and concept
hierarchy.
Self-Learning Topics: Application of data mining in
Business Intelligence.

Module: Data Mining Algorithm- Association
rules:
Association rule mining: support and confidence and
frequent item sets, market basket analysis, Apriori
algorithm, Associative classification- Rule Mining.
Self-Learning Topics: Association Rule Mining
applications

Data Mining Algorithm-Classification:

Classification methods: Statistical-based algorithms-
Linear Regression, Naïve Bayesian classification,

Distance- based algorithm- K Nearest Neighbor,
Decision Tree-based algorithms - ID3, C4.5, CART.
Self-Learning Topics: Comparative study of
classification algorithms.

Data Mining Algorithm-Clustering:
Clustering Methods: Partitioning methods- K-Means,
Hierarchical- Agglomerative (single link) and divisive
methods
Self-Learning Topics: Clustering algorithm
applications.

Web Mining:
Web content mining: crawlers
Web structure mining: Page rank algorithm
Web usage mining: Data structure.
Self-Learning Topic: Text mining.

SERVICES INTANGIBLE

Operations Mgmt throgh mm