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DATA MINING AND
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2170715
Code: 2170715
Credits: 5
Semester 7
Information Technology
Data Mining and Business Intelligence
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Data Mining and Business Intelligence Syllabus — Unit-wise Content
The official GTU curriculum for Data Mining and Business Intelligence (subject code 2170715) as prescribed for Information Technology · Semester 7 divides the course into 8 units, covering 39 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (2170715)Unit 1 Overview and concepts Data Warehousing and Business Intelligence 05 Hours 12%
- Why reporting and Analysing data
- Raw data to valuable information-Lifecycle of Data - What is Business Intelligence - BI and DW in today’s perspective - What is data warehousing - The building Blocks: Defining Features - Data warehouses and data 1marts - Overview of the components - Metadata in the data warehouse - Need for data warehousing - Basic elements of data warehousing - trends in data warehousing
Unit 2 The Architecture of BI and DW 07 Hours 16%
- BI and DW architectures and its types - Relation between BI and DW - OLAP (Online analytical processing) definitions - Difference between OLAP and OLTP - Dimensional analysis - What are cubes? Drill-down and roll-up - slice and dice or rotation - OLAP models - ROLAP versus MOLAP - defining schemas: Stars
- snowflakes and fact constellations
Unit 3 Introduction to data mining (DM) 04 Hours 08%
- Motivation for Data Mining - Data Mining-Definition and Functionalities – Classification of DM Systems - DM task primitives - Integration of a Data Mining system with a Database or a Data Warehouse - Issues in DM – KDD Process
Unit 4 Data Pre-processing 07 Hours 16%
- Why to pre-process data? - Data cleaning: Missing Values
- Noisy Data - Data Integration and transformation - Data Reduction: Data cube aggregation
- Dimensionality reduction - Data Compression - Numerosity Reduction - Data Mining Primitives - Languages and System Architectures: Task relevant data - Kind of Knowledge to be mined - Discretization and Concept Hierarchy
Unit 5 Concept Description and Association Rule Mining 07 Hours 16%
- What is concept description? - Data Generalization and summarization-based characterization - Attribute relevance - class comparisons Association Rule Mining: Market basket analysis - basic concepts - Finding frequent item sets: Apriori algorithm - generating rules – Improved Apriori algorithm – Incremental ARM – Associative Classification – Rule Mining
Unit 6 Classification and Prediction 07 Hours 16%
- What is classification and prediction? – Issues regarding Classification and prediction: Classification methods: Decision tree
- Bayesian Classification
- Rule based
- Neural Network Prediction methods: Linear and nonlinear regression
- Logistic Regression Introduction of tools such as DB Miner /WEKA/DTREG DM Tools
Unit 7 Data Mining for Business Intelligence Applications 04 Hours 08%
- Data mining for business Applications like Balanced Scorecard
- Fraud Detection
- Clickstream Mining
- Market Segmentation
- retail industry
- telecommunications industry
- banking & finance and CRM etc.
- Data Analytics Life Cycle: Introduction to Big data Business Analytics - State of the practice in analytics role of data scientists Key roles for successful analytic project - Main phases of life cycle - Developing core deliverables for stakeholders
Unit 8 Advance topics 04 Hours 08%
- Introduction and basic concepts of following topics
- Spatial mining
- web mining
- text mining
- Big Data: Introduction to big data: distributed file system – Big Data and its importance
- Four Vs, Drivers for Big data
- Big data analytics
- Big data applications
- Algorithms using map reduce
- Matrix-Vector Multiplication by Map Reduce
- Introduction to Hadoop architecture: Hadoop Architecture
- Hadoop Storage: HDFS
- Common Hadoop Shell commands
- Anatomy of File Write and Read.
- Secondary NameNode, and DataNode
- Hadoop MapReduce paradigm
- Map and Reduce tasks, Job, Task trackers - Cluster Setup – SSH & Hadoop Configuration – HDFS Administering – Monitoring & Maintenance
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