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 3163209) as prescribed for ICT · Semester 6 divides the course into 7 units, covering 34 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 (3163209)Unit 1 Overview and concepts Data Warehousing (DW) and Business Intelligence (BI)
- 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 marts - Overview of the components - Metadata in the data warehouse - Need for data warehousing - Basic elements of data warehousing - trends in data warehousing
- Relation between BI and DW - OLAP (Online analytical processing) definitions - Difference between OLAP and OLTP - Dimensional analysis
Unit 2 Introduction to data mining (DM)
- 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 3 Data Pre-processing
- 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 4 Concept Description and Association Rule Mining
- 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 Subject Code: 3163209 w.e.f
Unit 5 Classification and Prediction
- 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 6 Data Mining for Business Intelligence Applications
- Data mining for business Applications like Fraud Detection
- Market Segmentation
- Banking & Finance and CRM etc
Unit 7 Advance topics
- Introduction and basic concepts of - Clustering
- Spatial mining
- Temporal 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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