Syllabus Business Intelligence - (410253(C)) Credit Examination Scheme : 03 In-Sem (Paper) : 30 Marks End-Sem (Paper) : 70 Marks Unit I Introduction to Decision support systems and Business intelligence Decision support systems : Definition of system, representation of the decision-making process, evolution of information systems, Decision Support System, Development of a decision support system, the four stages of Simon‘s decision-making process and common strategies and approaches of decision makers Business Intelligence : BI, its components and architecture, previewing the future of BI, crafting a better experience for all business users, End user assumptions, setting up data for BI, data, information and knowledge, The role of mathematical models, Business intelligence architectures, Ethics and business intelligence (Chapter - 1) Unit II The Architecture of DW and BI BI and DW architectures and its types - Relation between BI and DW - OLAP (Online analytical processing) definitions - Different OLAP Architectures - Data Models-Tools in Business Intelligence - Role of DSS, EIS, MIS and digital Dash boards - Need for Business Intelligence 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. (Chapter - 2) Unit III Reporting Authoring Building reports with relational vs Multidimensional data models; Types of Reports - List, crosstabs, Statistics, Chart, map, financial etc; Data Grouping and Sorting, Filtering Reports, Adding Calculations to Reports, Conditional formatting, Adding Summary Lines to Reports. Drill up, drill- down, drill-through capabilities. Run or schedule report, different output forms - PDF, excel, csv, xml etc. (Chapter - 3) Unit IV Data preparation Data validation : Incomplete data, Data affected by noise. Data transformation : Standardization, Feature extraction. Data reduction : Sampling, Feature selection, Principal component analysis, Data discretization. Data exploration : 1. Univarate analysis : Graphical analysis of categorical attributes, Graphical analysis of numerical attributes, Measures of central tendency for numerical attributes, Measures of dispersion for numerical attributes, Identification of outliers for numerical attributes 2. Bivariate analysis : Graphical analysis, Measures of correlation for numerical attributes, Contingency tables for categorical attributes, 3. Multivariate analysis : Graphical analysis, Measures of correlation for numerical attributes (Chapter - 4) Unit V Impact of Machine learning in Business Intelligence Process Classification : Classification problems, Evaluation of classification models, Bayesian methods, Logistic regression. Clustering : Clustering methods, Partition methods, Hierarchical methods, Evaluation of clustering models. Association Rule : Structure of Association Rule, Apriori Algorithm (Chapter - 5) Unit VI BI Applications Tools for Business Intelligence, Role of analytical tools in BI, Case study of Analytical Tools : WEKA, KNIME, Rapid Miner, R; Data analytics, Business analytics, ERP and Business Intelligence, BI and operation management, BI in inventory management system, BI and human resource management, BI Applications in CRM, BI Applications in Marketing, BI Applications in Logistics and Production, Role of BI in Finance, BI Applications in Banking, BI Applications in Telecommunications, BI in sales force management (Chapter - 6)
