Concepts and Constructs - Design and Complexity Metrics - Productivity Metrics - Quality and Quality Management Metrics Unit V - Conducting In-Process Quality Assessments: Preparation Phase -Evaluation Phase Summarization Phase - Recommendations and Risk Mitigation - Audit and Assessment - Software Process Maturity Assessment and Software Project Assessment - Do's and Don'ts of Software Process Improvement. Text Book: 1. Stephen H. Kan, “Metrics and Models in Software Quality Engineering”, Second Edition, Addison-Wesley Professional, 2003. ISBN: 0201729156. Reference Books: 1. John W. Horch, “Practical Guide to Software Quality Management”, Second Edition, Artech House Computer Library, 2013. ISBN: 0813170324. 2. John C. Munson, “Software Engineering Measurement”, Auerbach Publications, 2003. ISBN: 0849315034. 3. Norman.E. Fenton and James Bieman ,“Software Metrics: A Rigorous and Practical Approach”, Third Edition, Taylor & Francis, 2014. ISBN 1439838224, 9781439838228. 4. Gerald M. Weinberg, “Quality Software Management: Anticipating Change”, Dorset House Publishing Company, 1997. 5. B A Kitchenham, “Software Metrics: Measurement for Software Process Improvement”, Blackwell Pub, 1996. ISBN: 1855548208. 18CA2004 DATA MINING Credits: 3:0:0 Course Objective: To understand Data mining, Kinds of data that can be mined To understand the Data objects and Attribute types To understand Data mining trends Course Outcome: Students will be able to: Describe Data mining, Kinds of data, patterns that can be mined Apply attribute types and statistical distribution of data Determine the different steps followed in Data mining and pre-processing for Data mining Select and Apply proper data mining algorithms to build applications Describe the designing of Data Warehousing Understand and apply the most current data mining trends and applications Unit I - Introduction: Why data mining – Data mining – Kinds of data that can be mined – Database data, data warehouses, Transactional data – Kinds of patterns that can be mined – Technologies used – major issues in data mining Unit II - Data objects and Attribute types: Attribute – Nominal attributes –Binary attributes – Ordinal attributes – Numeric attributes – Discrete versus continuous attributes – Statistical distribution of data - data visualization – measuring data similarity and dissimilarity Unit III - Data pre processing: Overview – Data cleaning – missing values, Noisy data, Data cleaning as a process – Data Integration – entity identification problem, redundancy and correlation analysis – Data reduction – Data transformation and data discretization Unit IV - Data Warehousing: What is Data warehouse – Difference between operational database systems and data warehouses – Data warehouse modeling – Data warehouse design and usage – data warehouse implementation. Unit V - Data mining trends: Mining complex data types – Data mining methodologies - Statistical data mining, Data mining foundation, Visual and Audio data mining - Data mining applications – data mining trends.
INFORMATION TECHNOLOGY
