NIT6160 Assessment Answers Australia

Students must pass a range of examinations that assess cognitive, problem-solving, and social skills, as well as a complete understanding of workflow model parameters, business company implementations, and system analysis.

The course NIT6160 covers the many stages of data warehouse creation (design, purchase, maintenance, inquiry, querying, preparation, and dissemination), with a focus on addressing an enterprise's educational and quantitative requirements. It examines the data warehouse and data-mart concepts from differing perspectives (function, layout, usage, and confidentiality) and describes the plan for the project as a data warehouse/data-mart solution, starting with the early stages of categorizing an enterprise's explanatory and analytical requirements and going to the end with the power generation of business analysis by successful in this competitive data from a data warehouse using knowledge representation methodology. The training also looks at how data warehouses may help with strategy and execution.

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Objectives of the Course:

The course aims to make students familiar with:

• Fundamental ideas, concepts, and implementations of data warehousing and data mining.
• Data mining as a key step in the knowledge recovery process.
• Conceptual, logical, and physical architecture of data warehouse OLAP programs and OLAP deployment.
• Essential ideas that form the basis of data mining.

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Learning Outcomes of the NIT6160 Data Warehousing and Mining Course:

After completing the course, students become capable to:

• Determine a company's current most critical information and analytics needs and design a data warehouse solution to meet those needs.
• Perform rudimentary data mining tasks using the data warehouse system.
• Completely overhaul a given organization's operations database(s) and create a data warehouse structure based on satisfying the enterprise's most essential factual and analytical needs.
• Use specific design strategies to address data structure issues in data warehouse project development (data partitioning; denormalization; multivariate, star, and winter design models).
• Data collection issues as well as ETL (Extract, Transform, Load) process issues are addressed. It provides business intelligence by retrieving usable information from the data warehouse.
• Make use of data mining and analysis techniques such as Online Analytical Processing (OLAP), Relationship OLAP (ROLAP), Multidimensional OLAP (MOLAP), Hybrid OLAP, Decision Support Systems (DSS), Executive Information Systems (EIS), and others.
• Assess the effectiveness and usability of data warehouse systems. Data mining is the study of common patterns, relationships, and correlations in data using one or more fundamental data mining techniques.
• Make categorical predictions on fresh incoming data using one or more fundamental data mining techniques. Build a data warehouse, populate it with data, and extract meaningful information from it.
• Discuss the difficulties of employing data warehousing in strategy and execution, assess the expenses, and identify the merits and limits of such a strategy.

Topics Covered in the NIT6160 Data Warehousing and Mining Course

The students in this course have to necessarily study the following topics:

• INTRODUCTION TO DATA MINING
• DATA WAREHOUSING AND ON-LINE ANALYTICAL PROCESSING
• MINING FREQUENT PATTERNS, ASSOCIATIONS AND CORRELATIONS
• CLASSIFICATION:
• CLUSTER ANALYSIS
• OUTLIER ANALYSIS

Reference Books

Our experts have created a list of the following books that may help you build your concepts in-depth:

• Jiawei Han, Micheline Kamber, Jian Pei (2012), Data Mining: Concepts and Techniques, 3^rd edition, Elsevier, United States of America.
• Alex Berson and Stephen J. Smith Data Warehousing, Data Mining OLAP, Tata McGraw Hill Edition, Tenth Reprint 2007.
• P. Soman, Shyam Diwakar and V. Ajay Insight into Data mining Theory and Practice, Easter Economy Edition, Prentice Hall of India, 2006.
• K. Gupta Introduction to Data Mining with Case Studies, Easter Economy Edition, Prentice Hall of India, 2006.
• Pang-Ning Tan, Michael Steinbach and Vipin Kumar Introduction to Data Mining, Pearson Education, 2007

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