im310 | undergraduate
Data Analytics And Modeling
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Assessing Results and Applied Experience
- Determine a businessâ typical data mining organization, roles, and infrastructure.
- Assess the impact of data mining models on business decisions using various assessment techniques.
- Describe the iterative nature of modeling.
Business Data Analytics Concepts
- Compare data modeling and data mining.
- Describe the levels of analytics evolution.
- Identify the business problem or opportunity.
- Examine two data mining methodologies used in industry: CRISP-DM and SEMMA.
- Share examples of data mining in various functional areas of a business.
Data Understanding and Data Preparation
- Define metadata.
- Identify source systems to create data sets for data mining.
- Explain the Extract, Transform, and Load (ETL) process.
- Identify steps used to explore data.
- Compare Data Warehousing, Big Data, Online Analytical Processing (OLAP), and data mining.
- Compare supervised (directed) data mining versus unsupervised (undirected) data mining.
- Identify the different types of data mining models to use to solve a business problem in various industries/ business areas.
- Assign the appropriate data mining technique to solving the business problem.
- Name various software tools used for data modeling and data mining.
- Identify how the data mining model results are for solving a business problem.
- Identify how the analysis results are provided to the end user.
- Discuss the role of business users and information technology professionals for the successful deployment of the data mining models.
- Share various visual communication tools.
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