im310 | undergraduate

Data Analytics And Modeling

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This course will provide a business perspective on how data is retrieved, formatted, and analyzed to make business decisions.

This undergraduate-level course is 5 This course is available to take individually or To enroll, speak with an Enrollment Representative.

Course details:

Credits: 3
Continuing education units: XX
Professional development units: XX
Duration: 5

topic title goes here

    Building Models

    • 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.
    • Compare supervised (directed) data mining versus unsupervised (undirected) data mining.

    Deploying Models

    • 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.

    Assessing Results and Applied Experience

    • Assess the impact of data mining models on business decisions using various assessment techniques.
    • Describe the iterative nature of modeling.
    • Determine a business’ typical data mining organization, roles, and infrastructure.

    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.
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