dat565 | Graduate

Data Analysis And Business Analytics

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This course will introduce students to data discovery, data analytics, and data visualization with contemporary business tools. Students will learn to differentiate data analytics from statistical analysis and use the power of informatics in shaping decision-making in the modern business context. The course will address analytics as a tool for business process-improvement and present data acquisition and formatting, metric creation, data discovery, and data visualization with real-world examples and exercises for students to work through in class.

This graduate-level course is 6 To enroll, speak with an Enrollment Representative.

Course details:

Credits: 3
Continuing education units: XX
Professional development units: XX
Duration: 6

topic title goes here

    Data and Information Visualization

    • Develop conclusions based upon data.
    • Demonstrate conclusions using an infographic or visualization.
    • Formulate the processes that form the foundation for your conclusions.

    Data and Analytics

    • Differentiate between metrics and statistical analysis of information.
    • Explore how analytics is shaping different subject areas of organizations.
    • Discuss the differences between descriptive analytics and predictive analytics.
    • Assess the ethical implications with respect to both individuals and institutions within the scope of data mining and predictive analytics.

    Informatics

    • Compile visual representations of information.
    • Propose how infographics convey information efficiently.
    • Differentiate raw data and information.

    Metrics Data and Process Modeling

    • Analyze the steps of knowledge discovery of data processing.
    • Identify a process for measurement.
    • Tabulate a related set of metrics.
    • Determine how process metrics could determine statistical patterns.

    Data Acquisition and Formatting

    • Discuss the types of information.
    • Identify data types.
    • Explain why a sample of data may not be consistent with the main data type and format.
    • Evaluate an expression to transform data.

    Data Analysis

    • Identify information from a set of sample data.
    • Create alternative explanations for a set of sample data.
    • Analyze data to create metrics with a data software tool.
    • Analyze how process changes might impact metrics.
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