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Table of Contents minLevel 2
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Course Description
Introduces information professionals to fundamentals about data and the standards, technologies, and methods for organizing, managing, curating, preserving, and using data. Discusses broader issues relating to data management, quality control and publication of data.The course introduces students to applied examples of data collection, processing, transformation, management, and analysis to provide students with hands-on introduction to data science experience. Students will explore key concepts related to data science, including applied statistics, information visualization, text mining and machine learning. “R”, the open source statistical analysis and visualization system, will be used throughout the course. R is reckoned by many to be the most popular choice among data analysts worldwide; having knowledge and skill with using it is considered a valuable and marketable job skill for most data scientists.
Credit(s)
3.0
Professor of Record
Jeff Saltz
Audience
Course Syllabus
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Course Details
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Learning Objectives
After taking this course, students will be able to:
- Understand essential concepts and characteristics of data.
- Understand scripting/code development for data management using R and R-Studio.
- Understand principles and practices in data screening, cleaning, and linking.
- Understand communication of results to decision makers.
- Identify a problem and the data needed for addressing the problem.
- Perform basic computational scripting using R and other optional tools.
- Transform data through processing, linking, aggregation, summarization, and searching.
- Organize and manage data at various stages of a project life-cycle.
- Determine appropriate techniques for analyzing data.
Course Syllabus
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