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Fall 2017
May 08, 2021
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Information Select the Course Number to get further detail on the course. Select the desired Schedule Type to find available classes for the course. The Schedule Type links will be available only when the schedule of classes is available for the selected term.

CS 24200 - Introduction To Data Science
Credit Hours: 3.00. (STAT 24200) This course provides a broad introduction to the field of data science. The course focuses on using computational methods and statistical techniques to analyze massive amounts of data and to extract knowledge. It provides an overview of foundational computational and statistical tools for data acquisition and cleaning, data management and big data systems. The course surveys the complete data science process from data to knowledge and gives students hands-on experience with tools and methods. Basic knowledge of Python required. Computer Science majors cannot count this course as a degree requirement but can take it for credit as a free elective if taken before CS 37300, 34800, 47100, 47300, 44800. Typically offered Fall Spring.
0.000 OR 3.000 Credit hours

Syllabus Available
Levels: Graduate, Professional, Undergraduate
Schedule Types: Distance Learning, Laboratory, Lecture

Offered By: College of Science
Department: Computer Science

Course Attributes:
Lower Division

May be offered at any of the following campuses:     
      West Lafayette

Learning Outcomes: 1. Use Python, R, and selected tools to scrape, clean, process, and visualize data. 2. Apply data management techniques to prepare, parse, manipulate, and store data. 3. Use statistical methods to summarize data and identify relationships. 4. Explain how to formulate new hypotheses and draw accurate conclusions from data. 5. Apply statistics and computational analysis to make predictions based on data. 6. Apply basic computer science concepts such as modularity, abstraction, and encapsulation to data analysis problems. 7. Effectively communicate the outcome of data analysis using descriptive statistics and visualizations.



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