CISD201 Data Science
Department of Science, Technology, Engineering & Mathematics: Data Science
- I. Course Number and Title
- CISD201 Data Science
- II. Number of Credits
- 3 credits
- III. Number of Instructional Minutes
- 2250
- IV. Prerequisites
- CISC219 (C or better) and MATH115 (C or better)
- Corequisites
- None
- V. Other Pertinent Information
- A significant portion of the course is dedicated to developing a hands-on proficiency with data science techniques and software tools (such as Python and R). Laboratory work is designed to provide the student with practical experience developing and implementing comprehensive data analysis strategies. A comprehensive final examination will be included in the course. The final will be evaluated at 15 - 25% of the course grade. A minimum of five laboratory assignments and exercises will be required. The laboratory grade will be comprised of no more than one-third of the course grade.
- VI. Catalog Course Description
- This course introduces Data Science using statistics, scientific methods, algorithms, and processes to extract useful insights from large amounts of data. Students analyze hidden patterns buried in raw data along with visualization techniques aimed at making this knowledge understandable.
- VII. Required Course Content and Direction
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Course Learning Goals
Students will:
- use data cleaning and other techniques to process, explore and prepare data for processing;
- analyze raw data using statistical methods and visualization tools to create data models for processing;
- utilize data models to discover relationships, patterns and trends in the underlying data; and
- develop reports and visualization strategies for data analysis.
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Planned Sequence of Topics and/or Learning Activities
- Overview of data science, data sourcing, and preparation
- Data structures and algorithms
- Introduction to machine learning
- Probability and statistical modeling
- Clustering
- Data visualization
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Assessment Methods for Course Learning Goals
Course-specific learning goals are evaluated via test results and problem solving including at least two (2) major projects, at least one (1) substantial research assignment, and a departmental final exam. -
Reference, Resource, or Learning Materials to be used by Student:
Departmentally selected textbook. Details provided by the instructor of each course section. See course syllabus.
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Review/Approval Date - 5/2020