Catalog Course Search Details

 Department:    CS

 Course #:    370

 Credits:    5

 Variable:     No

 IUs:    5

 CIP:    52.0201

 EPC:    n/a

 REV:    2024

 Course Description  

Explore critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data and geographic data, document collections, and social networks. Investigate the workplace implications of ethical and social issues surrounding data analysis including bias and privacy.

 Prerequisite  

Prerequisite: BASM Dept. Chair permission.

Contact Hours (based on 11 week quarter)

Lecture: 55

Lab: 0

Other: 0

Systems: 0

Clinical: 0

Intent: Distribution Requirement(s) Status:  

Vocational Preparatory N/A  

Equivalencies At Other Institutions

Learning Outcomes

After completing this course, the student will be able to:

1. Recognize the benefits and limits of computing technology for data analysis and problem solving by examining its application in their field of professional interest.
2. Implement sequence, selection, and iteration by design an algorithm that solve a workplace problem in their field of professional interest.
3. Make accurate predictions by using statistical methods (confidence intervals, regression, hypothesis testing) and a programing language such as Python, to address workplace problems.
4. Demonstrate accurate representation of data such as histograms, bar charts, and box plots by using contemporary data visualization tools such as a Python library or Tableau.

General Education Learning Values & Outcomes

Revised August 2008 and affects outlines for 2008 year 1 and later.

Course Contents

1. Benefits and limits of computing technology for data analysis and problem solving.
2. Sequence, selection, and iteration by design an algorithm that solve a workplace problem in their field of professional interest.
3. Accurate predictions by using statistical methods (confidence intervals, regression, hypothesis testing) and a programing language such as Python, to address workplace problems.
4. Accurate representation of data such as histograms, bar charts, and box plots by using contemporary data visualization tools such as a Python library or Tableau.