Course Content and Outcomes Guides (CCOG)

CCOG for STAT 108 archive revision 202701

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Effective Term:
Winter 2027

Course Number:
STAT 108
Course Title:
Explorations in Data Science
Credit Hours:
4
Lecture Hours:
30
Lecture/Lab Hours:
20
Lab Hours:
0

Course Description

Introduces concepts present in multiple fields of study devoted to understanding and using data. Explores appropriate methodologies for data organization, visualization, and reporting to gain a deeper understanding of the crucial role of data in society. Emphasizes preparation for ethical and informed engagement with data both in future courses and in life. Audit available.

Intended Outcomes for the course

Upon successful completion of the course, students should be able to: 

  1. Utilize appropriate technology, including programing and spreadsheets, to organize, validate, visualize, and analyze data.

  2. Apply the data science cycle to develop and answer statistical questions about univariate, bivariate, and multivariate data.

  3. Create clear and compelling communications in multiple formats, including data visualizations with supporting narratives, for specific audiences.

  4. Evaluate data practices and decision making with regards to ethics and best practices.

Quantitative Reasoning

Students completing an associate degree at Portland Community College will be able to analyze questions or problems that impact the community and/or environment using quantitative information.

General education philosophy statement

The Data Science Cycle is a multi-step mathematical process that requires mathematical reasoning to develop appropriate conclusions about data. Data visualizations are mathematical models. Students will analyze and create these models throughout the course. Students will analyze relevant real world data and prepare to engage ethically with data to be a responsible member of society.

Outcome Assessment Strategies

At least one project culminating in a product presenting data to an audience, such as a presentation, a poster, a dashboard, a written report, or similar that includes authentic individual assessment.

Additionally, at least two of the following additional measures:

  • Quizzes and/or examinations (group or individual)

  • Projects (group and/or individual)

  • Worksheets/graded homework

  • Online homework

  • Group or individual activities

  • Portfolios

Optional additional assessment strategies may include, but are not limited to

  • Individual student conference

  • Discussions

  • Participation

Course Content (Themes, Concepts, Issues and Skills)

Course Content

  • Data collection using manual or technological processes

  • Data ethics, data privacy, and professionalism

  • Engage with a database through a data dashboard 

  • Large data set cleaning and organization

  • Univariate, bivariate, and multivariate data analysis and visualization using spreadsheets, data dashboards, and a data optimized computing language (such as Python or R)

  • Regression and correlation with multiple variables

  • Presentation of data to an audience