Brainstorming Activity: Data Science Course Development
Brainstorming Activity: Course Framework
This document guides instructors through a comprehensive brainstorming process to develop a well-structured data science course aligned with the AUC Data Science and Analytics Framework. Following these steps will help you identify core components, define learning outcomes, and design effective modules that incorporate essential data science competencies.
Step 1: Write a Clear and Concise Course Description
Step 8: Select Data Science Competencies to Integrate
Step 1: Write a Clear and Concise Course Description
The foundation of your course planning begins with a clear, focused description that communicates the essence of your course to potential students and colleagues.
Subject and Focus
Clearly articulate your course's disciplinary focus and how data science methodologies will be integrated within this context.
Target Audience
Specify whether your course is designed for majors, nonmajors, or a mixed audience to set appropriate expectations.
Real-World Applications
Highlight specific tools, datasets, or contemporary issues that students will engage with to solve authentic problems.
Keep your description concise (3-5 sentences) while ensuring it conveys the unique value proposition of your course and how it contributes to students' data science competencies.
Step 2: Justify the Course (Course Rationale)
A compelling course rationale articulates why this course deserves a place in the curriculum right now. This justification should address institutional priorities, identify curriculum gaps, and demonstrate relevance to contemporary issues.
When developing your rationale, consider how your course might:
Align with Strategic Goals
Address specific departmental or institutional strategic goals through thoughtful curriculum design
Support Career Readiness
Support student career readiness or graduate school preparation through practical applications
Fill Curriculum Gaps
Identify and fill gaps in current course offerings to create a more comprehensive program
Respond to Industry Trends
Respond to industry demands or emerging trends in data science to keep education relevant
Connect to Society
Connect to broader societal issues, particularly those relevant to the African diaspora or community needs
Step 3: List Course Prerequisites
Establishing appropriate prerequisites ensures students enter your course with the foundational knowledge needed for success. Clear prerequisite expectations help minimize knowledge gaps and allow for more advanced content delivery.
Technical Prerequisites
Programming languages (Python, R, etc.)
Statistical concepts
Mathematics requirements (calculus, linear algebra)
Prior Coursework
Specific course numbers
Course sequences
Required grade minimums
Alternative Paths
Equivalent experience options
Placement test alternatives
Instructor permission options
If your course is introductory or designed for beginners with no prior data science experience, clearly indicate it is "Open to all" or specify "None" for prerequisites. This clarity is especially important for courses targeting non-majors or those fulfilling general education requirements.
Step 4: Define the Target Audience
Content Calibration
Precisely defining your target audience helps ensure course content, examples, and assignments are appropriately calibrated to student needs and preparation levels. Understanding who will take your course informs everything from technical complexity to contextual examples.
Academic Level & Course Role
Consider both the academic level (undergraduate or graduate) and the role this course plays in students' academic journey (requirement, elective, general education). Additionally, identifying specific majors or programs that might benefit from your course can help with recruitment and crosslisting opportunities.
Career Aspirations
When defining your audience, also consider how the course might serve students with varying career aspirations. For example, a course might simultaneously serve biology majors seeking basic data literacy, computer science students pursuing advanced methods, and social science majors interested in applying data analysis to research questions.
Step 5: Draft 335 Course Objectives
Course objectives articulate the broad goals you intend to accomplish through your teaching. They represent what you, as the instructor, aim to achieve by the end of the term. Using Bloom's Taxonomy can help structure objectives across different cognitive levels.
Be Broad
Objectives should capture overarching course goals rather than specific skills
Use Action Verbs
Begin each objective with verbs like "Develop," "Provide," or "Foster" that align with Bloom's cognitive levels
Connect to Framework
Ensure alignment with AUC Data Science and Analytics competencies
Ensure Measurability
Frame objectives so success can be evaluated
Apply Bloom's Taxonomy
Structure objectives across knowledge, comprehension, application, analysis, synthesis, and evaluation levels
Strong course objectives provide a roadmap for your teaching decisions and help communicate the purpose of your course to students, colleagues, and administrators. Incorporating Bloom's Taxonomy ensures objectives span appropriate cognitive complexity levels.
Student Learning Outcomes differ from course objectives in that they focus specifically on what students will be able to demonstrate by the end of your course. Well-crafted SLOs serve as the foundation for assessment design and help students understand course expectations.
Characteristics of
Effective SLOs
Begin with measurable action verbs (explain, create, analyze)
Focus on observable student behaviors
Address knowledge, skills, and attitudes
Connect directly to assessment strategies
Framework Alignment
Include at least one SLO for each relevant AUC DSA Framework competency
Balance technical and contextual outcomes
Consider ethical dimensions of data science practice
Assessment Connections
Each SLO should be assessable through course assignments
Use varied assessment approaches for different outcome types
Consider both formative and summative assessment options
Step 7: Identify Your Module Topics
Plan Sequential Flow
Organize topics in a logical progression that builds knowledge systematically over the term
Balance Components
Integrate technical skills, applications, ethical considerations, and communication elements
Incorporate Interdisciplinary Themes
Identify connections to other disciplines and real-world applications
Consider Guest Speakers
Note potential experts who could enhance specific modules
Emphasize Local Relevance
Highlight connections to community issues and local datasets
Your module structure should account for the natural rhythm of the academic term, including breaks, exams, and project deadlines. For a typical semester, plan 12-14 content modules with additional time for orientation and final presentations or assessments.
Step 8: Select Data Science Competencies to Integrate
Privacy, bias, fairness, transparency, responsible use
Communication
Data storytelling, presentation, domain translation, collaboration
While comprehensive courses may touch on all six competencies, most courses will emphasize 2-4 areas depending on course level, disciplinary focus, and student preparation. For each competency you select, articulate a specific implementation strategy that connects to your course context and learning outcomes.
AUC Data Science and Analytics Framework Competency Matrix
This matrix provides a comprehensive overview of how the six core competencies of the AUC Data Science and Analytics Framework can be implemented across different course types. Use this reference to identify which competencies best align with your course objectives and to plan for assignment and assessment alignment.
Mathematics & Statistics
Introductory: Basic descriptive statistics, probability concepts
Introductory: Using prepared datasets, basic cleaning
Intermediate: Data collection, transformation techniques
Advanced: Database design, complex ETL pipelines
When selecting competencies, consider how they can be authentically integrated into your disciplinary context rather than treated as isolated skills. The most effective data science courses weave technical competencies with domain knowledge, ethical considerations, and communication practices.
