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Primary Alignments

Primary Alignment competencies have strong, direct alignment across multiple courses.

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• Critical Thinking 

  The move from raw data to defensible insight is the core intellectual activity of this certificate. Activities should make that reasoning process explicit and evaluable, not just measure whether students got the right answer. The professional value is in the quality of the thinking, not just the output.

  Activity type	Example assignment	Suggested courses
  Data Source Evaluation Students assess a set of datasets for quality, bias, completeness, and appropriate use before any analysis begins. Builds the professional habit of interrogating data before trusting it — a discipline that distinguishes rigorous analysts from careless ones.	Should You Trust This Data? Provide students with three datasets on the same topic collected through different methods (a survey, an administrative database, a scraped web dataset). Students evaluate each on four criteria: how was it collected, who collected it and why, what is missing or potentially biased, and what conclusions would be defensible versus overreaching using this data. Students write a 1-page recommendation memo: which dataset would you use for a professional analysis and why? What caveats would you disclose? This mirrors the data vetting process that analysts, journalists, and researchers perform before every project.	ISCI 301, ISCI 310, MGSC 291, JOUR 400, STAT 201/205/206
  Insight Memo from Data Students are given a dataset or visualization and must produce a professional insight memo: what does this data actually show, what are the most important findings, what does it not show, and what decision or action should it inform? Emphasis on the interpretive step that connects data to judgment.	What Does This Actually Tell Us? Provide a dataset or a set of charts from a real-world source (city budget data, public health statistics, business performance metrics, geographic distribution data). Students write a 1-page insight memo structured as: key finding (1 sentence), supporting evidence (2-3 specific data points), limitations (what this data cannot tell us), and recommendation (what a decision-maker should do with this). Peer reviewers evaluate: Is the key finding defensible from the data shown? Are the limitations honestly stated? Would a professional trust this memo? This exercise is reusable with any dataset and builds the analytical writing skill employers consistently identify as rare.	ISCI 310, MGSC 291, STAT 301/515, GEOG 554, GEOG 564, JOUR 346
  Competing Interpretations Exercise Students are given a single dataset and asked to produce two plausible but conflicting interpretations of what it shows. They then evaluate which interpretation is better supported and explain why. Builds the critical habit of recognizing that data rarely speaks for itself.	Two Stories, One Dataset Students receive a dataset with ambiguous findings (crime statistics that could support either a safety improvement or an enforcement bias narrative, sales data that could indicate growth or seasonal volatility, demographic data that could signal opportunity or displacement). They write two 1-paragraph interpretations, each internally defensible, then a 1-paragraph evaluation of which interpretation is more strongly supported and why. Debrief asks: what would each interpretation require you to ignore or explain away? This develops the intellectual honesty that separates credible analysts from those who fit data to a conclusion.	ISCI 310, STAT 201/205/206, MGSC 291, JOUR 400, GEOG 554

   

• Technology

  Students in this certificate are building hands-on fluency with real tools. Activities should go beyond using tools correctly toward using them purposefully: selecting the right tool, understanding its limitations, and producing outputs that a professional would trust and act on.

  Activity type	Example assignment	Suggested courses
  Tool Application Project Students complete a defined analytical or visualization task using a course tool and submit the output alongside a brief professional rationale: why this tool, what decisions were made, what the output shows, and what a professional would need to know before using it.	Build It and Brief It  Students use a course tool (a GIS platform, a statistical software package, a visualization tool, a programming environment) to complete a real analytical task with provided data. They submit the output (a map, a chart, a model, a report) and a 1-page technical brief: what tool was used and why, what analytical decisions were made and why, what the output shows, and what limitations a professional should disclose before presenting this work. Graded equally on the output and the rationale. This reinforces that professional data work requires judgment, not just technical execution.	GEOG 263, GEOG 345, GEOG 554, GEOG 563, CSCE 106, CSCE 207, MGSC 290, ISCI 560
  Tool Selection Challenge Students are given a data problem and must evaluate two or three tools that could address it, recommend one, and justify the choice. Builds the professional judgment of selecting tools based on the task rather than defaulting to what is familiar.	Which Tool Fits This Problem? Present a professional scenario: a public health department needs to map disease incidence across counties; a media outlet wants to visualize survey results for a general audience; a business wants to identify patterns in customer transaction data. Students evaluate two or three tools for fit, cost, learning curve, output quality, and professional adoption in that field. They produce a 1-page recommendation memo as if briefing a manager who needs to make a purchasing or workflow decision. Debrief covers: how do professionals in this field actually make tool decisions, and what criteria matter most?	CSCE 207, CSCE 567, GEOG 263, MGSC 290, MGSC 394, ISCI 560
  Workflow Documentation Exercise Students document an analytical workflow clearly enough that a colleague could reproduce it: data source, cleaning steps, analytical method, tool used, and output. Builds the reproducibility discipline that professional data environments require.	Could Someone Else Reproduce This? After completing any analytical project, students write a 1-page workflow documentation: data source and how it was accessed, any cleaning or preparation steps performed, the analytical method applied and why, the tool used and any settings or parameters that matter, and the output produced. A peer attempts to follow the documentation to replicate the result. Feedback focuses on: were there steps missing? Were the decisions documented clearly enough to defend? This mirrors the reproducibility standards in data journalism, research, and business analytics and is a direct professional skill most students never practice explicitly.	CSCE 106, CSCE 207, ISCI 560, GEOG 554, MGSC 394, STAT 301

• Communication

  Data visualization is a communication act. Every design decision — color, scale, chart type, annotation, label — is an argument about what matters and what the audience should conclude. Activities should treat visual design as purposeful communication, not decoration, and require students to justify their choices.

  Activity type	Example assignment	Suggested courses
  Visualization Critique and Redesign Students analyze an existing data visualization for communication effectiveness: what it communicates well, where it misleads or confuses, and how it could be redesigned to serve its audience more honestly and clearly.	Fix This Chart Provide a flawed or misleading data visualization from a real source (a truncated Y-axis, a misleading pie chart, an overcrowded infographic, a color scheme that obscures comparison). Students write a 1-page critique: what is this visualization trying to communicate, what design choices undermine that goal, and what changes would improve it? They then produce a redesigned version using course tools with a 1-paragraph explanation of each change made. Debrief asks: who benefits from a misleading visualization and who is harmed? This connects visual design directly to the ethical and professional standards of data communication.	JOUR 203, JOUR 346, ARTS 102, ARTS 245, ISCI 250, GEOG 341
  Audience-Calibrated Data Presentation Students present the same data finding to two different audiences: one technical and one non-technical. They must adjust vocabulary, level of detail, chart type, and framing for each audience and explain the reasoning behind each adjustment.	Same Data, Two Audiences Students analyze a dataset and produce two presentations of their key finding: a 1-page technical summary for a data-literate colleague (including methodology, confidence levels, and analytical caveats) and a 1-paragraph plain-language summary for a general audience or decision-maker (leading with the implication, not the method). They write a brief reflection on what they changed across versions and why. Peer evaluators assess each version against its intended audience: would a data analyst trust the technical version? Would a non-expert understand and act on the plain-language version? This is one of the most direct career-readiness exercises in the certificate.	JOUR 346, JOUR 400, MGSC 394, ISCI 310, ISCI 560, GEOG 341
  Data Story Construction Students develop a data-driven narrative with a clear story structure: context, conflict, finding, implication. The exercise treats data not as a collection of facts but as the raw material for a purposeful argument addressed to a specific audience.	Find the Story in the Data Students receive a dataset relevant to a real-world issue (housing, health, economic mobility, environmental change) and must construct a data story with four components: the context (why this matters), the question (what the data was used to investigate), the finding (what it shows, with specific data points), and the implication (what a specific audience should do or understand differently because of this). Final output is a 1-2 page written narrative or a short visual presentation. Debrief asks: what story choices did you make and what did you leave out? What would a different analyst emphasize? Connects data journalism and visual communication disciplines around a shared framework.	JOUR 203, JOUR 346, JOUR 400, ISCI 250, GEOG 341, MGSC 394

Supporting Alignments

Supporting Alignment competencies have meaningful, specific alignment. Faculty may find activities in supporting competency sections useful for reinforcing career readiness in targeted ways without overloading the course.

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• Professionalism

  Data professionalism is about the standards that govern how data work is done: accuracy, transparency about limitations, ethical use, and accountability for outputs that inform real decisions. Activities should surface the professional stakes of data work and build the habit of honest, defensible practice.

  Activity type	Example assignment	Suggested courses
  Data Ethics Scenario Analysis Students are given a professional data scenario with ethical dimensions and must identify the professional standards at stake, evaluate the choices made, and recommend what a responsible professional should do.	What Are the Professional Obligations Here? Present a scenario: a data analyst discovers that a visualization their organization has been publishing contains a methodological error that changes the conclusion. Correcting it will embarrass the organization and may affect a public policy decision. What are the analyst's professional obligations? Students write a 1-page response: what professional standards apply, what are the competing pressures, and what should the analyst do? Debrief covers the professional norms around error disclosure, data integrity, and the responsibility that comes with producing work that others rely on. Connects directly to JOUR 400 and ISCI 301 content.	JOUR 400, ISCI 301, ISCI 310, MGSC 291
  Limitations and Uncertainty Disclosure Students practice explicitly disclosing the limitations and uncertainties in their own analytical work — a professional habit that data science, journalism, and research all require but that students rarely develop without explicit instruction.	What This Analysis Cannot Tell You After completing any analytical project, students write a 1-paragraph limitations statement as if it would appear in a professional report or published piece: what are the boundaries of this analysis, what does the data not capture, what alternative explanations remain possible, and what would a decision-maker need to know before acting on this finding? Peer reviewers evaluate: is this honest and complete? Would a skeptical professional trust a report that included this disclosure? This exercise builds the intellectual honesty and professional accountability that distinguish credible data practitioners	ISCI 310, MGSC 291, STAT 301/515, JOUR 400, GEOG 564

   

• Career and Self-Development

  Students completing this certificate are building a data skill set that applies across industries — but the career value depends on their ability to articulate it specifically. Activities should help students name what they built, connect it to real job market demand, and frame it for a specific career direction.

  Activity type	Example assignment	Suggested courses
  Portfolio Entry Reflection At the end of a major assignment, students write a structured reflection connecting the work to a NACE competency, a career-relevant skill, and a specific interview talking point. Builds the habit of translating analytical work into professional language.	What Did I Just Build? After any significant project, students complete: (1) What competency or skill did this assignment develop? (2) Name a job or role where this skill is valued. (3) Write one sentence you could say in an interview about this experience. Faculty collect these over the course of the certificate. Students accumulate a set of interview-ready talking points grounded in actual coursework — directly usable in a career advising appointment, on a resume, or in a LinkedIn profile.	All certificate courses
  Data Career Field Scan Students research how data analytics and visualization skills are being used and valued in a career field of interest. They identify specific tools, roles, and employer expectations that connect to what they are learning in the certificate.	Data Skills in My Field Students select a career field and produce a 1-page brief: What data tools are standard in this field? What roles involve data analytics or visualization at the entry level? Which courses in this certificate most directly prepare me for those roles? What experience or skills would I still need to build? This connects coursework to real labor market expectations and gives students a concrete answer to the interview question: why did you pursue this certificate? Works especially well mid-certificate when students can see how their pathway choices align with their career direction.	ISCI 310, MGSC 290, GEOG 263, JOUR 203, all certificate courses

   

• Teamwork

  Data projects in professional settings are rarely solo endeavors. Activities should give students structured experience coordinating across the different roles in a data team — analyst, visualizer, domain expert, communicator — and build the habits of handoff, documentation, and shared decision-making that collaborative data work requires.

  Activity type	Example assignment	Suggested courses
  Cross-Role Data Project Teams produce a shared data output with differentiated roles: one student leads the analysis, one leads the visualization, one leads the written interpretation. Each role has defined responsibilities and a structured handoff point. Builds the coordination habits of real data teams.	One Output, Three Roles Teams of three are assigned a dataset and a professional scenario. Roles are assigned: analyst (clean the data, run the analysis, document the methodology), visualizer (design the charts or maps that communicate the key findings), writer (produce the insight memo that translates the findings for a non-technical audience). Each role submits individual work plus a brief role reflection: what did you contribute, where did handoffs break down, what would you do differently? Whole-team debrief asks: how do professional data teams coordinate across these functions, and where are the most common failure points?	MGSC 290, MGSC 394, JOUR 400, ISCI 310, GEOG 341
  Peer Data Review Students exchange analytical work and provide structured professional feedback: evaluating not just the output but the methodology, the interpretation, and the communication. Mirrors the peer review process used in data journalism, research, and business analytics.	Review This Analysis Students exchange a completed analytical project with a peer. Reviewers evaluate using a structured rubric: Is the data source credible and appropriate? Is the methodology sound? Are the conclusions supported by the data? Are the limitations disclosed? Is the visualization or presentation clear and accurate? Writers receive written feedback and submit a brief revision note: what did you change based on the review and why? This mirrors the editorial and peer review processes that professional data work actually goes through and builds the collaborative accountability that data teams depend on.	ISCI 310, MGSC 291, JOUR 400, GEOG 554, STAT 301

A Note on Integration 

These activities are designed to be additive, not burdensome. A few principles for integration: 

• One well-designed reflection activity per unit is more valuable than multiple low-engagement checkboxes. Depth over volume. 
• Name the competency explicitly. When students know they are developing Critical Thinking through data interpretation or Communication through visualization design, they can represent it. Tell them what they are building. 
• Connect to the job market concretely. Employers in this field ask about this exact skill is more motivating than abstract competency language. 
• The portfolio entry reflection (Career and Self-Development section) and the workflow documentation exercise (Technology section) are the two highest-leverage additions. One builds career self-awareness; the other produces a professional habit that directly transfers to any data role. 
• Faculty interested in connecting these activities to the Carolina Career Ready campus network or the Career Champions program are encouraged to reach out to the USC Career Center.