IA 342: Visualization Methods, Technologies, and Tools for Intelligence Analysis

Syllabus - Fall 2026

Course: IA 342: Visualization Methods, Technologies, and Tools for Intelligence Analysis
Term: Fall 2026
Instructor: Dr. Xuebin Wei
Email: weixx@jmu.edu
Profile: Official JMU Faculty Profile
Office Hours: Monday and Wednesday, 9:30–11:00 AM

Course Description

IA 342 focuses on Data Visualization, Business Intelligence, Visual Analytics, and Data Storytelling. While AI can analyze data for us, humans still make decisions; visualization supports human perception, judgment, communication, and accountability. Throughout this course, we will use industry-standard tools (ArcGIS, Tableau) to transform raw data into interactive dashboards and clear, evidence-based visual communications.

Learning Goals

Perceive patterns and insights through exploratory data visualization.
Design interactive visual analytics dashboards that support human judgment.
Master industry-standard Business Intelligence platforms such as Tableau and ArcGIS.
Communicate evidence-based data stories to diverse stakeholders.
Maintain accountability and interpretability in data-driven analytical workflows.

Required Accounts & Resources

Textbook: Wei, Xuebin, and Xinyue Ye. Social Data Analytics in the Cloud with AI. CRC Press / Routledge, 2024.
Access: Official Textbook URL (Freely accessible via JMU Wi-Fi or by logging in with a JMU account.)
Google Account (Required): Students must register/create a Google account using their official James Madison University email address (@dukes.jmu.edu or @jmu.edu). This is required for the instructor to grant access to the shared Google Cloud credits pool and enroll students in Google AI Skills modules.
ArcGIS & Tableau (Required): Provided through JMU institutional licensing and SSO.
AI Tools (Optional / Recommended): Generative AI tools (such as Google Gemini, ChatGPT, Claude, GitHub Copilot) may be used as analytical assistants.
- Policy Update: Activating paid/student tiers (such as the Gemini Student Plan) is completely optional.
- Grading: Having or not having AI Pro / advanced tiers will have ZERO impact on your course grade.
- Privacy: If you choose to activate student promotions that require third-party verification, we strongly advise against uploading sensitive records (e.g., academic transcripts). Use at most a course schedule.

Email Communication Policy

Primary Method: Emailing the instructor from your JMU student account is the primary method of communication.
Response Time: The instructor typically responds within 1–2 business days. Emails received after 5:00 PM will be treated as received on the next business day. The instructor does not normally respond to emails on weekends or JMU holidays.
Canvas Messages: Canvas is strictly used for grades, course announcements, and logistics. DO NOT use the Canvas messaging system to contact the instructor; it is not regularly monitored and messages may be missed.

AI Policy

AI tools (such as Google Gemini, ChatGPT, Copilot, etc.) are recommended and permitted as analytical assistants in the course workflow. No paid subscription is required. However, students remain entirely responsible for the final visual design, data interpretation, and verification of all outputs. You must ensure the accuracy, ethical presentation, and integrity of your work.

Grading Breakdown

Attendance: 20%
Labs: 40%
Mini Project: 10%
Google AI Skills: 10% (Requires completing at least three Google AI Skills modules. The instructor will share a 5,000 Google Cloud credits pool with students.)
Final Project: 20%

Letter-Grade Scale

Grade	Range	Grade	Range	Grade	Range	Grade	Range
A	94 – 100%	A-	90 – 93.99%	B+	87 – 89.99%	B	84 – 86.99%
B-	80 – 83.99%	C+	77 – 79.99%	C	74 – 76.99%	C-	70 – 73.99%
D+	67 – 69.99%	D	64 – 66.99%	D-	61 – 63.99%	F	< 61%

Resubmission / Late Work / Project Policy

Before Deadline: You are allowed to resubmit assignments multiple times before the deadline. Only the final pre-deadline submission will be graded.
Late Penalty: Late submissions incur a 10% penalty per day, capped at a maximum 40% penalty, unless prior approval is obtained from the instructor.
Final Exam Week: No late submissions are accepted during final exam week.
Projects: Late submissions/resubmissions of the class projects will not be accepted.

Technical Assignment Support

Technical assignments may require troubleshooting. Start assignments early and allow sufficient time to resolve technical issues. Requests for assistance made shortly before a deadline may not receive a response before the deadline and do not automatically excuse late submissions.

Attendance / Excused Absence

Regular attendance is critical, as a significant portion of the learning happens in class through hands-on practice, critique, and discussion.

Registered Section Attendance: Students must attend the course section in which they are officially registered unless the instructor approves an exception in advance.
Unexcused Absences & Loss of Credit: Absence, leaving early without permission, being late more than 20 minutes, or disrespectful/disturbing behavior results in 0 attendance points for that meeting. Being late more than 5 minutes receives a late penalty.
Excused Absences: Excused absences are strictly limited to: sickness/health issues; mandatory activities with written documentation; or other situations with instructor approval.

Academic Integrity / Honor Code

All students are expected to abide by the JMU Honor Code. Plagiarism, including presenting unverified AI-generated content as your own original analysis without disclosure or responsibility, is a violation of the Honor Code. Please consult the JMU Honor Code website for more details.

Accessibility / Student Support

JMU is committed to creating a welcoming and accessible environment. If you have a documented disability and require accommodations, please register with the Office of Disability Services (ODS) and contact the instructor early in the semester to discuss your needs.

JMU offers numerous resources for student support, including the Science and Math Learning Center, the University Writing Center, and Counseling Center services. If you are facing academic or personal challenges, please utilize these resources or contact the instructor.

Inclement Weather

In the event of inclement weather or university closures, we will follow the official JMU delays and cancellations policy. Please monitor your JMU student email and Canvas announcements for updates regarding makeup classes or adjusted assignment deadlines.

Course Schedule (Fall 2026)

Schedule updated: August 28, 2026

Week / Dates	Lecture / Topic Focus	Hands-on Lab & Activities	Notes & Milestones
Week 1 Aug 24 – 28	Course Introduction & Cloud/AI Setup Why visualization matters; course workflow, expectations, and AI policy	Account verification; Tableau & ArcGIS institutional access; Google Cloud & AI environment setup	Classes begin Wed, Aug 26 (Status: Completed)
Week 2 Aug 31 – Sep 4	Introduction to Data Visualization Data → Information → Decision; Anscombe’s Quartet; visual perception, preattentive attributes & Gestalt principles; Heuer intelligence perception framing	ArcGIS Business Analyst Orientation, study areas, point/site creation, rings/drive-time areas, standard reports & infographics	Visual analytics & spatial orientation
Week 3 Sep 7 – 11	Map Design & Spatial Intelligence Spatial intelligence framing for analysis; lat/long & coordinate fundamentals; classification (equal interval, quantile, natural breaks, std dev); choropleth & proportional symbol maps	ArcGIS Business Analyst Color-coded maps, classification options, Smart Map Search, POI search, map layers & StoryMap basics	Thematic map design & spatial intelligence
Week 4 Sep 14 – 18	Color Theory for Data Visualization Hue, saturation, value; sequential, diverging, categorical & alerting palettes; color vision deficiency & accessibility	ArcGIS Business Analyst Import custom data, custom variables/reports, spatial analysis tools (e.g., benchmark comparison, suitability analysis), ArcGIS dashboard	Color accessibility & analytical GIS
Week 5 Sep 21 – 25	Choosing and Critiquing Graphs Chart-selection principles; histograms, box plots, scatter/line/bar charts, axes/scales; identifying misleading visualizations	Get Started with Tableau Data connection, basic sheets (bar, scatter), simple dashboard construction, and publishing workflow	Visual critique & core Tableau workflow
Week 6 Sep 28 – Oct 2	Data Preparation for Visual Analytics ETL pipelines, data quality, OLTP vs. OLAP, dimensions vs. measures, live vs. extract connections	Tableau Prep / Tableau Flow Clean, transform, null handling, standardizing values, calculated fields, split, union, join, and outputting clean data	Data cleaning & preparation pipelines
Week 7 Oct 5 – 9	MINI PROJECT: ArcGIS Spatial Visualization Project kickoff, clinic, and instructor support (No new lecture)	Mini Project Clinic & Studio Gun Violence Archive spatial analysis and demographic integration in ArcGIS Business Analyst	Fall Break begins Wed, Oct 7 (Mon session: Project Clinic) Mini Project Due
Week 8 Oct 12 – 16	Cloud-Connected BI & Multi-Table Data Relational DB vs. analytical warehouse, PK/FK, joins vs. relationships, cardinality, LOD, duplicate-row risks	Cloud-Connected BI • Mon: Tableau + Relational DB (Cloud PostgreSQL) • Wed: Tableau + BigQuery Warehouse (TICKIT multi-table data model)	Lab-heavy week: Relational DB & BigQuery
Week 9 Oct 19 – 23	Visual Analytics in Tableau Drill-down, sort, group, sets, filters, trend/reference lines, parameters, and interactive exploration	Visual Analytics in Tableau Parameters, sets, set actions, tooltips, interactive scatter plots, and Tableau mapping exercise	Advanced interactivity & spatial views in Tableau
Week 10 Oct 26 – 30	Dashboard Design Directed vs. exploratory discovery, KPIs, visual hierarchy, layout containers, eye-scanning, 5-second test, Shaffer 4Cs, Tufte/KISS principles	Tableau Dashboard Design Multiple coordinated views, dashboard layout, interactivity, dynamic titles, device layouts, and publishing	Usability & visual hierarchy
Week 11 Nov 2 – 6	Calculations in Tableau Basic calculations, date/string/logical functions, aggregate calculations, table calculations (scope/direction), LOD expressions (FIXED, INCLUDE, EXCLUDE)	Calculations in Tableau Calculated metrics, rates, moving averages, table calculations, and LOD expressions in dashboards	Advanced calculation logic & LOD
Week 12 Nov 9 – 13	AI-Generated Dashboards + Tableau Public AI visualization capabilities & human evaluation (correctness, chart choice, aggregation, 5-second test)	AI-Assisted Dashboards & Tableau Public • Mon: Gemini Canvas / Spark AI dashboard generation & critique • Wed: Tableau Public publishing & comparative evaluation (AI vs. Tableau)	AI vs. BI comparison & Tableau Public workflow
Week 13 Nov 16 – 20	AI Video Visualization & Analytical Storytelling AI video as visual communication; storytelling accuracy vs. hallucination risks	AI Video & Analytical Storytelling • Mon: Google Flow (visual/video synthesis supporting analytical findings) • Wed: Google Vids (assembling analytical video briefing)	Multimodal visual communication
Week 14 Nov 23 – 27	Thanksgiving Holiday (No Classes)	—	University Closed
Week 15 Nov 30 – Dec 4	Final Project Workshop: Week 1 Analytical question scoping, data pipeline, and visual planning (No new lecture)	Data Pipeline & Visual Scoping Clean and join COVID-19 dataset with approved contextual dataset (Colab/Pandas or Tableau Prep); initial Tableau sheets	Final Project data engineering & planning
Week 16 Dec 7 – 11	Final Project Workshop: Week 2 Dashboard design studio & instructor consultation (No new lecture)	Tableau Public Dashboard Finalization Build required map, temporal trend, comparison/distribution views, LOD calculations, interactivity, and interpretation	Final Project Studio & Link Testing
Week 17 Dec 12 – 18	Final Exam Week Final project submission only (No presentations)	Final Project Submission	Final Project Due (Tableau Public Dashboard)

Project Overviews

Mini Project: ArcGIS Gun Violence Spatial Visualization

Platform: ArcGIS Business Analyst / ArcGIS StoryMaps
Data: Instructor-provided Gun Violence Archive dataset + demographic contextual variable(s)
Core Requirements: Assigned state/study area analysis, meaningful thematic map, spatial analysis tool, infographic/report, and public StoryMap with analytical findings.
Focus: Map design, spatial visualization, color selection, and synthesis.

Final Project: COVID-19 Visual Analytics Project

Platform: Tableau Desktop / Tableau Public
Data: Instructor-provided primary COVID-19 dataset + one approved contextual dataset (e.g., population, poverty, income, education, unemployment).
Data Processing: Google Colab (Pandas) preferred, or Tableau Prep.
Required Dashboard Elements:
- At least one thematic map
- At least one temporal / trend visualization
- At least one comparison or distribution visualization
- At least one meaningful calculation (calculated field, table calculation, or LOD)
- At least one interactive feature (filter, parameter, set action, or equivalent)
- Clear, evidence-based analytical interpretation
Submission: Published interactive Tableau Public dashboard (No live presentation). Optional AI-generated visual assets (images/video) may support the story.

Return to Course Home