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Course Planning by Program

2026-27

Essential Objectives

Course Syllabus


Revision Date: 05-Mar-26
 

Summer 2026 | CIS-2420-VO01 - Data Visualization


Online Class

Online courses take place 100% online via Canvas, without required in-person or Zoom meetings.

Location: Online
Credits: 3 (45 hours)
Day/Times: Meets online
Semester Dates: 05-26-2026 to 08-17-2026
Last day to add this section:
Last day to drop without a grade: 06-08-2026 - Refund Policy
Last day to withdraw (W grade): 07-13-2026 - Refund Policy
Open Seats: 3 (as of 05-11-26 5:05 PM)
To check live space availability, Search for Courses.

Faculty

Hilary Ivy
View Faculty Credentials

Hiring Coordinator for this course: Deb Grant

    Note
  1. Many degree programs have specific general education recommendations. In order to avoid taking unnecessary classes, please consult with additional resources like your program evaluation, your academic program catalog year page, and your academic advisor.
  2. Courses may only be used to meet one General Education Requirement.

Course Description

This course introduces students to the foundational skills required to use data visualization tools. Through applied projects, students gain insight into data trends and patterns for better decision-making. Students use query editors and coding to pull data from numerous data sources, produce unified data sets for data analysis, merge and stack data together, and shape data from various sources to create different visualizations. This course builds skill in data analytics and the ability to make data-driven decisions. Prerequisite: Introduction to Computer Science


Essential Objectives

1. Describe the core concepts and components of data visualization tools and their role in data analysis and decision-making.
2. Explain and demonstrate how to use data visualization tools and functions including data sources, data modeling, visualizations, and reports.
3. Demonstrate the skills required to collect and transform data using queries including, connecting to various data sources, importing data, and cleaning, transforming, and shaping data for analysis.
4. Design effective data models in establishing relationships between tables, creating calculated columns and measures, and optimizing data for analysis.
5. Develop interactive visualizations that support data driven decisions.
6. Design comprehensive reports and dashboards.
7. Perform advanced data analysis techniques including using DAX formulas, incorporating time intelligence, and implementing advanced filtering and slicing.
8. Explore the scope and diversity of career opportunities in the field of data analytics and visualization through assignments such as informational interviews, job shadows, or other career-exploration activities.


Required Technology

More information on general computer and internet recommendations is available on the CCV computer recommendations Support page.

Please see CCV's Digital Equity Statement (pg. 45) to learn more about CCV's commitment to supporting all students access the technology they need to successfully finish their courses.


Required Textbooks and Resources


*** This is a no cost textbook or resource class. ***

CIS-2420-VO01 Link to Textbooks/Resources Information for this course in eCampus.

The last day to use a Financial Aid Advance to purchase textbooks/books is the 3rd Tuesday of the semester. See your financial aid counselor at your academic center if you have any questions.


Grading Criteria

CCV Letter Grades as outlined in the Evaluation System Policy are assigned according to the following chart:

 HighLow
A+10098
A Less than 9893
A-Less than 9390
B+Less than 9088
B Less than 8883
B-Less than 8380
C+Less than 8078
C Less than 7873
C-Less than 7370
D+Less than 7068
D Less than 6863
D-Less than 6360
FLess than 60 
P10060
NPLess than 600


Attendance Policy

Regular attendance and participation in classes are essential for success in and are completion requirements for courses at CCV. A student's failure to meet attendance requirements as specified in course descriptions will normally result in a non-satisfactory grade.

  • In general, missing more than 20% of a course due to absences, lateness or early departures may jeopardize a student's ability to earn a satisfactory final grade.
  • Attending an on-ground or synchronous course means a student appeared in the live classroom for at least a meaningful portion of a given class meeting. Attending an online course means a student posted a discussion forum response, completed a quiz or attempted some other academically required activity. Simply viewing a course item or module does not count as attendance.
  • Meeting the minimum attendance requirement for a course does not mean a student has satisfied the academic requirements for participation, which require students to go above and beyond simply attending a portion of the class. Faculty members will individually determine what constitutes participation in each course they teach and explain in their course descriptions how participation factors into a student's final grade.

Accessibility Services for Students with Disabilities:


CCV strives to mitigate barriers to course access for students with documented disabilities. To request accommodations, please
  1. Provide disability documentation to the Accessibility Coordinator at your academic center. https://ccv.edu/student-support/accessibility-services/
  2. Request an appointment to meet with accessibility coordinator to discuss your request and create an accommodation plan.
  3. Once created, students will share the accommodation plan with faculty. Please note, faculty cannot make disability accommodations outside of this process.


Academic Integrity


CCV has a commitment to honesty and excellence in academic work and expects the same from all students. Academic dishonesty, or cheating, can occur whenever you present -as your own work- something that you did not do. You can also be guilty of cheating if you help someone else cheat. Being unaware of what constitutes academic dishonesty (such as knowing what plagiarism is) does not absolve a student of the responsibility to be honest in his/her academic work. Academic dishonesty is taken very seriously and may lead to dismissal from the College.

Apply Now for this semester.

Register for this semester: November 3, 2025 - May 15, 2026