Course Description: The Data Visualization course is designed to help students understand that the vast majority of work in any analysis happens before the analytical procedure starts. Data wrangling is the process of changing the structure and format of raw data until the data is compatible with (sometimes) rigid requirements for analysis. Data wrangling also includes a quick check of data quality. Data Visualization will give students an understanding and appreciation of the power in representing data graphically.
| Quarter Credit Hours: | 4.5 |
| Course Length: | 60 hours |
| Prerequisites: | DS101, DS108, DS109 |
| Proficiency Exam: | No |
| Theory Hours: | 30 |
| Laboratory Hours: | 30 |
| Externship Hours: | 0 |
| Outside Hours: | 15 |
| Total Contact Hours: | 60 |
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Ground-based students are required to bring a late model laptop computer (either PC or MacBook) to class every day.
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Online students are required to have a late model laptop or desktop computer with internet access.
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Minimum: PC (Windows 10/11) or Mac (Big Sur or Monterey) laptop. 8GB ram, 512GB HD, Intel Core i5, AMD Ryzen 5, or Apple Intel or M1 Chipsets.
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Recommended: PC (Windows 10/11) or Mac laptop(Big Sur or Monterey). 16GB ram, 1TB SSD, Intel Core i7, AMD Ryzen 7, or Apple M1/M1 Pro Chipsets.
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Professionals: PC (Windows 10/11) or Mac(Big Sur or Monterey). 32-64 GB ram, 2-8TB SSD, Intel Core i9, AMD Ryzen 9/Threadripper, or Apple M1 Max Chipsets.
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It is a requirement that you are able to download programming resources to your laptop/desktop for this class. (This means you need a steady internet high bandwidth connection.)
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You are required to have a quiet place to study and to be able to focus on the material.
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You are required to have uninterrupted weekly 1:1 video meetings with your mentor.
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You are required to log into the Learning Management System (LMS) daily for at least 20 minutes.
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Please follow and review each lesson page by page coding examples provided as this will ensure you have a full understanding for your final hands-on assignments.
| Module | Lesson Number | Lesson Name |
|---|---|---|
| DS104 Data Wrangling and Visualization | 1 | Manipulating Columns and Rows |
| 2 | Data Transformations | |
| 3 | Recoding Data | |
| 4 | Displaying Quantitative Data | |
| 5 | Displaying Qualitative Data | |
| 6 | Tableau | |
| 7 | Infographics | |
| 8 | More Complex Visualizations | |
| 9 | Choosing Appropriate Statistical Analyses | |
| 10 | Final Project |
Upon successful completion of this course, students will be able to:
- Extract important data from an existing data set
- Use various data table manipulations (stacking, splDSOing, summarizing, and joining) to change table format
- Screen their data to improve data quality and reduce noise
- Decide which data to include and exclude from their analyses
- Explain required input formats for various analyses
- Understand the impact that metadata has on analyses
- Create various methods of visualizing data using common tools: boxplot, histogram, scatterplot, line chart, tree map, heat map, pareto chart, spark lines, dynamic charts
- Attach meaningful statistics and analyses to the appropriate visualization
| Exam | Points | Activity |
|---|---|---|
| L1 | 0 | Practice Hands On |
| L2 | 12 | Exam |
| L3 | 9 | Hands On |
| L4 | 0 | Practice Hands On |
| L5 | 9 | Hands On |
| L6 | 9 | Hands On |
| L7 | 12 | Exam |
| L8 | 0 | Practice Hands On |
| L9 | 9 | Hands On |
| L10 | 54 | Final Project |
| Type | Points |
|---|---|
| Professionalism, Attendance and Class Participation*: | 6 points (5%) |
| Assignment/Hands-On/Homework: | 36 points (30%) |
| Exam/Quiz Average: | 24 points (20%) |
| Projects/Competencies/Research: | 54 points: (45%) |
| Total points: | 120 (100%) |
Create visualizations using supplied data and consider the type of each data column and its use in the visualization. For instance, the Crash_ID column looks like continuous variable because it is a number, but it is actually a categorical variable. How would this be used appropriately?