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| Credit- Degree applicable | | Effective Quarter: Fall 2020 | I. Catalog Information
| CIS 64G | Data Visualization Methodology and Tools | 4.5 Unit(s) |
| | Requisites: Advisory: EWRT 211 and READ 211 (or LART 211), or ESL 272 and 273. Hours: Lec Hrs: 48.00
Lab Hrs: 18.00
Out of Class Hrs: 96.00
Total Student Learning Hrs: 162.00 Description: This course is an introduction to the strategies and technologies used in business intelligence reporting and dashboards for making data-driven decisions. |
| Student Learning Outcome Statements (SLO)
| | | • Student Learning Outcome: Design and implement reports and dashboards for data and trends analysis using technologies like Tableau, PowerBI, BIRT or Pentaho. |
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II. Course Objectives | A. | Exhibit understanding of Business Intelligence (BI) technologies as means to solving business problems. |
| B. | Demonstrate connecting to disparate data sources for creating reports. |
| C. | Analyze data and perform calculations to derive results. |
| D. | Design visualizations to enable quick decision making for key objectives. |
| E. | Identify ways to publish and share reports, dashboards and other key analytics information with stakeholders. |
III. Essential Student Materials IV. Essential College Facilities | | Access to a computer lab with Microsoft Office, Adobe Acrobat and any one of Tableau, PowerBI, BIRT or Pentaho |
V. Expanded Description: Content and Form | A. | Exhibit understanding of Business Intelligence (BI) technologies as means to solving business problems. |
| 1. | Understanding key concepts of Business Intelligence (BI). |
| 2. | Review the landscape of BI tools. |
| B. | Demonstrate connecting to disparate data sources for creating reports. |
| 1. | Structured and unstructured data Sources. |
| 3. | Data transformation, cleaning or blending. |
| c. | Swapping primary and secondary data sources. |
| d. | Data query syntax (SQL, JSON, DAX) |
| a. | Building views of data. |
| f. | Drill-down and hierarchies. |
| C. | Analyze data and perform calculations to derive results. |
| 1. | Dimensions and Measures. |
| 2. | Aggregate Calculations. |
| 5. | Level of detail expressions. |
| D. | Design visualizations to enable quick decision making for key objectives. |
| c. | Waterfall and funnel charts. |
| a. | Shapes, text boxes and images. |
| b. | Page layout and formatting. |
| c. | Enhancing display visualizations. |
| b. | Visual hierarchies and drill-downs. |
| c. | Group interactions among visualizations. |
| E. | Identify ways to publish and share reports, dashboards and other key analytics information with stakeholders. |
| 1. | Print and export reports and dashboards. |
| 2. | Create user groups for role-based access. |
| 3. | Publish to web based resources. |
| 4. | Republishing after refreshing data. |
VI. Assignments | A. | Reading: Required reading from the textbook and class notes |
| B. | Programs: 4-5 report generation homework assignments. |
| C. | Group Project: Creating business intelligence dashboards for assigned datasets. |
VII. Methods of Instruction | | Lecture and visual aids
Discussion of assigned reading
Discussion and problem solving performed in class
Collaborative learning and small group exercises
Collaborative projects
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VIII. Methods of Evaluating Objectives | A. | One or two midterm examinations requiring some programming, concepts clarification and
exhibiting mastery of reporting constructs presented in the course. |
| B. | A final examination requiring concepts clarification and exhibiting mastery of data analysis and visualization principles. |
| C. | Evaluation of programming assignments and group project, based on correctness, intuitiveness and visual appeal. |
IX. Texts and Supporting References | A. | Examples of Primary Texts and References |
| 1. | Evergreen, Stephanie: Effective Data Visualization: The Right Chart for the Right Data 1st Edition. SAGE Publications. ISBN-13: 978-1506303055, May 2016. |
| B. | Examples of Supporting Texts and References |
X. Lab Topics | A. | Data extraction from disparate sources. |
| B. | Cleansing, blending and transforming data. |
| C. | Modeling and calculating data. |
| D. | Creating reports and charts. |
| F. | Publishing and sharing dashboards. |
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