Critical Data Literacy
Beginner: 6-8 weeks
Developing/Proficient: 4-6 weeks
Course Introduction
There are four learning modules in critical data literacy. We begin with critical data literacy of understanding what is data, its importance, and how it is used. Next we learn about data lifecycles and how to properly document data and its importance. Following data life cycles, we turn to why data is heavily collected for businesses and organizations. Lastly, we take the data and how it can be organized using Microsoft excel from the storing of data to visualization.
Support for this research was provided by the Public Interest Technology University Network Challenge Fund, a fiscally sponsored project of New Venture Fund. The Public Interest Technology University Network’s challenge grants are funded through the support of the Ford Foundation, Hewlett Foundation, Mastercard Impact Fund with support from Mastercard Center for Inclusive Growth, Schmidt Futures, and The Siegel Family Endowment.
Intro Module PDF:
Prerequisites
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Learning Objectives
In this course you will:
- Overall objective for module 1
- Overall objective for module 2
- Overall objective for module 3
- Overall objective for module 4
*Mobile Version*
Critical Data Literacy
Beginner: 6-8 weeks
Developing/Proficient: 4-6 weeks
Course Introduction
There are four learning modules in critical data literacy. We begin with critical data literacy of understanding what is data, its importance, and how it is used. Next we learn about data lifecycles and how to properly document data and its importance. Following data life cycles, we turn to why data is heavily collected for businesses and organizations. Lastly, we take the data and how it can be organized using Microsoft excel from the storing of data to visualization.
Prerequisites
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Learning Objectives
In this course you will:
1. Overall objective for module 1
2. Overall objective for module 2
3. Overall objective for module 3
4. Overall objective for module 4
Navigating the Modules
| Beginner | Developing | Proficient | |
| Module 1: How to Navigate a World of Big Data | Begin here if: Learner has NO experience in data literacy or technological background knowledge: Can You Tell the Story | Begin here if: Learner has SOME skills/knowledge of data storytelling AND can demonstrate the importance of data: Types of Data | Begin here if: Learner is knowledgeable and able to demonstrate how data tells a story, demonstrate the importance of data, difference between correlation and causation with evidence based assertions: Evidence Based Assertions |
| Module 2: Data Organized and Stored | Begin here if: Learner has NO knowledge/skills in how data is stored and organized: Organized Data | Begin here if: Learner has SOME knowledge/skills of data stored and organized AND how data can be constructed in tables/databases: Table Construction | Begin here if: Learner is knowledgeable and able to demonstrate how data is stored and organized in databases AND construct a database: Activity Making a Database |
| Module 3: Documentation and Data Lifecycles | Begin here if: Learner has NO knowledge/skills in the lifecycle of data and how data is documented: Documentation…Let’s Take a Look | Begin here if: Learner has SOME knowledge/skills in the lifecycle of data AND explain the difference between good and bad data: Data Standardization | Begin here if: Learner is knowledgeable and able to demonstrate the lifecycle of data, how data is documented and standardized, AND utilize visualization tools: Activity Analyze Retail Sales |
| Module 4: Data for Identifying Patterns to Create Models | Begin here if: Learner has NO knowledge/skills of how patterns can be recognized in data to determine predictions and create models using data tables: Data is Collected for Identifying Patterns | Begin here if: Learner has SOME knowledge/skills in how patterns can be recognized, determine predictions, AND data visualizations tools: Data Visualization | Begin here if: Learner is knowledgeable and able to demonstrate data patterns utilizing statistical techniques AND create and describe data models utilizing Microsoft Power BI and Tableau: Data Visualization Tools |
*Mobile Version*
Module 1: How to Navigate a World of Big Data
| Beginner Begin here if: Learner has NO experience in data literacy or technological background knowledge: Can You Tell the Story |
| Developing Begin here if: Learner has SOME skills/knowledge of data storytelling AND can demonstrate the importance of data: Types of Data |
| Proficient Begin here if: Learner is knowledgeable and able to demonstrate how data tells a story, demonstrate the importance of data, difference between correlation and causation with evidence based assertions: Evidence Based Assertions |
Module 2: Data Organized and Stored
| Beginner Begin here if: Learner has NO knowledge/skills in how data is stored and organized: Organized Data |
| Developing Begin here if: Learner has SOME knowledge/skills of data stored and organized AND how data can be constructed in tables/databases: Table Construction |
| Proficient Begin here if: Learner is knowledgeable and able to demonstrate how data is stored and organized in databases AND construct a database: Activity Making a Database |
Module 3: Documentation and Data Lifecycles
| Beginner Begin here if: Learner has NO knowledge/skills in the lifecycle of data and how data is documented: Documentation…Let’s Take a Look |
| Developing Begin here if: Learner has SOME knowledge/skills in the lifecycle of data AND explain the difference between good and bad data: Data Standardization |
| Proficient Begin here if: Learner is knowledgeable and able to demonstrate the lifecycle of data, how data is documented and standardized, AND utilize visualization tools: Activity Analyze Retail Sales |
Module 4: Data for Identifying Patterns to Create Models
| Beginner Begin here if: Learner has NO knowledge/skills of how patterns can be recognized in data to determine predictions and create models using data tables: Data is Collected for Identifying Patterns |
| Developing Begin here if: Learner has SOME knowledge/skills in how patterns can be recognized, determine predictions, AND data visualizations tools: Data Visualization |
| Proficient Begin here if: Learner is knowledgeable and able to demonstrate data patterns utilizing statistical techniques AND create and describe data models utilizing Microsoft Power BI and Tableau: Data Visualization Tools |
Modules
Module 1: How to Navigate a World of Big Data
We begin to understand the importance of data and how our world navigates with it. Data is explained based on its various types and how it is being utilized for businesses, organizations, universities, and communities to make evidence-based decisions.
Module 2: Data Organized and Stored
Data enters visual storytelling by beginning how it is stored and organized utilizing tables and databases. It also progresses into how data can be interpreted to further its meaning and how impactful the telling of data can be.
Module 3: Documentation and Data Lifecycles
The lifecycle of data is explained as it progresses in each of its stages. Data visualization tools are introduced to support data standardization utilizing two industry tools, Microsoft Power BI and Tableau. The importance of data documentation is further explained to continue to tell the story behind the data.
Module 4: Data for Identifying Patterns to Create Models
Patterns in data are recognized using statistical techniques to make informed predictions. There is an engagement of real-world applying practical knowledge utilizing models for businesses, organizations, universities, and communities to help make better informed decisions.