Couse Outline

1.1. What is Data? - Explain how data is collected, stored in the tabular model, and analyzed to create information and knowledge.

1.1.1. Explain the relationship between data, information, and knowledge

1.1.2. Define data

1.1.3. Differentiate between primary and secondary data

1.1.4. Explain nominal, ordered, interval, and ratio data types

1.1.5. Describe how tables are structured

1.2. Transforming Data - Use spreadsheet functions to transform text, numeric, and geospatial data.

1.2.1. Describe how data functions are used to transform data

1.2.2. Differentiate between concatenate and parse text functions

1.2.3. Describe the order of operations for mathematical functions

1.2.4. Explain how logical functions can test value expressions

1.2.5. Use spreadsheets to transform data

1.3. Visualizing Data - Visualize data in tables, charts, maps, and dashboards using best practices.

1.3.1. Apply Ed Tufte’s Guidelines for Visualizing Data

1.3.2. List best practices in creating tables

1.3.3. List best practices in creating charts

1.3.4. List best practices in creating maps

1.3.5. List best practices in creating dashboards

1.3.6. Create and publish a dashboard with tables, charts, and maps

2.1. Data Models - Model tabular and geospatial data using conceptual, logical, and physical entity-relationship diagrams.

2.1.1. Describe the components of the Entity-Relationship Model

2.1.2. Label the symbology used in an Entity-Relationship Diagram

2.1.3. Explain conceptual, logical, and physical data models

2.1.4. List the three basic geospatial data models

2.1.5. Use an Entity-Relationship Diagram to model a database

2.2. Databases - Create database schema and relate tables in a Relational Database Management System (RDBMS).

2.2.1. Compare and contrast spreadsheets vs. databases

2.2.2. Explain what database schema is

2.2.3. Differentiate flat-file tables from relational tables

2.2.4. Identify the three cardinal relationships in an RDBMS

2.2.5. List and differentiate the three types of geodatabases

2.2.6. Create table joins and relates in GIS

2.3. Data Assimilation - Use Extract, Transform, Load (ETL) tools to assimilate vector, raster, and triangulated data in a geodatabase with subtypes and attribute domains.

2.3.1. Explain how ETL is used to assimilate data

2.3.2. Describe how vector data is modeled in GIS

2.3.3. Describe how raster data is modeled in GIS

2.3.4. Describe how triangulated data is modeled in GIS

2.3.5. Create geodatabase subtypes and attribute domains in GIS

2.3.6. Use ETL tools to assimilate data in GIS

3.1. Data Creation - Create and edit geospatial data using basic point, line, and polygon construction tools.

3.1.1. Explain what manual heads-up digitization is

3.1.2. Explain what automated classification digitization is

3.1.3. Describe how vector points, lines, and polygons are structured

3.1.4. Describe how raster grids are structured

3.1.5. Explain the role of editing environment in creating and modifying vector and raster data

3.1.6. Create and modify vector data using GIS

3.2. Data Editing - Create and edit geospatial data using advanced point, line, and polygon construction tools.

3.2.1. Provide examples of common raster and vector editing tools

3.2.2. Explain how geodetic features differ from planar features

3.2.3. Explain how Z and M coordinates enhance vector feature geometry

3.2.4. Create geodatabase contingent values in GIS

3.2.5. Use COGO to create geospatial data

3.2.6. Use advanced point, line, and polygon construction tools to create and edit geospatial data

3.3. Topology - Explain how network, geodatabase, and map topology are used in GIS.

3.3.1. Describe what topology is and how it’s used in GIS

3.3.2. List the topological elements that model connections between geospatial features

3.3.3. Differentiate between a directed and undirected network

3.3.4. Differentiate between geodatabase and map topology

3.3.5. Explain what metadata is

3.3.6. Create geodatabase and map topology in GIS

4.1. Location Data - Explain how GPS and DGPS create location data.

4.1.1. Differentiate between triangulation and trilateration

4.1.2. Describe the space, control, and user segments of GPS

4.1.3. List types of GPS error that degrade your positional accuracy

4.1.4. Explain how differential GPS works

4.1.5. Differentiate between image data and a mosaic dataset

4.1.6. Configure GIS data for collecting GPS field data

4.2. Field Data - Collect GPS field data using the field data collection workflow to minimize errors.

4.2.1. Describe the field data collection workflow

4.2.2. Explain how to plan and configure a mission

4.2.3. List types of feature-level metadata collected with GPS

4.2.4. Explain how to validate, assimilate, and backup field data

4.2.5. Collect spatial features and attribute values in the field

4.3. Data Automation - Describe and demonstrate the benefits of using data automation in GIS.

4.3.1. List the benefits of automating work

4.3.2. Describe the types of data processes that can be automated

4.3.3. Differentiate desktop, enterprise, and cloud-based automation

4.3.4. Describe different GIS data automation processes

4.3.5. Automate a data process using GIS