Quality and Consistent Data with Open Data Editor
Are your datasets looking messy but you don’t know how to help it?
Have you always been wondering how you can produce better data without having to become a data scientist?
Developed in partnership with Open Knowledge Brazil and Escola de Dados, Quality and consistent data with the Open Data Editor is an essential open educational resource for anyone who wants to generate knowledge from data. This course is specially designed for non-technical users working with tabular data (Excel, Google Sheets, CSV) but without advanced technical knowledge.
It is free and fun. You’ll learn a lot of very useful tips and tricks which will help you make your everyday work more efficient and be proud of your datasets today.
The ODE app + ODE course combo is definitely your gateway to the world of data.
Here’s what you’ll learn:
Detecting and fixing errors in tables
- Learn to work with tabular data
- Don’t get lost when reviewing your spreadsheet
- Clean up your spreadsheets to gain valuable insights
FAIR data principles
- Understand how to guarantee the Findability, Accessibility, Interoperability, and Reuse of digital assets
- Master all about metadata
- Understand the Open Definition and the main data principles
Learning-by-doing
- Follow in the footsteps of an exclusive instructor
- Get hands-on and learn from your mistakes and successes
- Learn how to produce/work with better databases without writing code
Curriculum
- 8 Sections
- 65 Lessons
- Lifetime
- Welcome!2
- Module 1 – Concepts and Best Practices28
- 2.1Introduction
- 2.2Structured datasets
- 2.3[Quiz] Structured datasets3 Questions
- 2.4Open data definiton
- 2.5[Quiz] Open data definition1 Question
- 2.6Five stars of open data
- 2.7[Quiz] Five stars of open data1 Question
- 2.8Tabular data
- 2.9[Quiz] Tabular data1 Question
- 2.10Tabular data formats
- 2.11[Quiz] Tabular data formats1 Question
- 2.12The burden of data in PDFs
- 2.13[Quiz] The burden of data in PDFs1 Question
- 2.14The anatomy of a CSV
- 2.15[Quiz] The anatomy of a CSV1 Question
- 2.16Data types (1/2)
- 2.17Data types (2/2)
- 2.18[Quiz] Data types1 Question
- 2.19Character encoding
- 2.20[Quiz] Character encoding1 Question
- 2.21Data documentation
- 2.22[Quiz] Data documentation1 Question
- 2.23Metadata matters (1/2)
- 2.24Metadata matters (2/2)
- 2.25[Quiz] Metadata matters1 Question
- 2.26Data governance (1/2)
- 2.27Data governance (2/2)
- 2.28[Quiz] Data governance1 Question
- Module 2 – Ensuring Data Quality15
- 3.1Introduction
- 3.2Data quality overview
- 3.3[Quiz] Data quality overview1 Question
- 3.4Completeness and missing data
- 3.5[Quiz] Completeness and missing data1 Question
- 3.6Validity and consistency
- 3.7[Quiz] Validity and consistency1 Question
- 3.8Data validation techniques
- 3.9[Quiz] Data validation techniques4 Questions
- 3.10Uniqueness and duplicated values
- 3.11[Quiz] Uniqueness and duplicated values3 Questions
- 3.12Version control
- 3.13[Quiz] Version control1 Question
- 3.14Five data quality issues – and how to fix them
- 3.15[Quiz] Five data quality issues – and how to fix them3 Questions
- Module 3 – Tidying Your Data12
- 4.1Introduction
- 4.2Tidy data standards
- 4.3[Quiz] Tidy data standards1 Question
- 4.4Tidying tables
- 4.5[Quiz] Tidying tables1 Question
- 4.6Tidying headers
- 4.7[Quiz] Tidying headers1 Question
- 4.8Tidying columns
- 4.9[Quiz] Tidying columns3 Questions
- 4.10Data cleaning tools (1/2)
- 4.11Data cleaning tools (2/2)
- 4.12[Quiz] Data cleaning tools3 Questions
- Module 4 – Applying FAIR Data Principles14
- 5.1Introduction
- 5.2FAIR data principles
- 5.3[Quiz] FAIR data principles1 Question
- 5.4Creating an identifier
- 5.5[Quiz] Creating an identifier1 Question
- 5.6Describing rich metadata (1/2)
- 5.7Describing rich metadata (2/2)
- 5.8[Quiz] Describing rich metadata1 Question
- 5.9Indexing your data
- 5.10[Quiz] Indexing your data1 Question
- 5.11Proprietary and open data formats
- 5.12[Quiz] Proprietary and open data formats1 Question
- 5.13FAIR data frameworks and tools
- 5.14[Quiz] FAIR data frameworks and tools1 Question
- Module 5 – Open Data Editor Basics9
- Module 6 – Open Data Editor Features27
- 7.1Introduction
- 7.2Describing the data resource
- 7.3[Quiz] Describing the data resource1 Question
- 7.4Ensuring data integrity with hash codes
- 7.5[Quiz] Ensuring data integrity with hash codes1 Question
- 7.6Choosing a license
- 7.7[Quiz] Choosing a license1 Question
- 7.8Listing authors and sources
- 7.9[Quiz] Listing authors and sources1 Question
- 7.10The dialect for CSV files
- 7.11[Quiz] The dialect for CSV files1 Question
- 7.12Understanding primary and foreign keys
- 7.13[Quiz] Understanding primary and foreign keys1 Question
- 7.14Describing fields
- 7.15[Quiz] Describing fields1 Question
- 7.16Working with data types
- 7.17[Quiz] Working with data types4 Questions
- 7.18Documenting missing values
- 7.19[Quiz] Documenting missing values1 Question
- 7.20Generating automatic error reports
- 7.21[Quiz] Generating automatic error reports1 Question
- 7.22Understanding documentation-based error reports
- 7.23[Quiz] Understanding documentation-based error reports1 Question
- 7.24Hands-on (1/4)
- 7.25Hands-on (2/4)
- 7.26Hands-on (3/4)
- 7.27Hands-on (4/4)
- Module 7 – Sharing and Contributing3
Features
- An exclusive instructor will take you by the hand to learn by doing from your mistakes and successes
- All references and additional reading suggestions are included in the slides attached to each lesson
- The certificate will be available once you complete at least 70% of the course content
Target audiences
- People who don’t know how to code or don’t have programming skills to automatise their work with data
- Journalists, researchers, and civil society workers looking to handle data more effectively
- Anyone interested in improving data consistency and reliability

