Data Literacy Training - Using Climate Change and Budget data of Nepal
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This training was given to the journalists - print and online media regarding data literacy process,
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Data Literacy Training - Using Climate Change and Budget data of Nepal
- 1. Data Literacy Training Climate Change and Budget Data Anjesh Tuladhar
- 2. What is data literacy? • Ability to read, use and communicate data as information • Think critically about data – Understanding how to work with large datasets, how they are produced, how to connect various datasets, how to interpret them
- 3. What is data? • Webster meaning: “facts or information used usually to calculate, analyse, or plan something” • Anything is data – text, image, numbers, … • For computer to understand, data needs to be in structured and machine-readable form
- 4. Why data literacy? • Slowly but steadily data are forcing their way into every nook and cranny of the industry, company and job
- 5. Making sense with data Prepare Analyse Apply
- 6. 1. Prepare • Ask questions • Collect data • Organise data • Cleanse data
- 7. 2. Analyse • Answer questions • Answer with charts/summaries/filters • Identify patterns/relationships
- 8. 3. Apply • Use results to communicate answers • Convince answers • Make decisions • Share visualisations
- 10. Very different when graphed
- 11. Climate Change and Budget Data • We will be using these data for the hands-on practice
- 12. 1. Prepare • Ask questions • Collect data • Organise data • Cleanse data
- 13. Prepare: Questions • Is climate change priority area for Nepal? – How much contribution does climate related projects have in the total budget? – How much contribution each ministry has in the climate projects? – Which ministry has the highest contribution in climate change? • A look at the specific projects
- 14. Prepare: Data collection • Where’s the data on Climate Change and Budget? – Budget: redbook (mof.gov.np) – Climate change project: NPC report • PDF? – Data extraction from PDF http://goo.gl/oCzfaW
- 15. Data Extraction Tools • CSV (most used open format) • Html (in websites) – many programming tools, google chrome scraper • Pdf – very difficult – Pdf2text, Tabula
- 16. Tabula • Tabula is a tool for liberating data tables trapped inside PDF files. • Tabula requires java and you have to run the software http://tabula.nerdpower.org/
- 17. Tabula • Upload the file • Highlight the table • Download the data in csv format • Challenges – Data not always in the correct format – Need cleaning/organising
- 19. Prepare: Organise data • Data not in usable form • data not being able to use formula if in such format • Use tools like excel, google refine to organize data – Add columns, remove columns, edit columns, add new fields
- 20. Prepare: Clean data • Data might still have issues • The PDF data have comma and they are not numbers – They are text and can’t be summed up, – Search and replace comma http://goo.gl/4ZuoiC
- 21. After organising and cleaning
- 22. Analyse • Answer questions • Answer with chart/summaries/filter • Identify patterns/relationships http://goo.gl/4ZuoiC
- 23. Analyse: Answer • How much contribution does climate related projects have in the total budget? – Add climate related projects budget – Simple addition formula
- 24. Analyse: Answer • How much contribution each ministry make to the climate change? – Not so simple answer – Try for 5 minutes – Create suitable chart
- 25. Analyse: Answer • How much contribution each ministry has in the climate change? – Use of Pivot table – 10 seconds – Create suitable chart
- 26. Analyse: Answer • Which ministry has the highest contribution in terms of their budget share? – Can you do it now? – HINT: You will need to get data from two sources and work in a third sheet – Create suitable chart
- 27. Apply • Using results to communicate answers • Make decisions • Visualisations • Create stories from the data
- 28. Apply: Visualisations • Putting patterns on the screen • Identify the outliers • Allows access to large amount of data • Makes data relevant
- 29. Visualisations: Datawrapper • Created by journalists for journalists • Go to http://datawrapper.de • Create account and paste relevant data for sharing
- 30. Next: Iterate, Iterate, Iterate… Prepare Analyse Apply
- 31. Thank you
Hinweis der Redaktion
- e.g. GDP data (data journalism) – a number that could be fabricatedTI data on most corrupt agecies – parties – 1000 respondentsBetter tools will not lead to better journalism if not used with insight
- Talk about data scientists, how they are exploiting data to make best use of the dataTop job in silicon valley is data scientists, 6000 companies hiring data scientists
- Process prepare analyse apply
- School datae.g. how to increase the graduation rate of my district schools?Where to find the information – DOE, ministry of education, other district websites?
- Pdfredbook
- Tabula running s3.yipl.com.np:8080
- DatawrapperSimilar tools google fusion table? For sharing
- Process prepare analyse apply