Data Literacy Training
Climate Change and Budget Data
Anjesh Tuladhar
What is data literacy?
• Ability to read, use and communicate
data as information
• Think critically about data
– Understa...
What is data?
• Webster meaning:
“facts or information used usually to
calculate, analyse, or plan something”
• Anything i...
Why data literacy?
• Slowly but steadily data are forcing
their way into every nook and cranny
of the industry, company an...
Making sense with data
Prepare
Analyse
Apply
1. Prepare
• Ask questions
• Collect data
• Organise data
• Cleanse data
2. Analyse
• Answer questions
• Answer with charts/summaries/filters
• Identify patterns/relationships
3. Apply
• Use results to communicate answers
• Convince answers
• Make decisions
• Share visualisations
Just numbers
http://en.wikipedia.org/wiki/Anscombe's_quartet
Very different when graphed
Climate Change and Budget
Data
• We will be using these data for the
hands-on practice
1. Prepare
• Ask questions
• Collect data
• Organise data
• Cleanse data
Prepare: Questions
• Is climate change priority area for Nepal?
– How much contribution does climate related
projects have...
Prepare: Data collection
• Where’s the data on Climate Change
and Budget?
– Budget: redbook (mof.gov.np)
– Climate change ...
Data Extraction Tools
• CSV (most used open format)
• Html (in websites)
– many programming tools, google chrome
scraper
•...
Tabula
• Tabula is a tool for liberating data
tables trapped inside PDF files.
• Tabula requires java and you have to
run ...
Tabula
• Upload the file
• Highlight the table
• Download the data in csv format
• Challenges
– Data not always in the cor...
Downloaded from Tabula
Prepare: Organise data
• Data not in usable form
• data not being able to use formula if
in such format
• Use tools like e...
Prepare: Clean data
• Data might still have issues
• The PDF data have comma and they
are not numbers
– They are text and ...
After organising and cleaning
Analyse
• Answer questions
• Answer with chart/summaries/filter
• Identify patterns/relationships
http://goo.gl/4ZuoiC
Analyse: Answer
• How much contribution does climate
related projects have in the total budget?
– Add climate related proj...
Analyse: Answer
• How much contribution each ministry make
to the climate change?
– Not so simple answer
– Try for 5 minut...
Analyse: Answer
• How much contribution each ministry has
in the climate change?
– Use of Pivot table
– 10 seconds
– Creat...
Analyse: Answer
• Which ministry has the highest
contribution in terms of their budget
share?
– Can you do it now?
– HINT:...
Apply
• Using results to communicate
answers
• Make decisions
• Visualisations
• Create stories from the data
Apply: Visualisations
• Putting patterns on the screen
• Identify the outliers
• Allows access to large amount of data
• M...
Visualisations: Datawrapper
• Created by journalists for journalists
• Go to http://datawrapper.de
• Create account and pa...
Next: Iterate, Iterate, Iterate…
Prepare
Analyse
Apply
Thank you
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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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  • 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
  • Data Literacy Training - Using Climate Change and Budget data of Nepal

    1. 1. Data Literacy Training Climate Change and Budget Data Anjesh Tuladhar
    2. 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. 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. 4. Why data literacy? • Slowly but steadily data are forcing their way into every nook and cranny of the industry, company and job
    5. 5. Making sense with data Prepare Analyse Apply
    6. 6. 1. Prepare • Ask questions • Collect data • Organise data • Cleanse data
    7. 7. 2. Analyse • Answer questions • Answer with charts/summaries/filters • Identify patterns/relationships
    8. 8. 3. Apply • Use results to communicate answers • Convince answers • Make decisions • Share visualisations
    9. 9. Just numbers http://en.wikipedia.org/wiki/Anscombe's_quartet
    10. 10. Very different when graphed
    11. 11. Climate Change and Budget Data • We will be using these data for the hands-on practice
    12. 12. 1. Prepare • Ask questions • Collect data • Organise data • Cleanse data
    13. 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. 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. 15. Data Extraction Tools • CSV (most used open format) • Html (in websites) – many programming tools, google chrome scraper • Pdf – very difficult – Pdf2text, Tabula
    16. 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. 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
    18. 18. Downloaded from Tabula
    19. 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. 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. 21. After organising and cleaning
    22. 22. Analyse • Answer questions • Answer with chart/summaries/filter • Identify patterns/relationships http://goo.gl/4ZuoiC
    23. 23. Analyse: Answer • How much contribution does climate related projects have in the total budget? – Add climate related projects budget – Simple addition formula
    24. 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. 25. Analyse: Answer • How much contribution each ministry has in the climate change? – Use of Pivot table – 10 seconds – Create suitable chart
    26. 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. 27. Apply • Using results to communicate answers • Make decisions • Visualisations • Create stories from the data
    28. 28. Apply: Visualisations • Putting patterns on the screen • Identify the outliers • Allows access to large amount of data • Makes data relevant
    29. 29. Visualisations: Datawrapper • Created by journalists for journalists • Go to http://datawrapper.de • Create account and paste relevant data for sharing
    30. 30. Next: Iterate, Iterate, Iterate… Prepare Analyse Apply
    31. 31. Thank you

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