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DataKind Singapore
DataLearn: Post-DataDive Sharing
23 July 2015
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Agenda
1. DataKind Singapore updates
2. DataDive Overview
3. DataDive Sharing: Earth Hour
4. DataDive Sharing: HOME
5. Small group discussions on data
handling best practices (if time allows)

Agenda
1. DataKind Singapore updates
2. DataDive Overview
3. DataDive Sharing: Earth Hour
4. DataDive Sharing: HOME
5. Data handling best practices

DataKind™ harnesses
the power of data
science in the service of
humanity.

Updates
Project Accelerator coming up on 5 Sept!
- Please help spread the word to any social change organizations you know
who do good work.
- Sign up form here: http://goo.gl/forms/0TbDySVFi7
- Sign up by Friday, Aug 14.

Other data4good stuff
http://unitedwehack.com/
- August 15-16, 24 hour hackathon organized with UN Women
- “A Hackathon to Promote Financial Literacy and Economic Empowerment for
Women Around the Globe.”
- Access to partner APIs
http://blog.datalook.io/openimpact/
- Until August 31
- “DataLook is a directory of reusable data-driven projects for social good. Join
our replication marathon and bring a project to your city.”

Get to know the
participating
organizations
Select and learn
about a problem
and the data
Determine a
specific task or
team that you
can work on
Data Dive in!
Repeat!
Coordinate with the team’s
Data Ambassadors & Project
Managers !→ code
→ final presentation
→ analysis
Contribute
Results

DataDive Retrospective
- Took place over weekend of 23 - 25 Apr
- More than 70 participants
- 2 non-profit organizations
- Earth Hour
- HOME
- Intro to the orgs on Friday and socialize,
working through Saturday, final
presentations on Sunday at 1pm.

DataDive Key Learnings
- Full involvement from partner orgs is important, and we need to
emphasize this from the very beginning
- Trello will be mandatory to avoid duplication of effort
- Grant access to data on Friday night and help participants to
start setting up data and tools, so that they can start right away
on Saturday morning
- Remind people that final presentations will be from Google
Presentation and set a hard deadline for getting content in, so
that there is time to vet

Tweet Data Analysis - EARTH HOUR
Main Objectives
- Identify influencers on twitter
- Sentiment Analysis
- Word Cloud Analysis

Tweets Analysis Prep - EARTH HOUR
1. Understanding the data
anon
_id
created
_at
Text No Comma coordi
nates
lang RT_
count
fav_
count
reply_to
_user_id place
8393
7561
27/03/
2015
RT @JimHarris: SHAME: Canada Ranks LAST
Among OECD Countries for #ClimateChange
Performance #cdnpoli #climate #gls15 http://t.
co/0DD7S6oy7h
en 152 0
8423
3936
28/03/
2015
RT @earthhour: It's not about which country
you're from; it's about what planet we're from.
Join us for #EarthHour! 28th March; 8.30pm.
en 360 493 8423393
6
8069
6055
27/03/2
015
@nenshi will you be joining the #earthhour
party? Plz retweet to encourage lights out?
#earthhourcalgary #yourpower http://t.
co/68TblYiW2Y
-114.0
66491
4;....
en 9 4 [-114.0
59111
818;....

Tweet Data Analysis Prep - EARTH HOUR
2. Identify preliminary tasks to support other analysis
- Identify which tweets are retweets
- Identify which tweets contain which EH hashtags
- For retweets, identify which user is being retweeted

3. Creating additional variables (I)
- Turn data into a table (tblTweets)
- Create binary variable to identify retweets
... Text No Comma ... is_retweet
RT @JimHarris: SHAME:
Canada Ranks LAST
Among OECD Countries
for #ClimateChange
Performance #cdnpoli
#climate #gls15 http://t.
co/0DD7S6oy7h
=IF(ISNUMBER
(SEARCH("RT
@",[@[Text No
Comma]])),1,0)
What does the formula do?
→ Check if “RT @” is found in the tweet text
Case 1: String is found
1. SEARCH returns start character of string
2. ISNUMBER evaluates to true as search
returned a number
3. IF returns 1 as isnumber is true
Case 2: String is not found
1. SEARCH returns #Value error
2. ISNUMBER will evaluate to false as
search did not return a number
3. IF returns 0 as isnumber is false

4. Secondary use for the is_retweet variable
- Understand lasting impact of campaign and event

5. Creating additional variables (II)
- The motto of EH was “use your power to change climate change”
- EH # crawled: #EarthHour, #climatechange, #yourpower, #useyourpower
- Create binary variables for each hashtag
... Text No Comma ... EarthHour ...
RT @JimHarris: SHAME: Canada
Ranks LAST Among OECD
Countries for #ClimateChange
Performance #cdnpoli #climate
#gls15 http://t.co/0DD7S6oy7h
=IF(ISNUMBER(SEARCH
("#"&tblTweets[[#Headers],
[EarthHour]],[@[Text No
Comma]])),1,0)
...

6. Secondary use for binary hashtag variables

Main Takeaways
- Coding knowledge (e.g. R / Python) is not required to
contribute during a Data Dive
- Preparatory tasks can yield useful insights as well
- Excel can be helpful but may not be the most suitable
tool for large data files

Identify Influencers - EARTH HOUR
Problem
Identify influencers
Solution
Analyse tweet data to identify most retweeted users

Identify influencers - EARTH HOUR
1. Creating additional variables
- extract which user is being retweeted
... Text No Comma ... is_ret
weet
original_tweeter
RT @earthhour: It's not about
which country you're from; it's
about what planet we're from. Join
us for #EarthHour! 28th March;
8.30pm.
1 =IF([@[is_retweet]]=0,"original",RIGHT(LEFT([@
[Text No Comma]],FIND(":",[@[Text No Comma]]
&":")-1),LEN(LEFT([@[Text No Comma]],FIND(":",
[@[Text No Comma]]&":")-1))-3))
→ @earthhour

1. Creating additional variables
What does the formula do in this example?
=IF([@[is_retweet]]=0,"original",RIGHT(LEFT([@[Text No Comma]],FIND(":",[@[Text No Comma]]&":")-1),
LEN(LEFT([@[Text No Comma]],FIND(":",[@[Text No Comma]]&":")-1))-3))
[@[is_retweet]] = 1
[@[Text no comma]] = “RT @earthhour: It's not about which country you're from; it's about what planet
we're from. Join us for #EarthHour! 28th March; 8.30pm.”
The formula can be broken down into a few parts:
1. Check if it is a retweet - if it is, go to Point 2, otherwise mark it as “original”
2. Find the first occurrence of “:” in the text, return the character where it appears - 1
3. Start at the left of the tweet text, truncate the string after [Point 2] characters
4. From the length of the string in [Point 3], subtract 3 (for “RT “)
5. Start at the right of the string in [Point 3], truncate after [Point 4] characters

1. Create additional variables
2. Select all tweets in dataset with retweets >=500
3. Check for extraction errors (if re-tweets > followers, manual investigation)
4. Check for parsing errors (if length of text >= 180 char, marked as error)
5. From remaining set: For users who were retweeted, assess profile
information, number of followers, country where available

6. Outcome: Users who were most retweeted
User who is
retweeted
Nr. RT
>=500
Total RT
Count
User info Nr. Of
Followers
Country /
Region
@earthhour 5 46,947 EarthHour 143,000 Global
@LeoDiCaprio 5 7,627 Leonardo di Caprio
- Actor, WWF Ambassador
12,800,000 US
@AstroSamantha 2 2,750 Sam Cristoforetti
- Italian Astronaut on ISS
510,000 Italy

Sentiment Analysis - Tweets
WHAT IS SENTIMENT ANALYSIS
● Sentiment analysis aims to determine the attitude of a speaker or a writer
with respect to some topic or the overall contextual polarity of a
document.
WHAT WE USED FOR SENTIMENT ANALYSIS
● We used the Python package VADER, a lexicon and rule-based sentiment
analysis tool that is specifically attuned to sentiments expressed in social
media, and works well on texts from other domains.
● More information on VADER can be found in
○ http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdfhttp:
//comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf
○ https://github.com/cjhutto/vaderSentiment
● Installed VADER thru PIP

HOW TO USE VADER IN YOUR CODE
from vaderSentiment import sentiment as vaderSentiment
sentences = [
"VADER is smart, handsome, and funny.", # positive sentence
example
"VADER is smart, handsome, and funny!", # punctuation emphasis
handled correctly (sentiment intensity adjusted)
].
for sentence in sentences:
print sentence,
vs = vaderSentiment(sentence)
print "nt" + str(vs)

HOW VADER ANALYZES SOME OF THE INPUTS
VADER is smart, handsome, and funny.
{'neg': 0.0, 'neu': 0.254, 'pos': 0.746, 'compound': 0.8316}
VADER is smart, handsome, and funny!
VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!!
A really bad, horrible book.
{'neg': 0.791, 'neu': 0.209, 'pos': 0.0, 'compound': -0.8211}

HOW VADER ANALYZES SOME OF THE INPUTS
At least it isn't a horrible book.
:) and :D
Today sux
Today SUX!

HOW WE PERFORMED SENTIMENT ANALYSIS ON EARTH HOUR DATA
● DEFINE RANGES FOR COMPOUND VALUE TO CATEGORIZE THE
SENTIMENT OF TWEET
VERY NEGATIVE, NEGATIVE, NEUTRAL, POSITIVE AND VERY
POSITIVE
● OPEN FILE
● READ A RECORD
● PARSE AND EXTRACT TWEET
● PASS TWEET TEXT TO VADER METHOD

HOW WE PERFORMED SENTIMENT ANALYSIS ON EARTH HOUR DATA
● PARSE OUTPUT TO EXTRACT COMPOUND
● BASED ON COMPOUND VALUE , DETERMINE SENTIMENT OF THE TWEET
● STORE THE CATEGORICAL VALUE OF SENTIMENT IN A VARIABLE
● ADD A DUMMY VARIABLE WITH VALUE OF 1
● GO TO STEP 3 TILL EOF
● WRITE THE OUTPUT TO A FILE

HOW WE PREPARED CHARTS FOR SENTIMENT ANALYSIS
● OPEN THE OUTPUT FILE CREATED AFTER APPLYING VADER
● READ RECORDS INTO DATAFRAMES OF PANDAS (A powerful Python
data analysis toolkit)
● PERFORM GROUPING (think of as GROUPBY in SQL) AND SUMMARIZE
THE DUMMY VARIABLE
● PRESENT THE OUTPUT BY PIE CHARTS (using Python package
MATPLOTLIB)

1) Using all valid records
- Apply Python package:
-vaderSentiment
- Categorize sentiment scores:
-Very positive: >0.55
-Positive: >=0.10 to <=.54
-Neutral: >= (-0.10) to <=0.09
-Negative: >=-0.55 to <=-0.11
-Very negative: <=-0.56
Input: Cleaned Hashtag Tweets

Breakdowns for Tweets / Re-tweets

Questions?
1. Which keywords are most retweeted?
2. Is there a relation among the topics containing each of the four
keywords of Earth Hour?
3. Which words represent different sentiments?

Data Cleaning
Looking at English tweets.
Tweeted messages includes
non meaningful characters.
# remove retweet entities RT/via
some_txt = gsub("(RT|via)((?:bW*@w+)+)", "", some_txt)
# remove <br>
some_txt = gsub("<br>", "", some_txt)
# remove @people
some_txt = gsub("@w+", "", some_txt)
# remove html links
some_txt = gsub("http(s?)(://)(.*)[.|/|_](.*)+", "", some_txt)
some_txt = gsub("htt(.*)", "", some_txt)
# replace smiley
some_txt = gsub(": ", " ", some_txt)

Create WordCloud
library(wordcloud)
library(tm)
# import the data
lords <- Corpus(DirSource("./Wordcloud_Input/"))
# transform and prepare the data for the word cloud
lords <- tm_map(lords, stripWhitespace)
lords <- tm_map(lords, content_transformer(tolower))
lords <- tm_map(lords, removeWords, c("amp"))
lords <- tm_map(lords, stemDocument)
# Word cloud graph
wordcloud(lords, scale=c(3,0.3), max.words=100, random.order=FALSE,
rot.per=0.35, use.r.layout=FALSE, colors=brewer.pal(8, "Dark2"))
R library
Load input file
Further text
processing
Graph
options

Word Cloud Analysis
Using Unique Tweets Only
- Remove the four EH hashtags
- Apply R package: wordcloud
- This package works on word-stems
Input: Cleaned Hashtag Tweets

Word Cloud Analysis
Comparison (Unique Tweets only):
#earthhour #climatechange

Putting in Sentiment Analysis
Word Cloud (only unique tweets)

Country assignment to subscribers - EARTH HOUR
Problem
Find out country of ActiveCampaign subscribers
Solution
Merge information from different columns, such as city,
country, lat-long

Parallelized human cleaning process (PHCP)
"id","email","sdate","cdate","city","state_province","zip","country1","country2","country3","location","language","address","age","discover"
"100011","email@0accounts.com","2014-01-26 15:35:28","2014-01-26 02:35:28","","","","Netherlands","","","","","","",""
"100012","email@163.com","2012-11-29 02:01:46","2012-11-28 13:01:46","","","","China","","","","","","",""
"100013","email@student.hcmus.edu.vn","2015-03-21 20:32:55","2015-03-21 07:32:55","ho chi minh","","","Viet Nam","","","","","","",""
"100014","email@gmail.com","2014-03-14 14:59:15","2014-03-14 01:59:15","Coimbatore","","","","","","","","","",""
"100015","email@","2013-09-27 10:25:29","2013-09-26 21:25:29","","","","China, Guangdong Province, Foshan City","","","","","","",""
"100016","email@yahoo.com.tw","2012-11-29 02:41:50","2012-11-28 13:41:50","","","","Taipei","","","","","","",""
"100017","email@gmail.com","2013-03-12 11:36:39","2013-03-11 22:36:39","","","","12`3123","","","","","","",""
ActiveCampaign crawler and conversion from JSON
Result
# of subscribers: 321,704
# of subs. with country: 188,462 → 58.6%

Campaign response rate comparison of users -
EARTH HOUR
Problem
Rank users based on historical campaign response
data
Solution
Use expected open rates to compare users with
different number of campaigns received

Raw data
JSON with user-level campaign response from ActiveCampaign API
"344": {"subscriberid": "451267", "orgname": "",
"times": "0", "phone": "", "tstamp": "2014-03-28
00:51:14", "email": "email@gmail.com"}, "0":
{"subscriberid": "439666", "orgname": "",
00:51:14", "email": "email2@gmail.com"},
00:51:14", "email": "email3@yahoo.com"},
00:51:14", "email": "email4@yahoo.com"}
{"open_list": [], "campaign_id": "90"}
{"open_list": [{"times": "304", "tstamp":
"2014-03-07 13:49:26", "subscriberid":
"395746", "email":
"somebody@earthhour.org"}],
"campaign_id": "89"}
{"open_list": [], "campaign_id": "20"}
{"open_list": [{"times": "2", "tstamp":
"2013-01-22 15:00:20", "subscriberid":
"14604", "email": "someone@earthhour.
org"}], "campaign_id": "8"}
{"0": {"info": [{"subscriberid": "5", "orgname":
"", "times": "1", "phone": "", "tstamp": "2015-
03-29 22:58:27", "email": "email@gmail.
com"}, {"subscriberid": "8", "orgname": "",
"times": "1", "phone": "", "tstamp": "2015-03-
29 23:03:03", "email": "puikwan.lee@gmail.
com"}], "a_unique": "2", "tracked": "1", "link":
"https://github.com/DataKind-SG", "a_total":
"2", "id": "26", "name": ""}, "result_output":
"json", "result_message": "Success:
Something is returned", "result_code": 1}}
Open list
Unopen
list
Link list

Open rates
Why raw open rate is a bad estimate?

Campaign counts
Users’ chance of engagement varies; they receive different amount of emails
Jan 2014 Jan 2015
Bridget
Alan

Who is better?
Response rate and campaign count together define how interested a user is
Alan Bridget
Response rate = 50%
Campaign count = 2
Response rate = 40%
Campaign count = 5

Uncertainty
To make users comparable suppose both users receive the same count
Alan Bridget
Response rate = 20-
80%
Campaign count = 5
Response rate = 40%
Campaign count = 5
?
?
?

Expected open rate
Calculate expected open rate based on distribution of open rate

Using evidence of open count
We already know Alan opened one e-mail and did not open another
• OpenCount >= 1
• UnopenCount >= 1 → UnopenCount = CampaignCount - OpenCount

Generalize to N campaigns
Expected open rate is weighted average of the conditional expected values
• M maximum campaign count any user received in the dataset
• j number of campaigns that Alan received

Final scoring
0.75 * Click-through score + 0.25 * Open score
Best userDistribution of score

Globe Visualisation - EARTH HOUR
Problem
Make a sexy visualisation for eye candy
Solution
Pre-canned javascript package!

Globe Visualisation
http://datakind-sg.github.io/
- Height of each bar is related to number of Twitter followers of Earth Hour,
and color is related to temperature change from 1970’s to 1990’s
- WebGL Globe is used: https://www.chromeexperiments.com/globe
- Essentially, you just need to pass in a JSON formatted array with the
following form:
[lat1, long1, height1, colour1, lat2, long2, height2, colour2, …]
- You can use the code as a template: https://github.com/DataKind-
SG/datakind-sg.github.io

Globe Visualisation - Colour
- The temperature data is available from the IPCC: http://www.ipcc-data.
org/cgi-bin/ddc_nav/
- This is a (bad) proxy for degree of climate change at each lat/long, and
Earth Hour suggested a broader measure.
- The temp difference between the 1970’s and 1990’s was scaled to be
between 0 and 3 so that blue corresponds to the biggest decrease in
temp during that period, and red corresponds to the biggest increase in
temp.
- As homework for you… change the color map so that there isn’t green in
the middle.

Globe Visualisation - Height
- The height of each bar is ~ log(1 + n), where n is the number of Twitter
followers at the lat/long (with the exact locations rounded to the nearest
quarter degree in order to bucket).
- So the difficult part is finding the lat/long.
- Twitter profiles have free text for the location, and this needs to be
converted into a lat/long.
- Geocoding hack: try to match with given list of:
cities http://download.geonames.org/export/dump/
or countries https://developers.google.com/public-
data/docs/canonical/countries_csv

Geocoding hack
- Code is here https://github.com/oliverxchen/geovis, quasi-pseudo code below.
- We’ll look at what happens to a few examples of inputs:
- "Üt: 10.253083,-67.585859"
- "01.350750086, 103.951196586"
- "Bristol, UK"
- "between sky and earth"
- "CALAMBA, laguna, philippines"
- “Singapore”
- "Xalapa, Veracruz, Mexico"

Geocoding hack
The program is basically just a big loop through all of the free text locations and
applying the following in sequence.
A) standardize the string (change to lower case, replace multiple spaces with single spaces)
- "Üt: 10.253083,-67.585859"
- "01.350750086, 103.951196586"
- "between sky and earth"
- "bristol, uk"
- "calamba, laguna, philippines"
- "singapura"
- "xalapa, veracruz, mexico"

Geocoding hack
B) if the string starts with "Üt:", usually followed by an actual lat/long which can be
directly used
In the example strings,
"Üt: 10.253083,-67.585859" is mapped to [10.253083,-67.585859]
C) split remaining strings by commas
- ["01.350750086", "103.951196586"]
- ["between sky and earth"]
- ["bristol", "uk"]
- ["calamba", "laguna", "philippines"]
- ["singapura"]
- ["xalapa", "veracruz", "mexico"]

Geocoding hack
D) if single string after split and there’s no match yet, try to match with country list
- ["singapura"] is matched to "singapore"
- ["between sky and earth"] is not mapped
E) if two strings after split and there’s no match yet, try to parse to a lat/long:
- ["01.350750086", "103.951196586"] is mapped to [1.350750086, 103.951196586]
- ["bristol", "uk"]: float parse fails

Geocoding hack (cont.)
F) if there isn’t a match yet
try to match zeroth string to list of cities
- ["bristol", "uk"] is mapped to "bristol, united states" (Whoops!)
- ["calamba", "laguna", "philippines"] is mapped to "calamba, philippines"
- ["xalapa", "veracruz", "mexico"] is not mapped

G) if there still isn’t a match yet
try to match the last string to list of countries
- ["xalapa", "veracruz", "mexico"] is mapped to "mexico"
H) if still no match, you’re out of luck
- ["between sky and earth"]

- To deal with typos and ‘interesting’ spellings, match to cities and countries is done
using a Jaro-Winkler measure (similar to Levenshtein edit distance, but higher
weight on letters early in the word).
- Largest Jaro-Winkler value is used and needs to be above a threshold to be
considered a match.
- Python package is python-levenshtein
- Other logic to use previous results if standardized strings match
- Many improvements are possible! Eg:
- non-uniqueness of city names is not handled
- splitting on not just commas
- etc.

How many cases does HOME receive each year, segmented
by domestic and non-domestic workers?
- Problems faced
- Dates had to be
reformatted
while reading
into R
- Some outlier
cases had to be
removed (e.g.,
year > 2016)
- Demo on how plot
was created

Across nationalities, what’s the proportion of workers placed
by each agency?
- Problems faced
- Some country
data was in free
text and had to
be integrated
- Demo on how plot
was created

HOME: Worker Salary Data
What’s the salary range for each nationality?
● Overall
● Domestic Workers
● Non-domestic Workers

Key Challenges:
● Missing salary
● Inconsistent units (per hour/day/week/month) or missing units
● Different currencies (SGD, USD, MYR)
● Ranges given (eg 6-8)
● Converting hours/days/weeks to months
How it was handled:
● Using Excel, divide into two columns: values and units
● Standardize to monthly salary in third column
● Discovered on the second day that a filter hadn’t been applied
correctly, so the columns became misaligned… quick fix was
applied, but this should be checked.

How did we create this?

Key Lessons
One graph may not be sufficient to convey
the intended information.
Be careful of unknowingly comparing
apples to oranges.

Postal Code Problem - HOME
- Problem
- Postal Codes were not present in every record.
- Solution
- Use Google maps API, OneMap API, OpenStreetMap API
to map address to Postal Code

- Method
- Retrieve Postal code using 3 APIs.
- Each API returned more than 1 Postal Code for one
address, as each address could map to different Postal
codes.
- Eg {"postalcode": ["089057", "088542", "079716", "088541",
"079911", "079120"], "address": "fuji xerox"}{"postalcode":
["039801", "039594", "039797", "039596", "039799"], "address":
"raffles"}{"postalcode": ["310031", "310035"], "address": "toa
payoh"}

- Problem
- All 3 API may or may not returned same set of Postal
codes.
- Solution
- Use polling method to decide which Postal code to pick.

Polling/Voting Algorithm
1. Collect all zips across all data source.
2. Weigh each zip by the number of times it appears in a data source.
3. Select the highest weighted zip.
4. Random select if there multiple highest weighted zips.
5. Sensor integration.

Polling/Voting Algorithm (Precisely)
1. Let A := ⋃i
Ai
be the set of all unique zips where Ai
is the set of zips from
data source i.
2. For each a ∊ A, compute the weight wa
= ∑i Ai
(a).
3. Select the zip a* where ∀a : wa*
wa
.
1. A1
:= (44, 34, 26, 17), A2
:= (34, 45, 17), A3
:= (17)
2. A = (44, 34, 26, 17, 45)
3. w44
= 1, w34
= 2, w45
= 1, w17
= 3
4. a*
= 17

Issues
1. Due to privacy concerns, we never saw the full address.
2. This means that we have no feel for the data.
3. Potential for errors.

- Map based visualization
- Problem : Couldn’t find a geo data to highlight districts in
Singapore.
- Solution : Find the Center Lat Long of each district and
show the results with a marker.
- Tools : Used leaflet.js for map visualization.
- Geo data for the map was used from openstreet
maps.

source - https://www.ura.gov.sg/realEstateIIWeb/resources/misc/list_of_postal_districts.htm

- Number of abuses per district

Example DC.js plot.
Interactive Data Visualization
Problem:
• HOME may need to do analysis in
future to see if the situation has
improved/changed
Solution:
• Build an interactive data
visualization tool to support self-
serviced investigations
Tools:
• Used DC.js for data visualization

5 easy steps to use DC.js

Filter by age and salary range

Data anonymization
Problems:
• lot of sensitive data:
• first order: name, home address, passport number, birthday,
contact number, FIN
• second order: current/previous employer nfo, case created
by, agency contact nfo
• HOME data had a lot of free text fields that had various level of private
information:
• “Do you want me to treat you like <NAME>?!”
• “On <exact date>...”
• “His friend, <NAME>, application….”

Data anonymization
• real anonymization:
• un-anonymized data should not leave its usual working environment
• un-anonymized data should be only handled by authorized users
• this requires a highly portable & easy to use utility:
• python - what about Windows?
• R - don’t get me started…
• compiled CLI utility: so many things can go wrong (apart from which
OS, arch)
browsers are pretty bloated SW products; you can do video editing with it.
https://github.com/DataKind-SG/HOME

Data handling best practices
• Break up into small groups for discussions on below topics, and appoint a
spokesperson to tell the larger group your thoughts
• Excel is a versatile tool that many people can use, but it has its
drawbacks. Should future DataLearns cover coding to replace Excel?
Should we discourage using Excel or would that discourage some
people from participating?
• In the heat of the DataDive, it’s easy to forget to document the steps
that were taken in data cleaning and transformations. Any ideas on the
fastest, most painless way to document?
• Open for any suggestions!

DataKind SG sharing of our first DataDive

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DataKind SG sharing of our first DataDive

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DataKind SG sharing of our first DataDive