Links to free big-data-sets

Many people who are starting their journey with big data and analytics find it hard to get their hands on the right kind of data to play or experiment with.

Most of the time, people have enthusiasm, they are learning the skill too, but they just don't have the right kind of dataset to apply their newly acquired skills.

Democratising data has been at the forefront of discussions for many data pioneers. Through their efforts and with some re-alignment of technology priorities, some government bodies have opened up their datasets to the public.

As a result, here is a set of links (reproduced) to some of the free sources.

1. Data.gov http://data.gov The US Government pledged last year to make all government data available freely online. This site is the first stage and acts as a portal to all sorts of amazing information on everything from climate to crime. 
2. US Census Bureau http://www.census.gov/data.html A wealth of information on the lives of US citizens covering population data, geographic data and education. 
3. Socrata is another interesting place to explore government-related data, with some visualisation tools built-in. 
4. European Union Open Data Portal http://open-data.europa.eu/en/data/ As the above, but based on data from European Union institutions. 
5. Data.gov.uk http://data.gov.uk/ Data from the UK Government, including the British National Bibliography – metadata on all UK books and publications since 1950. 
6. Canada Open Data is a pilot project with many government and geospatial datasets. 
7. Datacatalogs.org offers open government data from US, EU, Canada, CKAN, and more. 
8. The CIA World Factbook https://www.cia.gov/library/publications/the-world-factbook/Information on history, population, economy, government, infrastructure and military of 267 countries. 
9. Healthdata.gov https://www.healthdata.gov/ 125 years of US healthcare data including claim-level Medicare data, epidemiology and population statistics. 
10. NHS Health and Social Care Information Centre http://www.hscic.gov.uk/home Health data sets from the UK National Health Service. 
11. UNICEF offers statistics on the situation of women and children worldwide. 
12. World Health Organization offers world hunger, health, and disease statistics. 
13. Amazon Web Services public datasets http://aws.amazon.com/datasets Huge resource of public data, including the 1000 Genome Project, an attempt to build the most comprehensive database of human genetic information and NASA ’s database of satellite imagery of Earth. 
14. Facebook FB +0.32% Graph https://developers.facebook.com/docs/graph-api Although much of the information on users’ Facebook profile is private, a lot isn’t – Facebook provide the Graph API as a way of querying the huge amount of information that its users are happy to share with the world (or can’t hide because they haven’t worked out how the privacy settings work). 
15. Face.com: A fascinating tool for facial recognition data. 
16. UCLA makes some of the data from its courses public. 
17. Data Market is a place to check out data related to economics, healthcare, food and agriculture, and the automotive industry. 
18. Google Public data explorer includes data from world development indicators, OECD, and human development indicators, mostly related to economics data and the world. 
19. Junar is a data scraping service that also includes data feeds. 
20. Buzzdata is a social data sharing service that allows you to upload your own data and connect with others who are uploading their data. 
21. Gapminder http://www.gapminder.org/data/ Compilation of data from sources including the World Health Organization and World Bank covering economic, medical and social statistics from around the world. 
22. Google GOOGL +0.66% Trends http://www.google.com/trends/explore Statistics on search volume (as a proportion of total search) for any given term, since 2004. 
23. Google Finance https://www.google.com/finance 40 years’ worth of stock market data, updated in real time. 
24. Google Books Ngrams http://storage.googleapis.com/books/ngrams/books/datasetsv2.htmlSearch and analyze the full text of any of the millions of books digitised as part of the Google Books project. 
25. National Climatic Data Center http://www.ncdc.noaa.gov/data-access/quick-links#loc-clim Huge collection of environmental, meteorological and climate data sets from the US National Climatic Data Center. The world’s largest archive of weather data. 
26. DBPedia http://wiki.dbpedia.org Wikipedia is comprised of millions of pieces of data, structured and unstructured on every subject under the sun. DBPedia is an ambitious project to catalogue and create a public, freely distributable database allowing anyone to analyze this data. 
27. New York Times http://developer.nytimes.com/docs  Searchable, indexed archive of news articles going back to 1851. 
28. Freebase http://www.freebase.com/ A community-compiled database of structured data about people, places and things, with over 45 million entries. 
29. Million Song Data Set http://aws.amazon.com/datasets/6468931156960467 Metadata on over a million songs and pieces of music. Part of Amazon Web Services. 
30. UCI Machine Learning Repository is a dataset specifically pre-processed for machine learning. 
31. Financial Data Finder at OSU offers a large catalog of financial data sets. 
32. Pew Research Center offers its raw data from its fascinating research into American life. 
33. The BROAD Institute offers a number of cancer-related datasets. 

Credit to Forbes article at

http://www.forbes.com/sites/bernardmarr/2016/02/12/big-data-35-brilliant-and-free-data-sources-for-2016/#5b2a54cf6796

Hackathon - Fintechathon

A hackathon is a hacking marathon wherein many people are invited to attack problems around a theme.

I recently attended a hackathon over the valentines weekend.  Organised by StartupBootCamp Fintech, it was attended by about 100 people. Many ideas, many teams, some partners i.e. corporates with their own challenges.

I was initially team less, but then found some others who were in in my situation.  We formed a team,  around my favourite topic, data analytics.  We had two business development guys, Adam and Oksana, two java programmers, Nelson and Nick, and a mobile app developer Vlad.  

Hackathon teams are formed around ideas, wherein someone with an idea takes on the ownership, and the collects the team around it.  Things are focussed from moment one, and the march forward is fairly disciplined and fast, thats why the name hackathon... keep hacking, for long, long days and nights.

We, had the other way round.. all of us were teamless and therefore put together as a team. We had no idea to start with.  As a result of that, we spent better part of the friday evening and saturday zeroing on the problem to attack.  

Finally we decided to go ahead with an data analytics piece. I won't chalk out the details here, but its something that the marketing guys always love and like.  To know when their customers are off to a life event, and therefore could be offered some product.

By Sunday morning, we had lost two team members, one to a different idea and one to sleep. Vlad hadn't slept in 4 nights, so he kept sleeping much of Sunday.

As a result, we ended up a team without anyone who could do any UI design, and therefore only some backend API calls, some analytics pieces and nothing to show off.

The result was that, we couldn't show any working model in our pitch presentation and had to contend with a presentation only, which tried to describe our idea to the judges.

Of course we lost, but then it was a very nicely spent weekend, met some very nice people, made some contacts, and possibly a future for the idea.

Data Lineage.. what is that ?

It is one of those buzzwords, that keep doing the circuit every once in a while. Almost every enterprise wants to do the analysis regarding this, and is almost always hard to find people with knowledge/experience doing this kind of analysis.

For the unaware, Data Lineage is basically (really in very short words) a study of the data from its source to its eventual target, similar to what we'd do for our generation tree, we analyze the generation analysis of the data we are dealing with.

Starting from the source of the data, it travels through different subsystems, sometimes going through transformations, and thus possibly changing shape too...

Informatica had a very interesting blog post around this (already in 2007), which can turn out to be fairly informative.

http://blogs.informatica.com/perspectives/index.php/2007/05/21/data-lineage-where-did-that-data-come-from/

Just found out about this amazing thing...

A research initiative at Stanford University, Data Wrangler.. Wonderfully helping for analysts.

Try a demo video here -




And read more about it on http://vis.stanford.edu/wrangler

Wrangler Demo Video from Stanford Visualization Group on Vimeo.