Data Science London is a non-profit organization dedicated to the free, open, dissemination of data science.
We are the largest data science community in Europe.
We are more than 1,900 data scientists and data geeks in our community.
What we do
Data Science London is a community of data scientists that meets regularly to discuss data science ideas, concepts, and tools, methods and technologies used by many startups to analyse large scale data (big data), extract predictive insight, and exploit business opportunities from data products.
Our members research and discuss data science topics like: big data analytics, distributed data analytics, Pig, data mining, machine learning, Mahout, scalable data analytics, predictive analytics, Cascalog, heuristics, statistical computing, R language, Python, Clojure, Incanter, mass text mining, NLP, large scale analytical paradigms, data science opportunities & challenges, data science learning & training…
We are very active and do lots of activities:
- Community events
- Monthly Community Meetups, go to this link
- Data Science Hackathons, go to this link
- Data Science Projects, got to this link
- Data Science Training and Courses at The Data Science Academy
- Data Science R&D, see our twitter feed @ds_ldn for info on interesting data science projects
Big Data is not about data volume only. On ‘What is Big Data?’
There is a lot of confusion out there about the actual meaning of ‘Big Data’
- ‘Big Data’ is the main driver for a new large-scale data analytical paradigm…
- ‘Big Data’ means the 4 Vs of large scale data: Velocity, Variation, Variety, and… Volume too…
- ‘Big Data’ involves complex, semi-structured, poly-structured, and structured data too…
- Big Data’ is when the complexity, speed & volume of data requires you to transform your IT infrastructure…
- ‘Big Data’ involves adding new external poly-structured data sources to your internal structured data sources
When most people talk about Big Data, they are mainly referring to Big Data as an IT engineering problem. We call this ‘data plumbing. Data Science main concern is not ‘data plumbing’
Big Data= Crude Oil, Data Science= Refining Oil
If you think in terms of data as ‘crude oil,’ Big Data is pretty much about dealing with the ‘crude oil.’ Big Data is about extracting the ‘crude oil,’ transporting it in ‘mega-tankers,’ siphoning it through ‘pipelines,’ and storing it in massive ‘depots.’
On the other hand, Data Science is about ‘refining the crude oil.’ A data scientist works at the “oil refinery” and creates refined data products from the “crude oil” with an aim to answer a set of business questions.
Data Science starts by asking key business questions: What is that we don’t know?
Data Scientists are interested in extracting predictive insight from large-scale data, they are much less interested in data plumbing.
Data Science does not start by analysing big data. Data Science starts by asking a key business question, understanding what business questions haven’t been answered, and then finding a way to obtain actionable predictive insight from data.
Data Science is about exploratory analysis, data discovery and curiosity.
Data Science is not the same as Business Intelligence, Analytics, or Data Mining.
We are interested in the Practice of Data Science (DS) understood as:
- The ideas, concepts, and theories relative to inferring, deducting, simulating, automating, learning, recommending, or making intelligent decisions and extracting actionable, predictive insight from large-scale data
- The debate between More Data vs. Better Models
- The debate between Machine Learning vs. Domain Expertise
- The tools, technologies, and skills needed to deliver predictive, actionable insight from large-scale data
- The methods and processes needed to create data products that deliver business value
- The business case of data science as an immediate driver of positive business impact and growth
- The skills and knowledge gaps between “traditional” data analysts & BI roles, and developers, and the “emerging” Data Scientist role
- The new kind of information insight paradigm as an enhancement, complement or replacement of ‘traditional analytical’ models
Our mission and group values are:
- Free dissemination of Data Science
- Free debate, discussion, and forum of ideas
- We like to meet new people to share our experiences and points of view
- Building a community of Data Scientists
- Passionate about Data Science
- We are technology agnostic
- We are vendor independent
- We promote Open Source and Open Data
- We welcome sponsors as long as they agree not to pitch their products and agree not to use our community brand for commercial purposes
Data Science London is a founding member of Data Science Global