This post can also be found at https://kaparker.com/posts/technomads-datascience
On Monday 25th November we held our monthly TechNomads meetup where we focussed on Data Science. Joseph Allen & John Carney, Data Scientists based in Manchester, visited us at Liverpool Science Park to give an introduction to Data Science followed by a Q&A session. They provided fascinating insight drawn from their experience and shared plenty of tips and advice with us. The audience was made up of a mix of people working with data, including researchers, students and analysts as well as developers, aspiring data scientists and people working on side projects.
Why do we need Data Science?
The field of Data Science is broad and constantly evolving. As the hype for data science has grown, as has the confusion of what the role of a Data Scientist involves. As experienced Data Scientists and now running a Data Consultancy PDFTA, Joseph & John simplified the definition of Data Science to be: converting data into value.
One point that was highlighted that there is often over-emphasis on using Machine Learning, when a project may be quicker, simpler and cleaner to use other methods.
Data Science: converting data into value.
There are however lots of quick ways to get started with Machine Learning projects, including:
- Image classification: is the image a cat or a dog?
- Forecasting: using a python package
- Chatbots: integrating these into social media pages
How do we apply Data Science?
A revised version of the Data Science pyramid was introduced with the foundation of Data Science relying on good Software Engineering practices and how this is something we should all endeavour to improve.
Data Science is 95% exploring data, 5% Machine Learning
There are also many different paths into data science and Joe has summarised these in an article on Medium.
The Data Scientists i’ve met.
As an organiser of PyDataMCR I meet a lot of data types. I keep meeting the same Data Scientists, and I keep giving the…
Something I took away from the talk was that Data Science can still use the concept of a Minimum Viable Product. It’s better to apply a simple model and deploy to get feedback than to risk months for no value.
What is Data Science?
Joseph and John then went through some of the key terms that you hear in Data Science such as Model, Regression, Classification and gave examples of where these can be used — check out their slides for details!
Some example projects were presented which John and Joseph have worked on and a few of which are a good first Data Science project such as sentiment analysis of WhatsApp messages!
They then introduced Case Studies in Data Science including how Recommendation Engines can be used to search for similar and interesting products for the user. For this, UX teams, particularly in e-commerce, can provide insightful feedback to get example data of how users respond to this. Either Content-based or Behaviour-based recommendation systems can be used and python packages such as
prod2vec which is based around
word2vec can be used.
After a short break, the audience asked John & Joseph questions with some fascinating questions about:
- How often should I model be updated?
- The ethics and bias of data
- Where to get started as a Data Scientist?
Overall, it was a brilliant evening, thanks to John and Joseph for coming along to TechNomads and sharing your expertise and thanks everyone for coming! I certainly learned a lot and if you couldn’t attend, be sure to check out the slides!