[Day 169] Writing first version of introduction for my paper + day 2 of IEUK

 Hello :)
Today is Day 169!


A quick summary of today:
  • started writing introduction for my paper
  • attended day 2 of IEUK


Firstly, in regards to my paper

I decided to give overleaf a go. It is a popular latex editor so I went with it. I will share with white covering most of the part I bit.

I managed to write a whole page. Not sure if that is a lot, but I am quick to recall what I have read. Every time I wrote a university assignment or previous short research proposals, I end up changing the sections a lot. It is pretty normal (for me at least) that the initially written version is never the final version. Nevertheless, I also learned about bibtex and how its used for citations and referencing. At the moment I am thinking to give myself time until the end of June to write this intro + literature review + data plan + expected results, and then submit to my professor for review. And it would be good to do it at the end of June since it is exam season now for him. 
(I am also a little paranoid that because I am using the free version my progress will be lost haha. So just to be extra safe I copy-pasted everything into a google drive docs file)

I think a cool app to do in the future is a 'homemade' RAG app that takes all the read papers and then I can ask it questions. Because today I knew I had read some parts somewhere but it was not in my notes, so I ended up using 1 word ctrl+F searches over many papers. 


As for Internship Experience UK day 2

I think the only highlight of the day for me is hearing from Lloyds Banking Group employees. It felt I was back there again. 

What skills do we need to become a data scientist?

  • computer science
    • python and sql
    • cloud platforms
    • model performance and security
  • maths/statistics
    • statistical models
    • forecasting
    • optimisation
    • ML
    • DL
  • business/domain knowledge
    • understanding business problems
    • applying domain specific insight
    • adding business value
What types of data scientists are in the Bank?

  • data scientist modeller
    • builds ML models which find patterns in data to make predictions
    • key skills: python/coding, stats, ML, business knowledge
  • ML engineer
    • productionises ML models
    • key skills: python/coding, cloud platforms, software engineering, ML
  • data science leader
    • uses DS experience and knowledge to lead teams and translate business problems into DS solutions
    • key skills: business knowledge, communication, ML, stats
  • AI ethics expert
    • creates the tools, frameworks and guardrails which ensures DS and AI are developed responsibly
    • key skills: AI ethics, governance, data privacy, security, ML

Types of ML

  • supervised learning
  • unsupervised learning
  • reinforcement learning

Finally, the IEUK team assigned us some mini-project where we play to role of a project manager and we have to deliver on some items. I will talk about it in tomorrow's post because it is 2am now. 


That is all for today!

See you tomorrow :)

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