Yesterday, I completed one more minor achievement on my own – I was able to receive enough upvotes on my works to receive a status of Kaggle Notebooks Expert. I’ve set it as a personal goal for myself for the end of this year, but thought that it is too optimistic (I will explain later why), and just continued ‘dancing as nobody sees it’.
(more…)A second talk I had in Zurich was related to my brave attempt to parse data about last MeasureCamps – fortunately for me, it was not so hard due to usage Replit as a digital session board solution by organizers of last events.
Attaching here links to notebooks with analysis:
– Descriptive overview/EDA of topics, levels, focus areas, locations, and presenters count of the latest MeasureCamps: https://lnkd.in/dECwApyA
– Cluster analysis of MeasureCamp sessions descriptions & titles with the usage of BERtopic framework: https://lnkd.in/dXjny7yW
– A dataset – I deployed it on Kaggle too: https://lnkd.in/dh5zeda5
One month ago I had a chance to visit Zurich to present there two topic. One of that is related to application of PCA + k-means for user personas definition – attaching here a deck.
I decided to continue my journey with Kaggle and different datasets there, and to combine it with learning something new. While I used to check there some neutral datasets, and disappointed myself again with the inability to compose correct ML techniques on competitions, I still like to check what I can do with video games datasets, or try to extract some gaming-related stuff in common data sources.
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For last few weeks I’m obsessed with Kaggle: https://www.kaggle.com/ – a platform for data scientists, ML/AI engineers and data analysts. For significant share of people it is one of entry points in a machine learning world – just because of infinite variety of competitions, datasets, examples of notebooks and relatively strong community.
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I talk to a lot of analysts every day – product analysts, data engineers, web and digital analysts, CRO managers and growth hackers. When discussing the next analytics project, people are ready to spend days describing the complexity of the implemented data pipelines, the peculiarities of using such wonderful platforms as dbt, Dataform, the endless variety of tools inside GCP, AWS and Azure, and the opportunities they provide them.
And I really like discussions with that people! What they are actually doing, how do they achieve things they wanted to achieve. Sometimes people from data act like a stealth warriors, but then prepare a comprehensive analytics infrastructure, which then can be used by business for decision making process. Yeah, it is hard to overestimate the value of this activity.
But in this post, I want to speak not about data pipelines preparation, but about actual role of digital analyst, and maybe about actual opportunities, which currently we have.
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