Chief data scientists are typically recruited from a pool of talented data scientists. And though the transition from talented to chief sounds like a small and logical step, the role is very different.
Data scientists are solving problems in the front line. Every day they are sharpening their technical, coding, problem-solving and communication skills. Some aspire to become chief data scientists. And when they finally get the position, they are amazed by how different the new role is.
Personally, I struggled with not building models, learning new technologies, studying new methods and trying them out all day, every day. I felt like I was missing out and the whole industry was moving ahead while I stayed still. …
I saw the image above in a blog post by Sandeep Uttamchandani about examples of what can go wrong in a machine learning project. People often read these articles as funny anecdotes that can never happen to them. Machine learning projects usually require significant investment (talent, time, and technology). Accordingly, it would be a mistake to think that the probability of making a mistake is more important than what would be its consequences.
Also, I subscribed to yet another newsletter — Machine Learning Ops Roundup. Check it out if MLOps is of interest to you.
But now, let’s get into the reading list! …
My week, apart from being extremely busy, was epitomised by data visualisations. Until this week, I’d found most of the visualisations I enjoyed either through The Big Picture, FiveThirtyEight or randomly. Whereas, I stumbled across the visualisation of 645 hours of watching series! It is noteworthy for two reasons. Firstly, it reminded me how lucky I have been with Covid-restrictions in Switzerland having spent almost the same time on my bike last year. But more importantly, it navigated me to new sources of great data visualisations — @VeryData_365, #MakeoverMonday and the whole community around it.
Other topics on this week’s reading list include organisation of data teams and specifics of data science projects. …
Data is powering our economy. Businesses are using data to make better decisions, refine operations and create new revenue streams. Or are they?
Companies are certainly amassing vast amounts of data every day. Whilst the number (and variety) of data professionals on their payrolls is growing each year. Yet, data technology isn’t for free — as skyrocketing revenues from the large tech companies attest.
First week of the year. Everyone is back from holidays full of energy and ideas. People around me are querying which new technologies were identified as ‘to watch’ in 2021. And, everybody is launching their plans for the year ahead. I sincerely wish we will all be able to follow our intentions as much as possible and not get punched in the face by everyday reality too quickly.
But it will happen, as it does every year. So, don’t be surprised!
A bit of everything this week; enjoy the variety of topics or pick based on your interests.
The time of predictions, planning and resolutions is here. Data influencers are writing about the next big trends for 2021, practitioners decide what they plan to achieve in 2021 and how to go about it, and individuals are sharing their commitment to up-skill, learn and improve as data professionals.
The end of the year provides a natural opportunity to reflect on the industry, your business and yourself, and analyse what should be the next level and how to get there.
It is no surprise that these are the topics of this week’s reading list.
2020 has been a crazy year. The start-up I worked for went belly-up and covid restrictions limited the travel to the minimum so I found myself in Switzerland with plenty of time on my hands.
Luckily, Switzerland had never really banned cycling so I was able to enjoy the perks of this cycling paradise. Switzerland has high mountains, beautiful lakes, top-quality roads and attentive drivers, which makes the country a great place to ride a bike.
“Give a man a fish and feed him for a day. Teach a man to fish and feed him for a lifetime. …
Great visualisations are informative, intriguing, entertaining and action-inspiring. But most of all, they are stories told with data. FiveThirtyEight has made some great ones. The picture above is from their article celebrating the best — and weirdest — charts their visual jurnalists have published in the last 12 months. I enjoy these articles. Firstly, because of the aesthetics and secondly because I often find new angles even to the old well-known stories.
End of the year is traditionally hectic and this one is not any different. Therefore, the list only contains three articles. …
The image above is from Bill Schmarzo’s article about data monetisation. A topic that has become very close to my heart this year and is a key focus in my new business venture. Indeed, data has the potential of being a very valuable asset. But it has no value sitting on servers. It needs to be put to use. Let me quote from the article:
Companies must transition their executive mindset from “data as a cost to be minimized” to “data as an asset that will fuel the economic growth of the 21st century.”
With that in mind, let’s get into the best articles I’ve read this week. …
While some people are spending their time creating a Tableau visualisation of Trevor Noah’s hoodie collection I’m trying to achieve my cycling goal of climbing 200'000 m in 2020. It’s difficult to argue which is more beneficial for humankind!
This week’s list covers topics from data intuition to regular expressions. Here it comes.