…and how you can make the transition yourself

I left McKinsey to join Deepnote, a seed-stage startup. Going from consulting to the startup world is a well-trodden path, but based on my own experience and the questions I’ve been getting, it’s still a mysterious one for many. In this article, I want to address some of the questions that might be top-of-mind for you, if you’re a consultant thinking of making the leap yourself:

  • How do I know I’m ready to make the switch?
  • How do I grow my network outside consulting?
  • How can I learn about startups?
  • Where do I start the job search and how do…

Every data scientist knows that managing data, models, and results can be a tricky business — even more so when working with others.

Illustration by Victoria Kurpas on Shutterstock

Data science is a team discipline, but, ironically, data science notebooks generally don’t support collaboration. Workflows can get messy. As Mike, one of our users told us: “I got used to uploading my notebooks to a team GitHub repo to never be found again” or as Simon, another of our users, “versioning notebooks with git gets so confusing, I rather don’t do it at all”. With remote work becoming the new standard, such approaches simply do not cut it anymore. Data scientists need processes and tools that foster collaboration.

At Deepnote, we are rethinking notebooks with collaboration being central to…

At Deepnote, we aspire to be community-led. Here are some of the things we’ve learnt so far.

Community is the word of 2021, but this has been long in the making. Community-building has become a point of focus for many software companies, and is now spreading to new domains. The success of community-led companies like MongoDB, Twilio and Confluent points to a clear business case: when you create value for your users beyond your product, empower them and create conditions that allow them become the best version of themselves, the community scales up your presence in a way that traditional marketing cannot. …

Digging into the data to learn more about key trends in Jupyter notebooks.

2020 was a roller coaster, but the data science community is going strong. Interest in the data science domain has grown in the past year yet again. We dug into the data to learn more about the current state of a vital part of the data science ecosystem: the notebooks.

The analyses in this article were built using a Deepnote notebook. Click the link to run our code and explore the data yourself.

This article consists of 5 key sections:

  1. First, we explore key stats and trends of Jupyter notebooks on GitHub.
  2. We double down on popular Python libraries and…

The tools we use don’t just influence how fast we work, but also the kind of results we deliver.

Illustration by GoodStudio on Shutterstock

Notebooks have become the go-to tool for data scientists in both academia and industry, but they have also introduced a new set of challenges. Many conference talks and white papers have described the drawbacks. Notebooks don’t support collaboration, hinder reproducibility, encourage sloppy coding practices, don’t play well with the rest of your stack…you get the idea.

At Deepnote, we recognize the power of notebooks, but we see a lot of areas in which they can be improved upon. We want to create an interface that makes data scientists more productive and helps them reason and collaborate. We are building on…

I got through the check-in, bag drop, airport security, and to the gate within 10 minutes. I don’t often check my bag, but now I checked 2, both empty, to make space for all the things I would have to bring with me.

Making a trip in the midst of a global pandemic is an odd thing. The San Francisco airport was a ghost town, all the shops closed. The only available food outlet sported a large queue, so I settled for a smoothie and crisps, a poor choice for the 5.5-hour flight ahead.

I had been dreading the move-out…

Elizabeth Dlha

Product at Deepnote | SF by way of NYC by way of PRG

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