Posts

  • More Thoughts on GTD

    I recently shared some thoughts on the getting things done (GTD) method. I gave some general pointers regarding the methodology, and promised to go a bit deeper regarding tools and best practice that works for me. My place of work is in the Microsoft camp, which is why I’m using Microsoft ToDo, but these tips should be useful no matter what tool you use.

  • Academia to Industry, Part 2 - First Steps in Industry

    I recently wrote about taking the leap from academia to industry. I covered some pros and cons, and gave you advice for lading your fist industry gig. In this post, I’d like to focus on the next steps. We’ll cover how you can structure your learning and what will help you thrive in the first 6-18 months.

  • Academia to Industry, Part 1 - Taking the Leap

    A colleague asked me recently to talk over lunch about my journey from academia to industry. It’s a topic I get asked about frequently, so I thought it’s worth to post about. By definition this is a personal story, but hopefully there will be some takeaways that might be useful for you. It is also a big topic, so I’ll cover it in several posts. This first post will give you some context and talk about taking the leap from academia to industry. Later posts will talk about shaping your career, taking on leadership positions, and keeping a connection with academia.

  • Thoughts on Getting Things Done

    With a corporate job, a side hustle lecturing and working with students, and having a toddler at home, I really felt the need for a tool to help me organise and keep productive. I’ve started working using the getting things done (GTD) method. It worked well for me in some areas, in others it didn’t, and I have some thoughts on why. I want to share some lessons learned that might help others.

  • Lying With Data - Why Business Insight Matters

    When using data science in a business context, we aim to make decisions based on our data. This can have several advantages. Machines can potentially take decisions faster, at higher volume, and with less errors than humans, giving organizations that master data-driven decision making a huge competitive advantage. However, not all models and data sets are created equally, and business insight can make the difference between winning and shooting yourself in the foot.

  • Exploring IoT - Part 1: The Hardware

    In 2022 the number of connected IoT devices grew to 14.4 billion, according to IoT Analytics, a market research company. This is a mind-boggling number. IoT is here to stay and thrive, so I felt like it would be fun to get my hands dirty and try to hack an IoT environmental monitor at home. In this post, we’ll look at the IoT hardware.

  • Prediction Errors With Less Errors

    Last time we looked at model parameters and how to estimate their errors. Today we will take things a step further and look at how to use one of the methods we learned about, bootstrapping, to estimate errors on model predictions. As I previously explained, this is essential when basing business decisions on data. Let’s dive in!

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