Element of Science

Category: Uncategorized

  • Recommend the MIT “missing Computer Science semester” lectures

    If you code using GUIs or a mouse, go do the MIT “The Missing Semester of Your CS Education” course as soon as possible.

    I have been coding off and on for more than 20 years now. I have had Linux desktop systems on the recommendation of and in order to better work with colleagues. I use the shell somewhat regularly. And yet, even 10 minutes into the first lecture, I have learned a few things. This missing information both helps me be more efficient and gives me a hierarchical understanding of why some things I have tried have worked well and others not so much.

    One caveat is that passively listening to the lectures is not enough, even though it is easy to do. One complaint I have with the video is sometimes how quickly it moves on from the input text if I have a question about spacing, a capitalization, or the direction of a slash. Doing the examples and pausing the video until I’ve played with variations really helps me see how this plays out on my machine. I like this much more than some of the pre-loaded, artificial environments I have used to learn coding online. I can learn stuff that way, but it is hard to know where the problem is when it does not work in your environment.

  • Hello world!

    Welcome to Element of Science! This site has gone through several iterations and currently serves as a place to capture and share interesting bits about exploring the world through data.

    Here is a link to an inspiring 2005 TED talk by mathematician Peter Donnelly.

    This talk is important to me for 3 reasons:

    1. It discusses in a humorous way how communication gaps can arise when talking to an audience not already familiar with your work. “Modelling genes (jeans)” is a great job description that gets a different reaction than data scientist or bioinformatician.
    2. It reminds us that analyzing data is a specialization and like all specializations it requires judgment that is not necessarily intuitive to a lay person.
    3. It reminds us that there can be terrible, real-world consequences when we get data analysis and the related conclusions wrong. The world needs more contextual understanding to go with the pile of data we have and make. If the assumptions are violated or the data is not clean and applicable, we are by definition working from an inappropriate model.

    How can we apply these lessons in our everyday work?

    1. Seek common ground and when possible communicate with multiple modes (i.e. words and pictures).
    2. Never stop learning and continually question what we don’t know.
    3. Be explicit about our assumptions and which data counts.