How I Created a Data Visualization With Zero Coding Skills, Thanks to ChatGPT




Data visualization is cool but at the same time it’s bit daunting that I need to know lots of tech stacks to actually implement it.

I totally agree that even when I was studying data visualization, I spent a pretty substantial amount of time learning how to code, handle web hosting, work with Python, SQL, and more, all while absorbing knowledge on information visualization.

Thankfully, we no longer need to deep dive into technical gatekeepers in this field. This doesn’t mean that technical knowledge is not valuable, but rather that we no longer need to be intimidated by technology because AI can spoon-feed us knowledge and do the heavy lifting for us. Are you excited? Let’s get started!

I’m going to build the data visualization that one of my students posted on weekly write-up homework.

Author(s): Soonk Paik

Publication Date: 4 April 2023

Publication Site: Nightingale

Tiny Python Projects




The biggest barrier to entry I’ve found when I’m learning a new language is that small concepts of the language are usually presented outside of any useful context. Most programming language tutorials will start with printing “HELLO, WORLD!” (and this is book is no exception). Usually that’s pretty simple. After that, I usually struggle to write a complete program that will accept some arguments and do something useful.

In this book, I’ll show you many, many examples of programs that do useful things, in the hopes that you can modify these programs to make more programs for your own use.

More than anything, I think you need to practice. It’s like the old joke: “What’s the way to Carnegie Hall? Practice, practice, practice.” These coding challenges are short enough that you could probably finish each in a few hours or days. This is more material than I could work through in a semester-long university-level class, so I imagine the whole book will take you several months. I hope you will solve the problems, then think about them, and then return later to see if you can solve them differently, maybe using a more advanced technique or making them run faster.

Author(s): Ken Youens-Clark

Publication Date: 2020

Publication Site: Tiny Python Projects

Python and Excel Working Together




When we’re exploring using data science tools for actuarial modelling, we’d often like to keep using existing Excel workbooks, which can contain valuable and trusted models and data.

Fortunately, using Python doesn’t mean abandoning Excel as there are some very powerful tools available that allow closely coupled interaction between the two.

These tools allow us to have the “best of both worlds” combining the ease of use of Excel and the power of Python.

We’re going to start with the simplest options and lead through to ways of building workflows that can contain both Excel workbooks and Python code.

With the range of tools available, it should be possible to have the ‘best of both worlds’: the familiarity of existing Excel workbooks and the power of the Python ecosystem working together.

Finally, if you’d like to learn  more, the author of XLWings, Felix Zumstein, has written an excellent book “Python for Excel“, which covers these topics in more detail. Highly recommended.

Author(s): Carl Dowthwaite

Publication Date: 1 Feb 2022

Publication Site: Jove Actuarial





Excel continues to be actuaries’ most widely used software tool, with more than
94.3% of respondents reporting that they use it at least once a day.
• With that understood, most actuaries (92.3%) use more than one tool.
• Actuaries want to increase their proficiency in R (47.2%), Python (39.1%), SQL
(30.8%), and Excel (26.0%).
• No tool had more than 50% of respondents indicating that they wanted to increase
their proficiency.
• Time is the greatest barrier to learning new technology. (80.5% of respondents felt
• Newer analysis methods such as tree-based algorithms and artificial intelligence (AI)
are not widely used (16.5% and 7.0%, respectively).

Author(s): Casualty Actuarial Society

Publication Date: March 2022

Publication Site: CAS Research Paper

The Life Modeling Problem: A Comparison of Julia, Rust, Python, and R




All of the submissions and algorithms above worked, and fast enough that it gave an answer in very little time. And much of the time, the volume of data to process is small enough that it doesn’t matter.

But remember the CUNA Mutual example from above: Let’s say that CUNA’s runtime is already as fast as it can be, and index it to the fastest result in the benchmarks below. The difference between the fastest “couple of days” run and the slowest would be over 721 years. So it’s important to use tools and approaches that are performant for actuarial work.

So for little one-off tasks it doesn’t make a big difference what tool or algorthim is used. More often than not, your one-off calculatons or checks will be done fast enough that it’s not important to be picky. But if wanting to scale your work to a broader application within your company or the industry, I think it’s important to be perfromance-minded[4].

Author(s): Alec Loudenback

Publication Date: 16 May 2021

Publication Site: JuliaActuary

Python for Actuaries





Explaining why actuaries may want to use the language python in their work, and providing a demo. Free recorded webcast, from the CAS.

Author(s): Brian Fannin, John Bogaardt

Publication Date: 6 February 2020

Publication Site: CAS Online Learning