Tag: creativity

5 Quick Tips for Coding in the Classroom, by Kelly Bodwin

5 Quick Tips for Coding in the Classroom, by Kelly Bodwin

Kelly Bodwin is an Assistant Professor of Statistics at Cal Poly (San Luis Obispo) and teaches multiple courses in statistical programming. Based on her experiences, she compiled this great shortlist of five great tips to teach programming.

Kelly truly mentions some best practices, so have a look at the original article, which she summarized as follows:

1. Define your terms

Establish basic coding vocabulary early on.

  • What is the console, a script, the environment?
  • What is a function a variable, a dataframe?
  • What are strings, characters, and integers?

2. Be deliberate about teaching versus bypassing peripheral skills

Use tools like RStudio Cloud, R Markdown, and the usethis package to shelter students from setup.

Personally, this is what kept me from learning Python for a long time — the issues with starting up.

Kelly provides this personal checklist of peripherals skills including which ones she includes in her introductory courses:

Course TypeInstall/Update R and RStudioR Markdown fluencyPackage managementData managementFile and folder organizationGitHub
Intro Stat for Non-Majors⚠️⚠️
Intro Stat for Majors⚠️⚠️⚠️⚠️
Advanced Statistics⚠️⚠️
Intro to Statistical Computation

✅ = required course skill
⚠️ = optional, proceed with caution
❌ = avoid entirely
via https://teachdatascience.com/teaching_programming_tips/

3. Read code like English

The best way to debug is to read your process out loud as a sentence.

Basically Kelly argues that you should learn students to be able to translate their requirements into (R) code.

When you continuously read out your code as step-by-step computer instructions, students will learn to translate their own desires to computer instructions.

4. Require good coding practices from Day One

Kelly refers to this great talk by Jenny Bryan on “good” code and how to recognize it.

Kelly’s personal best practice included:

  • Clear code formatting
  • Object names follow consistent conventions
  • Lack of unnecessary code repetition
  • Reproducibility
  • Unit tests before large calculations
  • Commenting and/or documentation

For more R style guides, see my R resources overview.

5. Leave room for creativity

Open-ended questions (like “here’s a dataset, do a cool analysis“) let students explore and shine.


Large parts of the above were copied from this original article by Kelly Boldwin. I highly recommend you have a look at the original, and at the website hosting it: teachdatascience.com

Cover picture by freecodecamp.org.

GAN: Generative Adversarial Networks

GAN: Generative Adversarial Networks

A Generative Adversarial Network, GAN in short, is a machine learning architecture where two neural networks compete against each other. One of them functions as a discriminator, seeking to optimize its classification of data (i.e., determine whether or not there is a cat in a picture). The other one functions as a generator, seeking to best generate new data to fool the discriminator (i.e., create realistic fake images of cats). Over time, the generator network will become increasingly good at simulating realistic data and being able to mimic real-life.

The concept of GAN was introduced by Ian Goodfellow in 2014, whom we know from the Machine Learning & Deep Learning book. Although GANs are computationally heavy and still undergoing major development, their potential implications are widespread. We can see these architectures taking over all sort of creative work, where generating new “data” is the main task. Think for instance of designing clothes, creating video footage, writing novels, animating movies, or even whole video games. One of my favorite Youtube channels discusses multiple of its recent applications, and here are a few of my favorites:

If you want to know more about GANs, Analytics Vidhya hosts a short introduction, but I personally prefer this one by Rob Miles via Computerphile:

If you want to try out these GANs yourself but do not have the programming experience: Reiichiro Nakano made a GAN playground in (what seems) JavaScript, where you can play around with the discriminator and the generator to create an adversarial network that identifies and generates images of numbers.

gan_playground.png