ISSUE 396  ·   July 19, 2022

Trends

Is Data Scientist Still the Sexiest Job of the 21st Century?

It's been ten years since the authors posited that being a data scientist was the “sexiest job of the 21st century.” A lot's changed since then and even so, the role is in higher demand than ever. This post explores how "data science" has evolved in the past decade and where it's going next.
Harvard Business Review | Thomas H. Davenport and DJ Patil

Sponsored Link

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Tutorials, Projects & Opinions

Coming up with research ideas

This two-part series will help you decide what project you should work on next. Part 1 explores how to come up with good ideas. Part 2 dives into strategies for fleshing out an idea and how to decide if it's actually worth working on.
Marco Ribeiro

Thoughts on ML Engineering after first year of a PhD

The vehicle here is ML engineering but there are a lot of good insights in this post about research, real-world ML, roles, and business realities too. Great read.
Shreya Shankar

How to do Twitter Network Analysis and Visualization in R

This introductory tutorial shows how to analyze networks of people on Twitter and is a good case study of some of the methods used in Keith McNulty's Handbook of Graphs and Networks in People Analytics. It's easy to follow and by the end, you'll have an interactive visualization to explore and play with.
Level Up Coding | Keith McNulty

Ace Your Next Interview

Interview Query is an adaptive learning platform that's designed to give you everything you need to ace your next job interview. Whether your interest is data science, analytics, machine learning, or data engineering, Interview Query has you covered. Sign up for free.
( Data Elixir is partnered with Interview Query )

Resources

Critical Dataset Studies Reading List

In this collection of readings, the Knowing Machines team has created an entry point for the growing literature on ML datasets, covering the theory and practice of dataset design and use, as well as the politics, possibilities, and pitfalls of training data. There's a lot here but because datasets construct model worldviews, they're important to study. 
Knowing Machines

Tidy Finance with R

This open-source textbook is a great resource for readers who are interested in finance applications using R. It's a hands-on guide that provides a thorough implementation of typical approaches such as portfolio sorts, backtesting procedures, regressions, machine learning methods, and other related topics in empirical finance.
Christoph Scheuch, Stefan Voigt, and Patrick Weiss

Code & Tools

metrica: Prediction performance metrics

metrica is a new R package that provides more than 80 functions for quantifying and visualizing model performance. It supports regression, classification, machine learning, and forecasting models using a variety of evaluation metrics such as goodness of fit, confusion matrix, regression plots, and providing custom plots using ggplot2.
GitHub | Adrian Correndo

jupyter-scatter

jupyter-scatter is an interactive scatter plot widget for Jupyter Lab and Notebook that can handle millions of points and supports view linking.
GitHub | Fritz Lekschas

Events

JuliaCon 2022

JuliaCon 2022 is starting next week and will be online and free. Talks run from July 27th — July 29th. If you're just getting started with Julia, this week's pre-conference workshops are a great place to start.
JuliaCon

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