I'm looking for consulting work in data science/data analysis. I'm based in Christchurch, New Zealand, but I'm happy to work remote.
I have a background in media & communication, so I'm happy working with clients of any technical ability ^_^ I've worked on projects ranging from surveys of social workers to network analysis, so I'm happy to try anything!
Always happy to see new MLOps books! The DevOps for Data Science is a new book by Alex K Gold. As the name implies, the book focuses on topics related to DevOps for data scientists. This includes the following:
✅ Command line
✅ Working with Linux systems
✅ Docker
✅ Scaling resources
✅ Network, domains, DNS, SSL, etc.
✅ Authentication
In the development version of {collapse} [v2.0.15, available via install.packages("collapse", repos = "https://fastverse.r-universe.dev")], the pivot() function has received a FUN argument to support aggregation, including a number or hard-coded internal functions that do this "on the fly". Initial benchmarks show that this significantly outperforms other pivot table options in R. More at https://sebkrantz.github.io/collapse/reference/pivot.html (feel free to test and give feedback). #rcollapse#rstats#DataScience
posit::conf(2024) virtual tickets are now available!
Join us on August 12-14—from all over the world—to live stream the incredible talks and keynotes that will be taking place in Seattle.
We understand that not everyone will be able to make the trip to Seattle this year, so we’re excited to offer a fully virtual offering for everyone as an alternate option.
REGISTER: https://posit.co/conference/
🐘✨ Great news from Marcela Victoria Soto at R4HR in Buenos Aires! She recently shared updates about their dynamic activities: "Data analysis is crucial for agile decision-making in companies." Join them on June 1, 2024, for the "Data Visualization in HR" event. Perfect for Spanish-speaking R users interested in HR analytics. 📅👥 Read more: https://www.r-consortium.org/blog/2024/05/30/r4hr-in-buenos-aires-leveraging-r-for-dynamic-hr-solutions
🚀 Anúncio: Nova Versão do Módulo Python crossfire!
A nova versão do módulo Python crossfire, desenvolvida por mim e @cuducos está disponível!
✨ Novidades:
Bug corrigido: Agora compatível com Google Colab!
Funcionalidade extra: Parâmetro que desempacota dados aninhados para facilitar a análise.
Ideal para jornalistas de dados e analistas! Cadastre-se na API do Fogo Cruzado e acesse os dados direto no Python.
I was looking for examples of interactive data visualization for a gradient descent algorithm, and I found this app by Lili Jiang. This desktop app is based on C++ and enables simulation and visualization of different gradient descent algorithms, such as momentum, AdaGrad, RMSProp, and Adam. The app enables to compare different methods simultaneously.
(1/2) Shiny Apps for demystifying statistical models and methods 🚀
This is a cool website that explains different statistical concepts with the use of interactive Shiny Apps. Ben Prytherch made this website from the Department of Statistics at Colorado State University.
Before I head off on a trip to various parts of not-Barcelona, I thought I’d share a somewhat provocative paper by David Hogg and Soledad Villar. In my capacity as journal editor over the past few years I’ve noticed that there has been a phenomenal increase in astrophysics papers discussing applications of various forms of Machine Leaning (ML). This paper looks into issues around the use of ML not just in astrophysics but elsewhere in the natural sciences.
The abstract reads:
Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology – in which only the data exist – and a strong epistemology – in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. Here, we identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. For example, when an expressive machine learning model is used in a causal inference to represent the effects of confounders, such as foregrounds, backgrounds, or instrument calibration parameters, the model capacity and loose philosophy of ML can make the results more trustworthy. We also show that there are contexts in which the introduction of ML introduces strong, unwanted statistical biases. For one, when ML models are used to emulate physical (or first-principles) simulations, they introduce strong confirmation biases. For another, when expressive regressions are used to label datasets, those labels cannot be used in downstream joint or ensemble analyses without taking on uncontrolled biases. The question in the title is being asked of all of the natural sciences; that is, we are calling on the scientific communities to take a step back and consider the role and value of ML in their fields; the (partial) answers we give here come from the particular perspective of physics
arXiv:2405.18095
P.S. The answer to the question posed in the title is probably “yes”.
James Baker on Bluesky: 'From 2025/26 University of Southampton DH will be running a new MSc in Digital Humanities (Data Science). Huge thanks to all my colleagues who've helped turn our little idea into a reality, especially the amazing
Lexi Webster https://www.southampton.ac.uk/courses/digital-humanities-data-science-masters-msc
And look out for a Humanities Data Science Lectureship we'll be advertising in the Autumn to lead this programme (ask me if you are interested in knowing more before the ad comes out).'