Data Analytics Consulting Centre

As always, a weekly read before we head into the weekend!

What Does It Actually Take to Build a Data-Driven Culture? Building a data driven culture is hard. To capture what it takes to succeed, the authors look at the first two years of a new data program at Kuwait’s Gulf Bank in which they worked to build a culture that embraced data, and offer a few lessons. First, it is important to start building the new cul...

Here's your read for the day - Use OpenAI text embeddings with horror movie descriptions by Julia Silge.

Use OpenAI text embeddings with #TidyTuesday horror movie descriptions | Julia Silge A data science blog

Do you agree or disagree? What are some of your red flags when joining a data team?

Red Flags to Look Out for When Joining a Data Team What to consider for in terms of data, roadmap, role, manager, tooling, etc.

? Learn more about this DataFrame library and some of its advantages over .

Getting Started with the Polars DataFrame Library Learn how to manipulate tabular data using the Polars dataframe library (and replace Pandas)

A light-hearted post to end off the work week. How else are you using A.I.?

35 Ways Real People Are Using A.I. Right Now Artificial intelligence models have found their way into many people’s lives, for work and for fun.

In recent years, there has been an explosion in the amount of patient Electronic Health Records (EHR) made publicly available. This presents an opportunity to create predictive models that leverage the large amount of data to help guide healthcare worker’s decision-making capacity.

Previous work in this field has not leveraged the full potential of the data, since they opt to only deal with a single modality of data, or do not leverage the temporality of the data.

Hence we would like to share a newly accepted paper
'Adversarial Learning for Improved Patient Representations'. The first author of this paper is Bharat Shankar, a student that our Director, Carol Hargreaves supervised. They attempted to create a network that creates a multimodal representation of EHR data by modeling it as a multiple sparse time series fusion task.

They show that the patient representation extracted is meaningful and useful for downstream classification tasks. Read this paper here: https://lnkd.in/g3XDybUf

Adversarial Learning for Improved Patient Representations In recent years, there has been an explosion in the amount of patient Electronic Health Records (EHR) made publicly available. This presents an opportunity to create predictive models that leverage the large amount of data to help guide healthcare worker’s...

We are thrilled to share this glowing testimonial from our data science intern, Yuxin. It has been a pleasure working with you and seeing your skills and knowledge grow. Congratulations on a job well done, and we know you will go on to achieve great things!

Unleash the power of and in with this insightful guide! Perfect for data science enthusiasts and practitioners looking to up their game 🚀

The Matrix Algebra of Linear Regression in R Explore how to estimate regression parameter using R’s matrix operators

Unlock the Power of Automated Feature Engineering with Upgini! 🚀 A comprehensive guide to augmenting your dataset with new and informative features using is here. Get ready to take your data analysis to the next level!

Automated Feature Engineering in Python A guide to augmenting your dataset with new and informative features using Upgini

Maximizing model performance just got easier! 📈 Learn about Hyperparameter Optimization with Grid Search, Random Search and Bayesian Optimization in this article.

Hyperparameter Optimization — Intro and Implementation of Grid Search, Random Search and Bayesian… Most common hyperparameter optimization methodologies to boost machine learning outcomes.

Discover the power of K-Means Clustering in Unsupervised Learning! 🧠💡 Check out the article on how this method can help uncover hidden patterns in the data.

Unsupervised Learning Method Series — Exploring K-Means Clustering Let’s explore one of the most famous unsupervised learning methods, k-means, and how it uses distances to map similar instances together.

From intern to invaluable team member! Check out Weiye's testimonial about his amazing experience at DACC