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Why Open Source Integration is Key to Success in the Era of Analytics Heterogeneity
By Marinela Profi, Global Product Marketing at SAS Up until recent years, companies were mainly focused on how analytics work and how the model building was being done. For many organizations, it was largely a lab exercise to see if there is value in data science.  Now, as that... Read more
Improving Data Quality for Superior Results
When you’re a data scientist, you see a problem, and you build a model to solve it. If it’s not as accurate as you were hoping, you tweak the model. But what if it’s the quality of your data causing skewed or flawed results? Kaitlin Andryauskas of Wayfair, wants... Read more
Dr. David Armstrong Added to ODSC Europe Keynote Lineup
It’s not often that humans discover a new planet, let alone 50 at once. And when we do discover one, it’s often through satellites, telescopes, and other traditional means. Recently, however, 50 planets were discovered via a machine learning algorithm largely written by Dr. David Armstrong from the University... Read more
How AI and ML are the Next Evolutionary Step for DevOps
The advent of Machine Learning (ML) and Artificial Intelligence (AI) has changed the way we perceive DevOps. It is providing the type of DevOps that is considered to be the need-to-have framework. For many software development companies, it is crucial to use AI and ML with DevOps to ensure... Read more
Introduction to GPT-3
Natural Language Processing (NLP) has become the darling of the deep learning community in the past several years and is now an accelerating area of research. There have been significant gains over this time with many NLP tasks and benchmarks going through a two-step process: training with a number... Read more
Could Your Machine Learning Model Survive the Crisis: Monitoring, Diagnosis, and Mitigation Part 1
As the world is changing rapidly around us, it is often questionable whether something we learned from the past is still valid. Machine learning models that make predictions of the future based on past data points are probably under most scrutiny from businesses in the current climate. Close monitoring... Read more
Building a Production-Level Data Pipeline Using Kedro
Suppose you are a self-taught data scientist who does not have much experience in software development. One morning, your senior executive asks you to provide an ad-hoc analysis – perks of the job, and when you do, she thanks you for delivering useful insights for her planning. Great! Three... Read more
From Good to Great: The 5 Skills You Need to Shine in Data Science
Almost three years ago, I switched from a career in academia to a career in business in a data science role. This used to be somewhat of a rare event, but today it is commonplace: not only is there a shortage of data scientists, but also people change careers... Read more
10 Compelling Machine Learning Ph.D. Dissertations for 2020
As a data scientist, an integral part of my work in the field revolves around keeping current with research coming out of academia. I frequently scour arXiv.org for late-breaking papers that show trends and reveal fertile areas of research. Other sources of valuable research developments are in the form... Read more
Why Causation Matters in Data Science
Inferring causality is vital to deriving actionable insights in product data science, similar to more established fields like public policy. Without understanding the causal impact, we cannot make influential product changes that will alter outcomes or behaviors in-line with product or policy goals. In my experience, because of an... Read more