Domino Data Lab hosted its first ever Data Science Leaders Summit at the lovely Yerba Buena Center for the Arts in San Francisco on May 30-31, 2018. Cathy O'Neil, Nate Silver, Cassie Kozyrkov and Eric Colson were some of the speakers at this event.
I saw an article recently that referred to the normal curve as the data scientist's best friend. We examine myths around the normal curve, including - is most data normally distributed?
When it comes to big data, possession is not enough. Comprehensive intelligence is the key. But traditional data analytics paradigms simply cannot deliver on the promise of data-driven insights. Here’s why.
This post is not really about how to lie with Data Science. Instead, it’s about how we may be fooled by not giving enough attention to details in different parts of the pipeline.
Predictive analytics are useful for doing all those things and more, and could increase the overall competitiveness of individual companies or entire sectors.
We review World Cup predictions (all failed), examine what makes such events difficult to predict, and suggest 3 golden rules to determine when you can trust the predictions.
Twenty five years covering Data Mining, Knowledge Discovery in Data, KDD, Predictive Analytics, Big Data, Data Science, Machine Learning, and AI - my reflections on 25 years of publishing and editing KDnuggets.
At startups, you often have the chance to create products from scratch. In this article, the author will share how to quickly build valuable data science products, using his first project at Instacart as an example.
I talk to Kirill Eremenko about my journey to data science, how KDnuggets started, why you should start honing your machine learning engineering skills at this very moment, what's the future of data science, and more.
Every move we make, every breath we take, and every heartbeat is an effect that is caused. Even apparent randomness may just be something we cannot explain.
By dropping 'Hadoop' from its name, the @strataconf 2018 in San Jose signaled the emphasis on machine learning, cloud, streaming and real-time applications. Â
This article provides a list of resources for data scientists who are transitioning from early-career/entry-level positions to more established roles. Surveys have shown a sharp decrease in satisfaction starting around 4 years into the profession, and resources are less obvious and readily available for professionals who have a good handle on the basics of data science than they are for beginners.
The application of data science to streaming data from vehicles is an emerging field. Here we review general trends and some specific examples of relevant data feeds and applications where data science can deliver value.
With the arrival of the GDPR there has been increased focus on non-discrimination in machine learning. This post explores different forms of model bias and suggests some practical steps to improve fairness in machine learning.
A good programmer or software developer should have a basic knowledge of SQL queries in order to be able retrieve data from a database. This cheat sheet can help you get started in your learning, or provide a useful resource for those working with SQL.
Causation in a Nutshell
Cartoon: Data Scientist was the sexiest job of the 21st century until …
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