2018 Jul Opinions, Interviews
All (104) | Courses, Education (5) | Meetings (15) | News, Features (8) | Opinions, Interviews (25) | Top Stories, Tweets (9) | Tutorials, Overviews (36) | Webcasts & Webinars (6)
- Weapons of Math Destruction, Ethical Matrix, Nate Silver and more Highlights from the Data Science Leaders Summit - Jul 31, 2018.
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.
- What is Normal? - Jul 31, 2018.
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?
- 5 reasons data analytics are falling short - Jul 30, 2018.
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.
- How to Lie with Data Science - Jul 27, 2018.
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.
- Data Science For Business: 3 Reasons You Need To Learn The Expected Value Framework - Jul 26, 2018.
This article highlights the importance of learning the expected value framework in data science, covering classification, maximization and testing.
- The Industries That Can Benefit Most From Predictive Analytics - Jul 26, 2018.
Predictive analytics are useful for doing all those things and more, and could increase the overall competitiveness of individual companies or entire sectors.
- 9 Reasons why your machine learning project will fail - Jul 25, 2018.
This article explains in detail some of the issues that you may face during your machine learning project.
- Why Germany did not defeat Brazil in the final, or Data Science lessons from the World Cup - Jul 24, 2018.
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.
- Happy 25th Birthday, KDnuggets - Jul 23, 2018.
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.
- Building A Data Science Product in 10 Days - Jul 23, 2018.
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.
- SuperDataScience Podcast: Insights from the Founder of KDnuggets - Jul 21, 2018.
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.
- Chaos is needed to keep us smart with Machine Learning - Jul 20, 2018.
This post analyses why the chaotic nature of our lives can be used to improve machine learning algorithms.
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Causation in a Nutshell - Jul 20, 2018.
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. - Key Takeaways from the Strata San Jose 2018 - Jul 16, 2018.
By dropping 'Hadoop' from its name, the @strataconf 2018 in San Jose signaled the emphasis on machine learning, cloud, streaming and real-time applications.
- Beating the 4-Year Slump: Mid-Career Growth in Data Science - Jul 16, 2018.
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.
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Cartoon: Data Scientist was the sexiest job of the 21st century until … - Jul 14, 2018.
This Data Scientist thought that he had the sexiest job of the 21st century until the arrival of the competition ... - The Future of Map-Making is Open and Powered by Sensors and AI - Jul 13, 2018.
This article investigates the future of map-making and the role of Sensors, Artificial Intelligence and Machine Learning within that.
- What is Minimum Viable (Data) Product? - Jul 12, 2018.
This post gives a personal insight into what Minimum Viable Product means for Machine Learning and the importance of starting small and iterating.
- AI Solutionism - Jul 12, 2018.
Machine learning has huge potential for the future of humanity — but it won’t solve all our problems.
- GDPR after 2 months – What does it mean for Machine Learning? - Jul 11, 2018.
Almost 2 months on from the GDPR introduction, how was machine learning affected? What does the future hold?
- How to Balance the Load on a Data Team - Jul 11, 2018.
This post will help you to better understand a data team’s workflow and allocate their resources to business users.
- Data science of the connected vehicle: perspectives, applications and trends - Jul 9, 2018.
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.
- Weak and Strong Bias in Machine Learning - Jul 6, 2018.
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.
- SQL Cheat Sheet - Jul 2, 2018.
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.
- Why a Professional Association for Data Scientists is a Bad Idea - Jul 2, 2018.
This post presents the argument against having a professional association for data scientists.
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