Opinions
We welcome guest blogs and opinions - see submission guidelines.
Latest:
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7 Must-Haves in your Data Science CV - Apr 13, 2021.
If you are looking for a new role as a Data Scientist -- either as a first job fresh out of school, a career change, or a shift to another organization -- then check off as many of these critical points as possible to stand out in the crowd and pass the hiring manager's initial CV screen. -
Why Automated Feature Selection Has Its Risks - Apr 13, 2021.
Theoretical relevance of features must not be ignored. -
Deepfakes are now mainstream. What’s next? - Apr 9, 2021.
Deepfakes have become mainstream. Here we take a closer look at recent news about deepfakes, and what it all might mean for the future. -
Why machine learning struggles with causality - Apr 8, 2021.
If there's one thing people know how to do, and that's guess what caused something else to happen. Usually these guesses are good, especially when making a visual observation of something in the physical world. AI continues to wrestle with such inference of causality, and fundamental challenges must be overcome before we can have "intuitive" machine learning. -
How to Make Sure Your Analysis Actually Gets Used - Apr 7, 2021.
Few things are as demoralizing as seeing your data analysis tossed aside. Learn from these tips -- assembled from experience, academic research, and industry best practice -- on how to make sure your hard work receives the credit it deserves and delivers the value to your organization that you expect. -
KDnuggets Top Blogs Reward Program - Apr 6, 2021.
To encourage more high-quality and especially original contributions to KDnuggets, we announce KDnuggets Top Blogs Reward program, where we will pay the authors of top blogs published each month, starting with blogs submitted April 7 or later. -
One Million KDnuggets Visitors in March. Wow. - Apr 3, 2021.
KDnuggets has reached an amazing milestone of one million unique visitors in March 2021. We review how we got here. -
What did COVID do to all our models? - Apr 2, 2021.
An interview with Dean Abbott and John Elder about change management, complexity, interpretability, and the risk of AI taking over humanity. -
The 8 Most Common Data Scientists - Apr 1, 2021.
Admit it all you wanna-be, newbie, and old-old-school Data Scientists on the planet, whether you like it or not, you've probably behaved like one of these types. Or two. Or all eight.
March:
- Data vault: new weaponry in your data science toolkit
- What Took Me So Long to Land a Data Scientist Job
- Deep Learning Is Becoming Overused
- The question that makes your data project more valuable
- Data Science Curriculum for Professionals
- 15 Habits I Learned from Highly Effective Data Scientists
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How to Succeed in Becoming a Freelance Data Scientist
, by Devin Partida With recent growth in data science, now is the best time to get into freelancing. The following steps will help you get started with finding clients or help you improve your current strategy. - AI in Dating: Can Algorithms Help You Find Love?
- Introducing dbt, the ETL and ELT Disrupter
- Are you satisfied in your job? Take our Data Community Job Satisfaction Survey
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4 Machine Learning Concepts I Wish I Knew When I Built My First Model
, by Terence Shin Diving into building your first machine learning model will be an adventure -- one in which you will learn many important lessons the hard way. However, by following these four tips, your first and subsequent models will be put on a path toward excellence. - Is It Too Late to Learn AI?
- 8 Women in AI Who Are Striving to Humanize the World
- More Resources for Women in AI, Data Science, and Machine Learning
- Dask and Pandas: No Such Thing as Too Much Data
- 9 Skills You Need to Become a Data Engineer
- The Ultimate Guide to Acing Coding Interviews for Data Scientists
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Are You Still Using Pandas to Process Big Data in 2021? Here are two better options
, by Roman Orac When its time to handle a lot of data -- so much that you are in the realm of Big Data -- what tools can you use to wrangle the data, especially in a notebook environment? Pandas doesn’t handle really Big Data very well, but two other libraries do. So, which one is better and faster?
