Opinions, Interviews, Reports
We welcome guest blogs and opinions - see submission guidelines.
Latest:
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How to Learn Python for Data Science the Right Way - Jun 14, 2019.
The biggest mistake you can make while learning Python for data science is to learn Python programming from courses meant for programmers. Avoid this mistake, and learn Python the right way by following this approach. -
Show off your Data Science skills with Kaggle Kernels - Jun 14, 2019.
Kaggle is not just about data science competitions. They also have a platform called Kaggle Kernels, using which you can build a stellar data science portfolio. -
5 Useful Statistics Data Scientists Need to Know - Jun 14, 2019.
A data scientist should know how to effectively use statistics to gain insights from data. Here are five useful and practical statistical concepts that every data scientist must know. -
Why Machine Learning is vulnerable to adversarial attacks and how to fix it - Jun 13, 2019.
Machine learning can process data imperceptible to humans to produce expected results. These inconceivable patterns are inherent in the data but may make models vulnerable to adversarial attacks. How can developers harness these features to not lose control of AI? -
The Data Fabric for Machine Learning Part 1-b – Deep Learning on Graphs - Jun 11, 2019.
Deep learning on graphs is taking more importance by the day. Here I’ll show the basics of thinking about machine learning and deep learning on graphs with the library Spektral and the platform MatrixDS. -
What you need to know: The Modern Open-Source Data Science/Machine Learning Ecosystem - Jun 10, 2019.
We identify the 6 tools in the modern open-source Data Science ecosystem, examine the Python vs R question, and determine which tools are used the most with Deep Learning and Big Data. -
The Infinity Stones of Data Science - Jun 10, 2019.
Do you love data science 3000? Don't want to be embarrassed in front of the other analytics wizards? Aspire to be one of Earth's mightiest heroes, like Kevin Bacon? Help make data science a snap with these simple insights. -
A Step-by-Step Guide to Transitioning your Career to Data Science – Part 2 - Jun 7, 2019.
How do you identify the technical skills a hiring manager is looking for? How do you build a data science project that draws the attention of a hiring manager? -
Top 10 Statistics Mistakes Made by Data Scientists - Jun 7, 2019.
The following are some of the most common statistics mistakes made by data scientists. Check this list often to make sure you are not making any of these while applying statistics to data science. -
Using the ‘What-If Tool’ to investigate Machine Learning models - Jun 6, 2019.
The machine learning practitioner must be a detective, and this tool from teams at Google enables you to investigate and understand your models. -
NLP and Computer Vision Integrated - Jun 5, 2019.
Computer vision and NLP developed as separate fields, and researchers are now combining these tasks to solve long-standing problems across multiple disciplines. -
Data Scientists Are Thinkers: Execution vs. exploration and what it means for you - Jun 4, 2019.
Data scientists serve a very technical purpose, but one that is vastly different from other individual contributors. Unlike engineers, designers, and project managers, data scientists are exploration-first, rather than execution-first. -
Clearing air around “Boosting” - Jun 3, 2019.
We explain the reasoning behind the massive success of boosting algorithms, how it came to be and what we can expect from them in the future.
May:
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A Step-by-Step Guide to Transitioning your Career to Data Science – Part 1
If you are looking to transition your career to data science, don't immediately start learning Python or R. Instead, leverage the domain expertise you have accumulated over the years. Here's a foolproof guide on how to do that. - What Does a Lady Tasting Tea Have to Do with Science?
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Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis
Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.
- How the Lottery Ticket Hypothesis is Challenging Everything we Knew About Training Neural Networks
- Why organizations fail in scaling AI and Machine Learning
- AI in the Family: how to teach machine learning to your kids
- ICLR 2019 highlights: Ian Goodfellow and GANs, Adversarial Examples, Reinforcement Learning, Fairness, Safety, Social Good, and all that jazz
- Fixing a Major Weakness in Machine Learning of Images with Hinton’s Capsule Networks
- 6 Industries Warming up to Predictive Analytics and Forecasting
- How do you teach physics to machine learning models?
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The Data Fabric for Machine Learning – Part 1
How the new advances in semantics and the data fabric can help us be better at Machine Learning
- Think Like an Amateur, Do As an Expert: Lessons from a Career in Computer Vision
- Why Data Professionals Should Negotiate Every Job Offer
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Machine Learning in Agriculture: Applications and Techniques
Machine Learning has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments. - What’s Going to Happen this Year in the Data World
- What my first Silver Medal taught me about Text Classification and Kaggle in general?
- Modeling 101
- Data Science Poem
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How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls
We outline some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.
- Ethical AI: EU’s New Guidelines and the Future of AI Trustworthiness
- 5 Things to Review Before Accepting That Data Scientist Job Offer
- “Please, explain.” Interpretability of machine learning models
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Data Scientist – Best Job of the Year in USA
CareerCast ranks Data Scientist as the top job in USA, with very good work environment, low stress, high growth, and median salary of $114,520.
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2019 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?
Vote in KDnuggets 20th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We will publish the anon data, results, and trends here.
- Data Science vs. Decision Science
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The Third Wave Data Scientist
An extensive look at what skills are needed to make up the portfolio of the third wave of data scientists.
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The 3 Biggest Mistakes on Learning Data Science
Data science or whatever you want to call it is not just knowing some programming languages, math, statistics and have “domain knowledge” and here I show you why.
- Strata SF day 2 Highlights: AI and Politics, Chatbots Insights, Forecasting Uncertainty, Scalable Video Analysis, and more
- XGBoost Algorithm: Long May She Reign
- Which Deep Learning Framework is Growing Fastest?