- Analytics and Machine Learning training in Q2 - Mar 24, 2017.
Learn Anomaly Detection, Deep Learning, or Customer Analytics in R online at Statistics.com with top instructors who are leaders of the field. Use code 3CAP17 before March 30 to save $170.
- 17 More Must-Know Data Science Interview Questions and Answers - Feb 15, 2017.
17 new must-know Data Science Interview questions and answers include lessons from failure to predict 2016 US Presidential election and Super Bowl LI comeback, understanding bias and variance, why fewer predictors might be better, and how to make a model more robust to outliers.
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- 4 ways to learn about Deep Learning, Anomaly Detection and more Data Science topics online at Statistics.com - Jan 11, 2017.
Online courses at Statistics.com are small, with rich and engaging content that includes readings, videos, quizzes, homework, projects, and practical work with software. Use promo code deepkdn17 to save.
- Statistics.com new courses: Anomaly Detection, Meta Analysis, IoT, Deep Learning, Spatial Analytics - Oct 5, 2016.
Five new courses from Statistics.com, fully online and asynchronous - interact with leading experts in private forums. Use promo code “kdn2016” for $50 off any course.
- Data Science vs Crime: Detecting Pickpocket Suspects from Transit Records - Sep 1, 2016.
A team of US and Chinese researchers has creatively used massive data collected by automated fare collectors for identifying thieves in the public transit systems. The system was tested in Beijing and was able to identify 93% of known pickpockets.
- A simple approach to anomaly detection in periodic big data streams - Aug 24, 2016.
We describe a simple and scaling algorithm that can detect rare and potentially irregular behavior in a time series with periodic patterns. It performs similarly to Twitter's more complex approach.
- Analytics, Security, Deep Learning, IoT, Data Science Online Courses - Aug 20, 2016.
Upcoming online courses include : Statistical and machine learning methods for detecting anomalies, identifying images, and processing data from sensors; Deep Learning; Internet of Things (IoT): Programming for Analytics; and Meta Analysis in R.
- 21 Must-Know Data Science Interview Questions and Answers, part 2 - Feb 20, 2016.
Second part of the answers to 20 Questions to Detect Fake Data Scientists, including controlling overfitting, experimental design, tall and wide data, understanding the validity of statistics in the media, and more.
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- On Why Sequels Are Bad and Red Light Cameras Aren’t As Effective - Feb 3, 2016.
Regression to the mean is a statistical phenomenon whereby extreme observations will tend to decrease (regress) towards the mean on subsequent readings. Regression to the mean is essentially a result of selection bias, learn more about it.
- Understanding Rare Events and Anomalies: Why streaks patterns change - Jan 8, 2016.
We often look back at the past year and an overall history of rare events, and try to then extrapolate future odds of the same rare event, based on that. We illustrate here, that rare past events have no usefulness in understanding the rarity of the same events in the future!
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- Anomaly Detection in Predictive Maintenance with Time Series Analysis - Dec 9, 2015.
How can we predict something we have never seen, an event that is not in the historical data? This requires a shift in the analytics perspective! Understand how to standardization the time and perform time series analysis on sensory data.
- Strata + Hadoop World 2015 San Jose – Day 2 Highlights - Mar 10, 2015.
Strata + Hadoop World 2015 was a great conference, and here are key insights from some of the best sessions on day 2.
- Top KDnuggets tweets, Mar 2-8: 6 categories in the Hadoop Ecosystem; How PayPal uses Deep Learning to fight fraud - Mar 9, 2015.
How #PayPal uses #DeepLearning and detective work to fight #fraud; Beginning #deeplearning with 500 lines of Julia; Processing frameworks for Hadoop and 6 categories in the #Hadoop Ecosystem; KDnuggets Poll results: #Analytics, #DataMining, #DataScience salary income by region.
- Top KDnuggets tweets, Nov 10-11: R on its way to the top 10; Using #MachineLearning to Detect Abnormalities - Nov 12, 2014.
Statistical language R on its way to the top 10; California rules #DataScience, but there's a very long tail; How to Explain #BigData to Your Grandmother; Using #MachineLearning to Detect Abnormalities in Time Series Data.
- Top KDnuggets tweets, Oct 27-28: Twitter Breakout detection in the wild; Marc Andreessen on #BigData and finance - Oct 29, 2014.
Dilbert on inability of designers predict results of A/B tests; Marc Andreessen @pmarc, web pioneer, VC @a16z on #BigData, upending finance; Will Deep Learning take over Machine Learning, make other algorithms obsolete?;.@WillJHenry @data_nerd @KirkDBorne Data Scientists don't wear bowties!
- Top KDnuggets tweets, May 21-22: Outlier Detection for Temporal Data; Become a Big Data mgr with #ieMBD - May 23, 2014.
Outlier Detection for Temporal Data ; 1.5M #BigData managers will be needed - Become one with #ieMBD; Goldman Sachs Surveillance Analytics; InformationWeek 10 Big Data Pros To Follow On Twitter.
- Outlier Detection for Temporal Data - May 22, 2014.
Outlier Detection for Temporal Data covers topics in temporal outlier detection, which have applications in numerous fields. It starts with the basic topics then moves on to state of the art techniques in the field.