# Tag: Outliers (11)

**How To Debug Your Approach To Data Analysis**- Dec 29, 2017.

Seven common biases that influence how we understand, use, and interpret the world around us.**Top 6 errors novice machine learning engineers make**- Oct 30, 2017.

What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.**KDnuggets™ News 17:n07, Feb 22: 17 Must-Know Data Science Interview Q&A; Removing Outliers in Python**- Feb 22, 2017.

Also Removing Outliers Using Standard Deviation in Python; Natural Language Processing Key Terms, Explained; Data Scientists Strongly Oppose Trump Immigration Ban.**Removing Outliers Using Standard Deviation in Python**- Feb 16, 2017.

Standard Deviation is one of the most underrated statistical tools out there. It’s an extremely useful metric that most people know how to calculate but very few know how to use effectively.**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.

**3 methods to deal with outliers**- Jan 3, 2017.

In both statistics and machine learning, outlier detection is important for building an accurate model to get good results. Here three methods are discussed to detect outliers or anomalous data instances.**Data Science Basics: What Types of Patterns Can Be Mined From Data?**- Dec 14, 2016.

Why do we mine data? This post is an overview of the types of patterns that can be gleaned from data mining, and some real world examples of said patterns.**A Neat Trick to Increase Robustness of Regression Models**- Aug 22, 2016.

Read this take on the validity of choosing a different approach to regression modeling. Why isn't L1 norm used more often?**20 Questions to Detect Fake Data Scientists**- Jan 1, 2016.

Hiring Data Scientists is no easy job, particularly when there are plenty of fake posers. Here is a handy list of questions to help separate the wheat from the chaff.**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.**What is numbersense – test yours**- Mar 25, 2014.

Kaiser Fung, Marketing and Analytics expert, and author of "Numbersense" book, explains what is numbersense in the age of Big Data. Test yours.