# Tag: Statistics (164)

**A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more**- Dec 7, 2018.

A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.**The 5 Basic Statistics Concepts Data Scientists Need to Know**- Nov 13, 2018.

Today, we’re going to look at 5 basic statistics concepts that data scientists need to know and how they can be applied most effectively!**Quantum Machine Learning: A look at myths, realities, and future projections**- Nov 5, 2018.

An overview of quantum computing and quantum algorithm design, including current state of the hardware and algorithm design within the existing systems.**How I Learned to Stop Worrying and Love Uncertainty**- Oct 24, 2018.

This is a written version of Data Scientist Adolfo Martínez’s talk at Software Guru’s DataDay 2017. There is a link to the original slides (in Spanish) at the top of this post.**University of San Francisco: Assistant Professor, Tenure Track, Mathematics and Statistics [San Francisco, CA]**- Oct 17, 2018.

The University of San Francisco invites applications for a tenure-track Assistant Professor position to begin August 2019. We seek well-qualified candidates in the areas of applied mathematics or statistics, with a focus on the extraction of knowledge from data.**Mindstrong Health: Sr Data Scientist / Machine Learning, Statistics, Coding [Palo Alto, CA]**- Oct 17, 2018.

Mindstrong Health is seeking a Sr Data Scientist in Palo Alto, CA, who is passionate about our mission, committed to excellence and excited to build a company that will address one of the greatest health challenges of our time.**Unfolding Naive Bayes From Scratch**- Sep 25, 2018.

Whether you are a beginner in Machine Learning or you have been trying hard to understand the Super Natural Machine Learning Algorithms and you still feel that the dots do not connect somehow, this post is definitely for you!**Machine Learning Cheat Sheets**- Sep 11, 2018.

Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.**5 Things to Know About A/B Testing**- Sep 7, 2018.

This article presents 5 things to know about A/B testing, from appropriate sample sizes, to statistical confidence, to A/B testing usefulness, and more.**Essential Math for Data Science: ‘Why’ and ‘How’**- Sep 6, 2018.

It always pays to know the machinery under the hood (even at a high level) than being just the guy behind the wheel with no knowledge about the car.**What on earth is data science?**- Sep 4, 2018.

An overview and discussion around data science, covering the history behind the term, data mining, statistical inference, machine learning, data engineering and more.**Basic Statistics in Python: Probability**- Aug 21, 2018.

At the most basic level, probability seeks to answer the question, "What is the chance of an event happening?" To calculate the chance of an event happening, we also need to consider all the other events that can occur.**Interpreting a data set, beginning to end**- Aug 20, 2018.

Detailed knowledge of your data is key to understanding it! We review several important methods that to understand the data, including summary statistics with visualization, embedding methods like PCA and t-SNE, and Topological Data Analysis.**Top KDnuggets tweets, Aug 1-14: Basic Statistics in Python; Essential Command Line Tools for Data Scientists**- Aug 15, 2018.

Basic Statistics in Python: Descriptive Statistics; Top 12 Essential Command Line Tools for Data Scientists; WTF is a Tensor?!?; How GOAT Taught a Machine to Love Sneakers;**KDnuggets™ News 18:n30, Aug 8: Iconic Data Visualisation; Data Scientist Interviews Demystified; Simple Statistics in Python**- Aug 8, 2018.

Also: Selecting the Best Machine Learning Algorithm for Your Regression Problem; From Data to Viz: how to select the the right chart for your data; Only Numpy: Implementing GANs and Adam Optimizer using Numpy; Programming Best Practices for Data Science**Basic Statistics in Python: Descriptive Statistics**- Aug 1, 2018.

This article covers defining statistics, descriptive statistics, measures of central tendency, and measures of spread. This article assumes no prior knowledge of statistics, but does require at least a general knowledge of Python.**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?**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.**Explaining the 68-95-99.7 rule for a Normal Distribution**- Jul 19, 2018.

This post explains how those numbers were derived in the hope that they can be more interpretable for your future endeavors.**Why Data Scientists Love Gaussian**- Jun 26, 2018.

Gaussian distribution model, often identified with its iconic bell shaped curve, also referred as Normal distribution, is so popular mainly because of three reasons.**Every time someone runs a correlation coefficient on two time series, an angel loses their wings**- Jun 18, 2018.

We all know correlation doesn’t equal causality at this point, but when working with time series data, correlation can lead you to come to the wrong conclusion.**Statistics, Causality, and What Claims are Difficult to Swallow: Judea Pearl debates Kevin Gray**- Jun 15, 2018.

While KDnuggets takes no side, we present the informative and respectful back and forth as we believe it has value for our readers. We hope that you agree.**A Better Stats 101**- Jun 12, 2018.

Statistics encourages us to think systemically and recognize that variables normally do not operate in isolation, and that an effect usually has multiple causes. Some call this multivariate thinking. Statistics is particularly useful for uncovering the Why.**The Statistics of Gang Violence**- Jun 6, 2018.

For Carlos Carcach, Professor & Director, Center for Public Policy at the Escuela Superior de Economía y Negocios (ESEN) in Santa Tecla, El Salvador, gangs are an object of intellectual curiosity and the subject of his research.**Football World Cup 2018 Predictions: Germany vs Brazil in the final, and more**- Jun 5, 2018.

Looking ahead to the FIFA World Cup that kicks off this month (14th June), we have created the official KDnuggets predictions.**The Book of Why**- Jun 1, 2018.

Judea Pearl has made noteworthy contributions to artificial intelligence, Bayesian networks, and causal analysis. These achievements notwithstanding, Pearl holds some views many statisticians may find odd or exaggerated.**Frequentists Fight Back**- May 24, 2018.

Frequentist methods are sometimes described as “classical”, though most have only appeared in recent decades and new ones are under development as you read this. Whatever we call it, this branch of statistics is very much alive.**24houranswers: Analytics / Data Science / Math / Statistics Tutors**- May 9, 2018.

Seeking qualified Ph.D. students or faculty members for the position of Tutor/Instructor to provide one-on-one lectures to the needs of our students in Applied Analytics, Computer Science, Applied Math and Statistics, and more.**Skewness vs Kurtosis – The Robust Duo**- May 4, 2018.

Kurtosis and Skewness are very close relatives of the “data normalized statistical moment” family – Kurtosis being the fourth and Skewness the third moment, and yet they are often used to detect very different phenomena in data. At the same time, it is typically recommendable to analyse the outputs of both together to gather more insight and understand the nature of the data better.**Key Algorithms and Statistical Models for Aspiring Data Scientists**- Apr 16, 2018.

This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.**Descriptive Statistics: The Mighty Dwarf of Data Science – Crest Factor**- Apr 6, 2018.

No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.**Descriptive Statistics: The Mighty Dwarf of Data Science**- Mar 20, 2018.

No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.**Madrid Advanced Statistics and Data Mining Summer School**- Mar 19, 2018.

The courses cover topics such as Neural Networks and Deep Learning, Bayesian Networks, Big Data with Apache Spark, Bayesian Inference, Text Mining and Time Series. Each course has theoretical and practical classes, the latter done with R or Python.**Multiscale Methods and Machine Learning**- Mar 19, 2018.

We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.**A Few Statistics Tips for Marketers**- Mar 6, 2018.

Statistics can help good marketers become better marketers. Here are a few things they should know about stats.**Histogram 202: Tips and Tricks for Better Data Science**- Feb 15, 2018.

We show how to make an ideal histogram, share some tips, and give examples. Let's dive into the world of binning.**Propensity Score Matching in R**- Jan 18, 2018.

Propensity scores are an alternative method to estimate the effect of receiving treatment when random assignment of treatments to subjects is not feasible.**How Not To Lie With Statistics**- Jan 11, 2018.

Darrell Huff's classic How to Lie with Statistics is perhaps more relevant than ever. In this short article, I revisit this theme from some different angles.**Robust Algorithms for Machine Learning**- Dec 11, 2017.

This post mentions some of the advantages of implementing robust, non-parametric methods into our Machine Learning frameworks and models.**5 Tricks When A/B Testing Is Off The Table**- Dec 8, 2017.

Sometimes you cannot do A/B testing, but it does not mean we have to fly blind - there is a range of econometric methods that can illuminate the causal relationships at play.**KDnuggets™ News 17:n45, Nov 29: New Poll: Data Science Methods Used? Deep Learning Specialization: 21 Lessons Learned**- Nov 29, 2017.

Also The 10 Statistical Techniques Data Scientists Need to Master; Did Spark Really Kill Hadoop? A Framework for Textual Data Science.**You have created your first Linear Regression Model. Have you validated the assumptions?**- Nov 15, 2017.

Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.**The 10 Statistical Techniques Data Scientists Need to Master**- Nov 15, 2017.

The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.**How Bayesian Networks Are Superior in Understanding Effects of Variables**- Nov 9, 2017.

Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.**Conjoint Analysis: A Primer**- Nov 1, 2017.

Conjoint is another of those things everyone talks about but many are confused about…**Monty Hall chooses the final exit door**- Oct 7, 2017.

Monty Hall, the game show host, died last week. He was the host of the popular show "Let's Make a Deal", where contestants try to guess which one of 3 doors hides a valuable prize.**Statistical Mistakes Even Scientists Make**- Oct 3, 2017.

Scientists are all experts in statistics, right? Wrong.**30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets**- Sep 22, 2017.

This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.**How To Lie With Numbers**- Sep 21, 2017.

It takes less effort to lie without numbers, but there are now more numbers and more ways to lie with them than ever before. Poor Reverend Bayes, who understood the true meaning of "evidence".**Vital Statistics You Never Learned… Because They’re Never Taught**- Aug 29, 2017.

Marketing scientist Kevin Gray asks Professor Frank Harrell about some important things we often get wrong about statistics.**Machine Learning vs. Statistics: The Texas Death Match of Data Science**- Aug 23, 2017.

Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom’s family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of the potential for awkwardness.**Data Science Primer: Basic Concepts for Beginners**- Aug 11, 2017.

This collection of concise introductory data science tutorials cover topics including the difference between data mining and statistics, supervised vs. unsupervised learning, and the types of patterns we can mine from data.**Analytically Speaking Featuring Pedro Saraiva, July 12**- Jul 7, 2017.

Former academician and now Portugal MP Pedro Saraiva says that Parliaments and societies will improve if more people with a good statistical background become MP. Learn about the paradoxes and issues in statistics and politics.**Who Cares About Evidence?**- Jun 29, 2017.

Why bother with evidence? Because it improves the odds that what we believe is actually true. But not always.**Is Regression Analysis Really Machine Learning?**- Jun 5, 2017.

What separates "traditional" applied statistics from machine learning? Is statistics the foundation on top of which machine learning is built? Is machine learning a superset of "traditional" statistics? Do these 2 concepts have a third unifying concept in common? So, in that vein... is regression analysis actually a form of machine learning?**Descriptive Statistics Key Terms, Explained**- May 18, 2017.

This is a collection of 15 basic descriptive statistics key terms, explained in easy to understand language, along with an example and some Python code for computing simple descriptive statistics.**Propensity Scores: A Primer**- May 16, 2017.

Propensity scores are used in quasi-experimental and non-experimental research when the researcher must make causal inferences, for example, that exposure to a chemical increases the risk of cancer.**Madrid UPM Advanced Statistics and Data Mining Summer School, June 26 – July 7**- May 12, 2017.

The courses cover topics such as Neural Networks and Deep Learning, Bayesian Networks, Big Data with Apache Spark, Bayesian Inference, Text Mining and Time Series, and each has theoretical as well as practical classes, done with R or Python. Early bird till June 5.**Analytically Speaking Featuring Melisa Buie – On Demand**- Apr 6, 2017.

Learn how to keep your audience from struggling to understand your work, why others should review your experimentation process, how to build your experimental muscle, and more.**Stuff Happens: A Statistical Guide to the “Impossible”**- Apr 6, 2017.

Why are some people struck by lightning multiple times or, more encouragingly, how could anyone possibly win the lottery more than once? The odds against these sorts of things are enormous.**How to think like a data scientist to become one**- Mar 23, 2017.

The author went from securities analyst to Head of Data Science at Amazon. He describes what he learned in his journey and gives 4 useful rules based on his experience.**What Top Firms Ask: 100+ Data Science Interview Questions**- Mar 22, 2017.

Check this out: A topic wise collection of 100+ data science interview questions from top companies.**Why A/B Testers Have The Best Jobs In Tech**- Mar 22, 2017.

Learning about what these people do made it clear that when you are deeply involved in A/B testing at scale, there is a tremendous rush from doing so many different things that matter.**Analytics 101: Comparing KPIs**- Mar 20, 2017.

Different business units in the organisation have different behaviours (e.g. turnover rate) and they can’t be compared with each other. So, how can we tell whether the changes in their behaviour are reasons for concern?**17 More Must-Know Data Science Interview Questions and Answers, Part 3**- Mar 15, 2017.

The third and final part of 17 new must-know Data Science interview questions and answers covers A/B testing, data visualization, Twitter influence evaluation, and Big Data quality.

**Get more insights from fewer experiments**- Mar 3, 2017.

Efficient experimentation can save both time and money in the long term when it helps optimize product or process performance. This webcast shows how a dynamic model can dramatically improve outcomes.**Introduction to Correlation**- Feb 22, 2017.

Correlation is one of the most widely used (and widely misunderstood) statistical concepts. We provide the definitions and intuition behind several types of correlation and illustrate how to calculate correlation using the Python pandas library.**Causation or Correlation: Explaining Hill Criteria using xkcd**- Feb 20, 2017.

This is an attempt to explain Hill’s criteria using xkcd comics, both because it seemed fun, and also to motivate causal inference instructures to have some variety in which xkcd comic they include in lectures.**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.**The Top Predictive Analytics Pitfalls to Avoid**- Jan 23, 2017.

Predictive modelling and machine learning are significantly contributing to business, but they can be very sensitive to data and changes in it, which makes it very important to use proper techniques and avoid pitfalls in building data science models.**A Non-comprehensive List of Awesome Things Other People Did in 2016**- Jan 10, 2017.

A top statistics professor and statistical researcher reflects on a number of awesome accomplishments by individuals in, and related to, the fields of statistics and data science, with a focus on the world of academia but with resonance far beyond.**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.**Top KDnuggets tweets, Dec 14-20: False positives versus false negatives: Best explanation ever**- Dec 21, 2016.

Also #MachineLearning, #AI experts: Main Developments 2016, Key Trends 2017; Official code repository for #MachineLearning with #TensorFlow book; Top 10 Essential Books for the #Data Enthusiast.**Machine Learning vs Statistics**- Nov 29, 2016.

Machine learning is all about predictions, supervised learning, and unsupervised learning, while statistics is about sample, population, and hypotheses. But are they actually that different?**How Bayesian Inference Works**- Nov 15, 2016.

Bayesian inference isn’t magic or mystical; the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Read an in-depth overview here.**Trump, The Statistics of Polling, and Forecasting Home Prices**- Nov 12, 2016.

Why polling has failed in US Presidential election? The home price index offers an apt comparison inasmuch as sample selection is problematic, equally snagging both election predictions and home price futures.**How Can Lean Six Sigma Help Machine Learning?**- Nov 1, 2016.

The data cleansing phase alone is not sufficient to ensure the accuracy of the machine learning, when noise / bias exists in input data. The lean six sigma variance reduction can improve the accuracy of machine learning results.**Data Science Basics: Data Mining vs. Statistics**- Sep 28, 2016.

As a beginner I was confused at the relationship between data mining and statistics. This is my attempt to help straighten out this connection for others who may now be in my old shoes.**The Great Algorithm Tutorial Roundup**- Sep 20, 2016.

This is a collection of tutorials relating to the results of the recent KDnuggets algorithms poll. If you are interested in learning or brushing up on the most used algorithms, as per our readers, look here for suggestions on doing so!**How top companies use data to make wise decisions**- Sep 15, 2016.

Lucky people are those who pay attention to patterns and maintain a healthy curiosity. Download JMP Foreword magazine and read about those people.**8451: Statistical Model Research Scientist**- Sep 9, 2016.

Seeking a Research Scientist who will employ skills and experience to improve, create and innovate data-driven modeling approaches for our price and promotion solutions, while anticipating and charting future research needs.**A Tutorial on the Expectation Maximization (EM) Algorithm**- Aug 25, 2016.

This is a short tutorial on the Expectation Maximization algorithm and how it can be used on estimating parameters for multi-variate data.**Central Limit Theorem for Data Science – Part 2**- Aug 16, 2016.

This post continues an explanation of Central Limit Theorem started in a previous post, with additional details... and beer.**Central Limit Theorem for Data Science**- Aug 12, 2016.

This post is an introductory explanation of the Central Limit Theorem, and why it is (or should be) of importance to data scientists.**Understanding the Empirical Law of Large Numbers and the Gambler’s Fallacy**- Aug 12, 2016.

Law of large numbers is a important concept for practising data scientists. In this post, The empirical law of large numbers is demonstrated via simple simulation approach using the Bernoulli process.**Understand customer needs with choice modeling**- Aug 3, 2016.

Which product features are most important to your customers? This case study of American vs Belgian chocolate choice analysis can help you understand which factors drive your customer.**KDnuggets™ News 16:n28, Aug 3: Data Science Stats 101; Understanding NoSQL Databases; Core of Data Science**- Aug 3, 2016.

Data Science Statistics 101; 7 Steps to Understanding NoSQL Databases; The Core of Data Science; Data Science for Beginners 2: Is your data ready?**What Statistics Topics are Needed for Excelling at Data Science?**- Aug 2, 2016.

Here is a list of skills and statistical concepts suggested for excelling at data science, roughly in order of increasing complexity.**Doing Statistics with SQL**- Aug 2, 2016.

This post covers how to perform some basic in-database statistical analysis using SQL.**Data Science Statistics 101**- Jul 28, 2016.

Statistics can often be the most intimidating aspect of data science for aspiring data scientists to learn. Gain some personal perspective from someone who has traveled the path.**Why Big Data is in Trouble: They Forgot About Applied Statistics**- Jul 18, 2016.

This "classic" (but very topical and certainly relevant) post discusses issues that Big Data can face when it forgets, or ignores, applied statistics. As great of a discussion today as it was 2 years ago.**Big Data, Bible Codes, and Bonferroni**- Jul 8, 2016.

This discussion will focus on 2 particular statistical issues to be on the look out for in your own work and in the work of others mining and learning from Big Data, with real world examples emphasizing the importance of statistical processes in practice.**How to Compare Apples and Oranges ? : Part III**- Jul 6, 2016.

In the previous article, look at techniques to compare categorical variables with the help of an example. In this article, we shall look at techniques to compare mixed type of variables i.e. numerical and categorical variables together.**Civis Analytics: Data Scientist, Statistics**- Jun 28, 2016.

Seeking a Data Scientist to work closely and collaboratively with analysts and engineers to develop and operationalize the techniques that quantify and solve big, meaningful problems.**How to Compare Apples and Oranges, Part 2 – Categorical Variables**- Jun 21, 2016.

In the previous article, we looked at some of the ways to compare different numerical variables. In this article, we shall look at techniques to compare categorical variables with the help of an example.**How to Compare Apples and Oranges – Part 1**- Jun 17, 2016.

We are always told that apples and oranges can’t be compared, they are completely different things. Learn as an analyst, how you deal with such difference and make sense of it on a daily basis.**Machine Learning Classic: Parsimonious Binary Classification Trees**- Jun 14, 2016.

Get your hands on a classic technical report outlining a three-step method of construction binary decision trees for multiple classification problems.**Webcast: Learn how statisticians can work across disciplines.**- Jun 13, 2016.

Learn why subject-matter experts are better off when they understand their data; how traditional statistics has missed an opportunity; why it takes a long time for some methods to gain popularity and more.**Data Science of Variable Selection: A Review**- Jun 7, 2016.

There are as many approaches to selecting features as there are statisticians since every statistician and their sibling has a POV or a paper on the subject. This is an overview of some of these approaches.**Eugenics – journey to the dark side at the dawn of statistics**- Apr 27, 2016.

Today is the 80th anniversary of the death of Karl Pearson, one of the founding father of statistics (correlation coefficient, principal components, the p-value, and much more). He was also deeply involved with eugenics, a jarring reminder that truth often comes bundled with a measure of darkness.**Civis Analytics: Data Scientist, Statistics**- Apr 1, 2016.

Be part of the Research and Development team, responsible for developing the fundamental data science methods, techniques, and best practices that power the mission of our company, performing predictive analytics, algorithm development, experimental design, visualization, and survey research.**Top KDnuggets tweets, Mar 16-21: After 150 Years, ASA Says “NO” to p-values; Free Resources to Learn #MachineLearning**- Mar 22, 2016.

After 150 Years, ASA Says "NO" to p-values; Using Deep Q-Network to Learn Play Flappy Bird; Why we work so hard: The problems is overworked professionals are NOT miserable; Free Resources to Learn #MachineLearning.**The Evolution of the Data Scientist**- Mar 16, 2016.

We trace the evolution of Data Science from ancient mathematics to statistics and early neural networks, to present successes like AlphaGo and self-driving car, and look into the future.**After 150 Years, the ASA Says No to p-values**- Mar 15, 2016.

The ASA has recently taken a position against p-values. Read the overview and opinion of a well-respected statistician to gain additional insight.**When Good Advice Goes Bad**- Mar 14, 2016.

Consider these 4 examples of good statistical advice which, when misused, can go bad.**Bayes Theorem for Computer Scientists, Explained**- Feb 16, 2016.

Data science is vain without the solid understanding of probability and statistics. Learn the basic concepts of probability, including law of total probability, relevant theorem and Bayes’ theorem, along with their computer science applications.**Online Courses, from basic statistics to Big Data and Analytics, from Statistics.com**- Feb 15, 2016.

Statistics.com offers a rich array of online courses to accelerate your data science career or help upgrade the skills of your Big Data team. Small classes, not MOOCs, taught by top instructors - people who write the textbooks and have real industry experience.**Top 10 TED Talks for the Data Scientists**- Feb 9, 2016.

TEDTalks have been a great platform for sharing ideas and inspirations. Here, we have sifted ten interesting talks for the data scientist from statistics, social media and economics domains.**Plausibility vs. probability, prior distributions, and the garden of forking paths**- Jan 14, 2016.

A discussion on plausibility vs. probability: while many given events may be plausible, but they can’t all be probable.**A Non-comprehensive List of Awesome Things other People Did in 2015**- Jan 9, 2016.

A top statistics professor and statistical researcher reflects on a number of awesome accomplishments by individuals in, and related to, the fields of statistics and data science, with a focus on the world of academia but with resonance far beyond.**Predictive Power of Terror Alerts and Monkeys**- Jan 4, 2016.

The terrorism threat advisory system was designed to give the public prior warning to when terrorist plots are about to unfold. However, the analysis shows that this system is not more helpful than monkey throwing a dart.**Amazon Top 20 Books in Statistics**- Nov 9, 2015.

These are the most popular statistics books on Amazon. Some interesting books made their way onto this list, and hopefully you find something of interest here.**Data-Planet Statistical Datasets**- Nov 4, 2015.

Data-Planet Statistical Datasets provides easy access to an extensive repository of standardized and structured statistical data, with more than 25 billion data points from more than 70 source organizations.**We need a statistically rigorous and scientifically meaningful definition of replication**- Oct 29, 2015.

Replication and confirmation are indispensable concepts that help define scientific facts. It seems that before continuing the debate over replication, we need a statistically meaningful definition of replication.**OpenText Data Digest Oct 23: World Statistics Day**- Oct 26, 2015.

This week we look at 3 top entries in the UN competition based around eight Millennium Development Goals to see how well they stack up against the data visualizations you are working on.**Lets talk about Ethics in Analytics / Data Science**- Oct 20, 2015.

Is it time that data scientists go through formal ethics training? The saying “Lies, damned lies, and statistics” suggests that statistics (and Data Science) can be tweaked to prove any point and ethics training will help to improve the integrity and credibility of analytics profession.**ASA says Statistics is Foundational to Data Science**- Oct 9, 2015.

ASA cites statistics as one of three foundational communities in data science and emphasizes the importance of collaboration among all field’s key disciplines.**U. of San Francisco: Tenure Track Professor, Applied Mathematics, Statistics, Data Science**- Sep 24, 2015.

We seek well-qualified candidates in applied mathematics or statistics, with a focus on the extraction of knowledge from data, including data science, modeling, applied probability, applied statistics, and machine learning.**Most popular “Statistical Analysis and Data Mining” Papers**- Sep 9, 2015.

Most popular recent papers from “Statistical Analysis and Data Mining” journal cover They outlier detection, clustering, large-scale analytics, link prediction, mining sequential patterns, and more. They will be free to read for a limited time.**Commonly Misunderstood Analytics Terms**- Sep 3, 2015.

Unable to follow what your analyst language during presentations? Understand what exactly the common terminologies in the data science mean.**How to become a Data Scientist for Free**- Aug 28, 2015.

Here are the most required skills for a data scientist position based on ReSkill’s analyses of thousands of job posts and free resources to learn each skill.**Understanding Basic Concepts and Dispersion**- Aug 10, 2015.

In analytics it is a common practice to understand the basic statistical properties of its variables viz. range, mean and deviation. Centrality measures are the most important to them, explore how to use these measures.**Statistics – Understanding the Levels of Measurement**- Aug 6, 2015.

For doing statistics or analytics it is first step to understand the variables. Moreover, it is important that one truly knows which measure to take with different available types.**CSU East Bay: Faculty, Statistics/Biostatistics, Full-Time Tenure-Track**- Aug 4, 2015.

Teach undergraduate/MS courses in Statistics/Analytics/Data Science/Statistical Learning/Big Data, sustain related research program. Teach, advise, and mentor ethnically diverse students.**KDnuggets™ News 15:n23, Jul 22: Deep Learning Adversarial Examples Myths & Facts; Stop Hiring Data Scientists Until …**- Jul 22, 2015.

Deep Learning Adversarial Examples - facts and myths; Stop Hiring Data Scientists Until; arXiv.org and the 24 Hour Research Cycle; Statistics Denial Myth and Repackaging Statistics.**Statistics Denial Myth: Repackaging Statistics With Straddling Terms**- Jul 16, 2015.

Data science is nothing but the old wine in new bottle versions of the statistics with different fields. Here, we are busting the myth which states data scientist is new and different than traditional statisticians.**Deep Learning and the Triumph of Empiricism**- Jul 7, 2015.

Theoretical guarantees are clearly desirable. And yet many of today's best-performing supervised learning algorithms offer none. What explains the gap between theoretical soundness and empirical success?**Civis Analytics: Data Scientist – Statistics**- Jun 25, 2015.

Apply state-of-the-art statistical methods to our research and develop entirely new methods to use our data in new ways; be a critical voice on determining which statistical approaches are appropriate for which problems.**Applied Statistics Is A Way Of Thinking, Not Just A Toolbox**- May 29, 2015.

The choice of tools in applied statistics is driven by the objective, the structure of the data, and the nature of the uncertainty in the numbers, whereas in academic statistics its driven by publishing or teaching. Here we provide some of common statistical tools and the overlapping genealogy.**Essays On Statistics Denial**- May 20, 2015.

Statistics denial comes in waves as areas of application discover and rediscover the potential of data insights. We examine the statistics denial myths and where they come from.