 algorithm

A series of repeatable steps for carrying out a certain type of task with data. As with data structures, people studying computer science learn about different algorithms and their suitability for various tasks. Specific data structures often play a role in how certain algorithms get implemented. See also data structure
 AngularJS

An opensource JavaScript library maintained by Google and the AngularJS community that lets developers create what are known as Single [web] Page Applications. AngularJS is popular with data scientists as a way to show the results of their analysis. See also JavaScript, D3
 artificial intelligence

Also, AI. The ability to have machines act with apparent intelligence, although varying definitions of “intelligence” lead to a range of meanings for the artificial variety. In AI’s early days in the 1960s, researchers sought general principles of intelligence to implement, often using symbolic logic to automate reasoning. As the cost of computing resources dropped, the focus moved more toward statistical analysis of large amounts of data to drive decision making that gives the appearance of intelligence. See also machine learning, data mining
 backpropagation

Also, backprop. An algorithm for iteratively adjusting the weights used in a neural network system. Backpropagation is often used to implement gradient descent. See also neural network, gradient descent
 Bayes' Theorem

Also, Bayes' Rule. An equation for calculating the probability that something is true if something potentially related to it is true. If P(A) means “the probability that A is true” and P(AB) means “the probability that A is true if B is true,” then Bayes' Theorem tells us that P(AB) = (P(BA)P(A)) / P(B). This is useful for working with false positives—for example, if x% of people have a disease, the test for it is correct y% of the time, and you test positive, Bayes' Theorem helps calculate the odds that you actually have the disease. The theorem also makes it easier to update a probability based on new data, which makes it valuable in the many applications where data continues to accumulate. Named for eighteenthcentury English statistician and Presbyterian minister Thomas Bayes. See also Bayesian network, prior distribution
 Bayesian network

Also, Bayes net. “Bayesian networks are graphs that compactly represent the relationship between random variables for a given problem. These graphs aid in performing reasoning or decision making in the face of uncertainty. Such reasoning relies heavily on Bayes’ rule.”^{[bourg]} These networks are usually represented as graphs in which the link between any two nodes is assigned a value representing the probabilistic relationship between those nodes. See also Bayes' Theorem, Markov Chain
 bias

In machine learning, “bias is a learner’s tendency to consistently learn the same wrong thing. Variance is the tendency to learn random things irrespective of the real signal.... It’s easy to avoid overfitting (variance) by falling into the opposite error of underfitting (bias). Simultaneously avoiding both requires learning a perfect classifier, and short of knowing it in advance there is no single technique that will always do best (no free lunch).”^{[domingos]} See also variance, overfitting, classification
 Big Data

As this has become a popular marketing buzz phrase, definitions have proliferated, but in general, it refers to the ability to work with collections of data that had been impractical before because of their volume, velocity, and variety (“the three Vs”). A key driver of this new ability has been easier distribution of storage and processing across networks of inexpensive commodity hardware using technology such as Hadoop instead of requiring larger, more powerful individual computers. The work done with these large amounts of data often draws on data science skills.
 binomial distribution

A distribution of outcomes of independent events with two mutually exclusive possible outcomes, a fixed number of trials, and a constant probability of success. This is a discrete probability distribution, as opposed to continuous—for example, instead of graphing it with a line, you would use a histogram, because the potential outcomes are a discrete set of values. As the number of trials represented by a binomial distribution goes up, if the probability of success remains constant, the histogram bars will get thinner, and it will look more and more like a graph of normal distribution. See also probability distribution, discrete variable, histogram, normal distribution
 chisquare test

Chi (pronounced like “pie” but beginning with a “k”) is a Greek letter, and chisquare is “a statistical method used to test whether the classification of data can be ascribed to chance or to some underlying law.”^{[websters]} The chisquare test “is an analysis technique used to estimate whether two variables in a cross tabulation are correlated.”^{[shin]} A chisquare distribution varies from normal distribution based on the “degrees of freedom” used to calculate it. See also normal distribution and Wikipedia on the chisquared test and on chisquared distribution.
 classification

The identification of which of two or more categories an item falls under; a classic machine learning task. Deciding whether an email message is spam or not classifies it among two categories, and analysis of data about movies might lead to classification of them among several genres. See also supervised learning, clustering
 clustering

Any unsupervised algorithm for dividing up data instances into groups—not a predetermined set of groups, which would make this classification, but groups identified by the execution of the algorithm because of similarities that it found among the instances. The center of each cluster is known by the excellent name “centroid.” See also classification, supervised learning, unsupervised learning, kmeans clustering
 coefficient

“A number or algebraic symbol prefixed as a multiplier to a variable or unknown quantity (Ex.: x in x(y + z), 6 in 6ab”^{[websters]} When graphing an equation such as y = 3x + 4, the coefficient of x determines the line's slope. Discussions of statistics often mention specific coefficients for specific tasks such as the correlation coefficient, Cramer’s coefficient, and the Gini coefficient. See also correlation
 computational linguistics

Also, natural language processing, NLP. A branch of computer science for parsing text of spoken languages (for example, English or Mandarin) to convert it to structured data that you can use to drive program logic. Early efforts focused on translating one language to another or accepting complete sentences as queries to databases; modern efforts often analyze documents and other data (for example, tweets) to extract potentially valuable information. See also GATE, UIMA
 confidence interval

A range specified around an estimate to indicate margin of error, combined with a probability that a value will fall in that range. The field of statistics offers specific mathematical formulas to calculate confidence intervals.
 continuous variable

A variable whose value can be any of an infinite number of values, typically within a particular range. For example, if you can express age or size with a decimal number, then they are continuous variables. In a graph, the value of a continuous variable is usually expressed as a line plotted by a function. Compare discrete variable
 correlation

“The degree of relative correspondence, as between two sets of data.”^{[websters]} If sales go up when the advertising budget goes up, they correlate. The correlation coefficient is a measure of how closely the two data sets correlate. A correlation coefficient of 1 is a perfect correlation, .9 is a strong correlation, and .2 is a weak correlation. This value can also be negative, as when the incidence of a disease goes down when vaccinations go up. A correlation coefficient of 1 is a perfect negative correlation. Always remember, though, that correlation does not imply causation. See also coefficient
 covariance

“A measure of the relationship between two variables whose values are observed at the same time; specifically, the average value of the two variables diminished by the product of their average values.”^{[websters]} “Whereas variance measures how a single variable deviates from its mean, covariance measures how two variables vary in tandem from their means.”^{[grus]} See also variance, mean
 crossvalidation

When using data with an algorithm, “the name given to a set of techniques that divide up data into training sets and test sets. The training set is given to the algorithm, along with the correct answers... and becomes the set used to make predictions. The algorithm is then asked to make predictions for each item in the test set. The answers it gives are compared to the correct answers, and an overall score for how well the algorithm did is calculated.”^{[segaran]} See also machine learning
 D3

“DataDriven Documents.” A JavaScript library that eases the creation of interactive visualizations embedded in web pages. D3 is popular with data scientists as a way to present the results of their analysis. See also AngularJS, JavaScript
 data engineer

A specialist in data wrangling. “Data engineers are the ones that take the messy data... and build the infrastructure for real, tangible analysis. They run ETL software, marry data sets, enrich and clean all that data that companies have been storing for years.”^{[biewald]} See also data wrangling. (A Wikipedia search for “data engineering” redirects to “information engineering,” an older term that describes a more enterpriseoriented job with greater system architecture responsibility and less handson work with the data.)
 data mining

Generally, the use of computers to analyze large data sets to look for patterns that let people make business decisions. While this sounds like much of what data science is about, popular use of the term is much older, dating back at least to the 1990s. See also data science
 data science

“The ability to extract knowledge and insights from large and complex data sets.”^{[patil]} Data science work often requires knowledge of both statistics and software engineering. See also data engineer, machine learning
 data structure

A particular arrangement of units of data such as an array or a tree. People studying computer science learn about different data structures and their suitability for various tasks. See also algorithm
 data wrangling

Also, data munging. The conversion of data, often through the use of scripting languages, to make it easier to work with. If you have 900,000 birthYear values of the format yyyymmdd and 100,000 of the format mm/dd/yyyy and you write a Perl script to convert the latter to look like the former so that you can use them all together, you're doing data wrangling. Discussions of data science often bemoan the high percentage of time that practitioners must spend doing data wrangling; the discussions then recommend the hiring of data engineers to address this. See also Perl, Python, shell, data engineer
 decision trees

“A decision tree uses a tree structure to represent a number of possible decision paths and an outcome for each path. If you have ever played the game Twenty Questions, then it turns out you are familiar with decision trees.”^{[grus]} See also random forest
 deep learning

Typically, a multilevel algorithm that gradually identifies things at higher levels of abstraction. For example, the first level may identify certain lines, then the next level identifies combinations of lines as shapes, and then the next level identifies combinations of shapes as specific objects. As you might guess from this example, deep learning is popular for image classification. See also neural network
 dependent variable

The value of a dependent value “depends” on the value of the independent variable. If you're measuring the effect of different sizes of an advertising budget on total sales, then the advertising budget figure is the independent variable and total sales is the dependent variable.
 dimension reduction

Also, dimensionality reduction. “We can use a technique called principal component analysis to extract one or more dimensions that capture as much of the variation in the data as possible... Dimensionality reduction is mostly useful when your data set has a large number of dimensions and you want to find a small subset that captures most of the variation.”^{[grus]} Linear algebra can be involved; “broadly speaking, linear algebra is about translating something residing in an mdimensional space into a corresponding shape in an ndimensional space.”^{[shin]} See also linear algebra
 discrete variable

A variable whose potential values must be one of a specific number of values. If someone rates a movie with between one and five stars, with no partial stars allowed, the rating is a discrete variable. In a graph, the distribution of values for a discrete variable is usually expressed as a histogram. See also continuous variable, histogram
 econometrics

“The use of mathematical and statistical methods in the field of economics to verify and develop economic theories”^{[websters]}
 feature

The machine learning expression for a piece of measurable information about something. If you store the age, annual income, and weight of a set of people, you're storing three features about them. In other areas of the IT world, people may use the the terms property, attribute, or field instead of “feature.” See also feature engineering
 feature engineering

“To obtain a good model, however, often requires more effort and iteration and a process called feature engineering. Features are the model’s inputs. They can involve basic raw data that you have collected, such as order amount, simple derived variables, such as ‘Is order date on a weekend? Yes/No,’ as well as more complex abstract features, such as the ‘similarity score’ between two movies. Thinking up features is as much an art as a science and can rely on domain knowledge.”^{[anderson]} See also feature
 GATE

“General Architecture for Text Engineering,” an open source, Javabased framework for natural language processing tasks. The framework lets you pipeline other tools designed to be plugged into it. The project is based at the UK’s University of Sheffield. See also computational linguistics, UIMA
 gradient boosting

“Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stagewise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function.”^{[wikipediagb]}
 gradient descent

An optimization algorithm for finding the input to a function “that produces the largest (or smallest) possible value... one approach to maximizing a function is to pick a random starting point, compute the gradient, take a small step in the direction of the gradient (i.e., the direction that causes the function to increase the most), and repeat with the new starting point. Similarly, you can try to minimize a function by taking small steps in the opposite direction.”^{[grus]} See also backpropagation
 histogram

A graphical representation of the distribution of a set of numeric data, usually a vertical bar graph. See also probability distribution, binomial distribution, discrete variable
 independent variable
 JavaScript

A scripting language (no relation to Java) originally designed in the mid1990s for embedding logic in web pages, but which later evolved into a more generalpurpose development language. JavaScript continues to be very popular for embedding logic in web pages, with many libraries available to enhance the operation and visual presentation of these pages. See also AngularJS, D3
 kmeans clustering

“A data mining algorithm to cluster, classify, or group your N objects based on their attributes or features into K number of groups (socalled clusters).”^{[parsian]} See also clustering
 knearest neighbors

Also, kNN. A machine learning algorithm that classifies things based on their similarity to nearby neighbors. You tune the algorithm’s execution by picking how many neighbors to examine (k) as well as some notion of “distance” to indicate how near the neighbors are. For example, in a social network, a friend of your friend could be considered twice the distance away from you as your friend. “Similarity” would be comparison of feature values in the neighbors being compared. See also classification, feature
 latent variable

“In statistics, latent variables (from Latin: present participle of lateo ('lie hidden'),as opposed to observable variables), are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured). Mathematical models that aim to explain observed variables in terms of latent variables are called latent variable models.”^{[wikipedialv]}
 lift

“Lift compares the frequency of an observed pattern with how often you’d expect to see that pattern just by chance... If the lift is near 1, then there’s a good chance that the pattern you observed is occurring just by chance. The larger the lift, the more likely that the pattern is ‘real.’”^{[zumel]}
 linear algebra

A branch of mathematics dealing with vector spaces and operations on them such as addition and multiplication. “Linear algebra is designed to represent systems of linear equations. Linear equations are designed to represent linear relationships, where one entity is written to be a sum of multiples of other entities. In the shorthand of linear algebra, a linear relationship is represented as a linear operator—a matrix.”^{[zheng]} See also vector, vector space, matrix, coefficient
 linear regression

A technique to look for a linear relationship (that is, one where the relationship between two varying amounts, such as price and sales, can be expressed with an equation that you can represent as a straight line on a graph) by starting with a set of data points that don't necessarily line up nicely. This is done by computing the “least squares” line: the one that has, on an xy graph, the smallest possible sum of squared distances to the actual data point y values. Statistical software packages and even typical spreadsheet packages offer automated ways to calculate this. People who get excited about machine learning often apply it to problems that would have been much simpler by using linear regression in an Excel spreadsheet. See also regression, logistic regression, machine learning
 logarithm

If y = 10^{x}, then log(y) = x. Working with the log of one or more of a model's variables, instead of their original values, can make it easier to model relationships with linear functions instead of nonlinear ones. Linear functions are typically easier to use in data analysis. (The log(y) = x example shown is for log base 10. Natural logarithms, or log base e—where e is a specific irrational number a little over 2.7—are a bit more complicated but also very useful for related tasks.) See also dependent variable, linear regression
 logistic regression

A model similar to linear regression but where the potential results are a specific set of categories instead of being continuous. See continuous variable, regression, linear regression
 machine learning

The use of datadriven algorithms that perform better as they have more data to work with, “learning” (that is, refining their models) from this additional data. This often involves crossvalidation with training and test data sets. “The fundamental goal of machine learning is to generalize beyond the examples in the training set.”^{[domingos]} Studying the practical application of machine learning usually means researching which machine learning algorithms are best for which situations. See also algorithm, crossvalidation, artificial intelligence
 Markov Chain

An algorithm for working with a series of events (for example, a system being in particular states) to predict the possibility of a certain event based on which other events have happened. The identification of probabilistic relationships between the different events means that Markov Chains and Bayesian networks often come up in the same discussions. See also Bayesian network, Monte Carlo method
 MATLAB

A commercial computer language and environment popular for visualization and algorithm development.
 matrix

(Plural: matrices) An older Webster’s dictionary with a heavier emphasis on typographical representation gives the mathematical definition as “a set of numbers or terms arranged in rows and columns between parentheses or double lines”^{[websters]}. For purposes of manipulating a matrix with software, think of it as a twodimensional array. As with its onedimensional equivalent, a vector, this mathematical representation of the twodimensional array makes it easier to take advantage of software libraries that apply advanced mathematical operations to the data—including libraries that can distribute the processing across multiple processors for scalability. See also vector, linear algebra
 mean

The average value, although technically that is known as the “arithmetic mean.” (Other means include the geometric and harmonic means.) See also median, mode
 Mean Absolute Error

Also, MAE. The average error of all predicted values when compared with observed values. See also Mean Squared Error, Root Mean Squared Error
 Mean Squared Error

Also, MSE. The average of the squares of all the errors found when comparing predicted values with observed values. Squaring them makes the bigger errors count for more, making Mean Squared Error more popular than Mean Absolute Error when quantifying the success of a set of predictions. See also Mean Absolute Error, Root Mean Squared Error
 median

When values are sorted, the value in the middle, or the average of the two in the middle if there are an even number of values. See also mean, mode
 mode

“The value that occurs most often in a sample of data. Like the median, the mode cannot be directly calculated”^{[stanton]} although it’s easy enough to find with a little scripting. For people who work with statistics, “mode” can also mean “data type”—for example, whether a value is an integer, a real number, or a date. See also mean, median, scripting
 model

“A specification of a mathematical (or probabilistic) relationship that exists between different variables.”^{[grus]} Because “modeling” can mean so many things, the term “statistical modeling” is often used to more accurately describe the kind of modeling that data scientists do.
 Monte Carlo method

In general, the use of randomly generated numbers as part of an algorithm. Its use with Markov Chains is so popular that people usually refer to the combination with the acronym MCMC. See also Markov Chain
 moving average

“The mean (or average) of time series data (observations equally spaced in time, such as per hour or per day) from several consecutive periods is called the moving average. It is called moving because the average is continually recomputed as new time series data becomes available, and it progresses by dropping the earliest value and adding the most recent.”^{[parsian]} See also mean, time series data
 ngram

The analysis of sequences of n items (typically, words in natural language) to look for patterns. For example, trigram analysis examines threeword phrases in the input to look for patterns such as which pairs of words appear most often in the groups of three. The value of n can be something other than three, depending on your needs. This helps to construct statistical models of documents (for example, when automatically classifying them) and to find positive or negative terms associated with a product name. See also computational linguistics, classification
 naive Bayes classifier

“A collection of classification algorithms based on Bayes Theorem. It is not a single algorithm but a family of algorithms that all share a common principle, that every feature being classified is independent of the value of any other feature. So for example, a fruit may be considered to be an apple if it is red, round, and about 3” in diameter. A Naive Bayes classifier considers each of these ‘features’ (red, round, 3” in diameter) to contribute independently to the probability that the fruit is an apple, regardless of any correlations between features. Features, however, aren’t always independent which is often seen as a shortcoming of the Naive Bayes algorithm and this is why it’s labeled ‘naive’.”^{[aylien]} This naiveté makes it much easier to develop implementations of these algorithms that scale way up. See also Bayes' Theorem, classification
 neural network

Also, neural net or artificial neural network to distinguish it from the brain, upon which this algorithm is modeled. “A robust function that takes an arbitrary set of inputs and fits it to an arbitrary set of outputs that are binary... In practice, Neural Networks are used in deep learning research to match images to features and much more. What makes Neural Networks special is their use of a hidden layer of weighted functions called neurons, with which you can effectively build a network that maps a lot of other functions. Without a hidden layer of functions, Neural Networks would be just a set of simple weighted functions.”^{[kirk]} See also deep learning, backpropagation, perceptron
 normal distribution

Also, Gaussian distribution. (Carl Friedrich Gauss was an early nineteenthcentury German mathematician.) A probability distribution which, when graphed, is a symmetrical bell curve with the mean value at the center. The standard deviation value affects the height and width of the graph. See also mean, probability distribution, standard deviation, binomial distribution, standard normal distribution
 NoSQL

A database management system that uses any of several alternatives to the relational, tableoriented model used by SQL databases. While this term originally meant “not SQL,” it has come to mean something closer to “not only SQL” because the specialized nature of NoSQL database management systems often have them playing specific roles in a larger system that may also include SQL and additional NoSQL systems. See also SQL
 null hypothesis

If your proposed model for a data set says that the value of x is affecting the value of y, then the null hypothesis—the model you're comparing your proposed model with to check whether x really is affecting y—says that the observations are all based on chance and that there is no effect. “The smaller the Pvalue computed from the sample data, the stronger the evidence is against the null hypothesis.”^{[shin]} See also P value
 objective function

“When you want to get as much (or as little) of something as possible, and the way you’ll get it is by changing the values of other quantities, you have an optimization problem...To solve an optimization problem, you need to combine your decision variables, constraints, and the thing you want to maximize together into an objective function. The objective is the thing you want to maximize or minimize, and you use the objective function to find the optimum result.”^{[milton]} See also gradient descent
 outlier

“Extreme values that might be errors in measurement and recording, or might be accurate reports of rare events.”^{[downey]} See also overfitting
 overfitting

A model of training data that, by taking too many of the data's quirks and outliers into account, is overly complicated and will not be as useful as it could be to find patterns in test data. See also outlier, crossvalidation
 P value

Also, pvalue. “The probability, under the assumption of no effect or no difference (the null hypothesis), of obtaining a result equal to or more extreme than what was actually observed.”^{[goodman]} “It’s a measure of how surprised you should be if there is no actual difference between the groups, but you got data suggesting there is. A bigger difference, or one backed up by more data, suggests more surprise and a smaller p value...The p value is a measure of surprise, not a measure of the size of the effect.”^{[reinhart]} A lower p value means that your results are more statistically significant. See also null hypothesis
 PageRank

An algorithm that determines the importance of something, typically to rank it in a list of search results. “PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites.”^{[googlearchive]} PageRank is not named for the pages that it ranks but for its inventor, Google cofounder and CEO Larry Page.
 Pandas

A Python library for data manipulation popular with data scientists. See also Python
 perceptron

“Pretty much the simplest neural network is the perceptron, which approximates a single neuron with n binary inputs. It computes a weighted sum of its inputs and ‘fires’ if that weighted sum is zero or greater.”^{[grus]} See also neural network
 Perl

An older scripting language with roots in preLinux UNIX systems. Perl has always been popular for text processing, especially data cleanup and enhancement tasks. See also scripting, data wrangling
 pivot table

“Pivot tables quickly summarize long lists of data, without requiring you to write a single formula or copy a single cell. But the most notable feature of pivot tables is that you can arrange them dynamically. Say you create a pivot table summary using raw census data. With the drag of a mouse, you can easily rearrange the pivot table so that it summarizes the data based on gender or age groupings or geographic location. The process of rearranging your table is known as pivoting your data: you're turning the same information around to examine it from different angles. ^{[macdonald]}
 Poisson distribution

A distribution of independent events, usually over a period of time or space, used to help predict the probability of an event. Like the binomial distribution, this is a discrete distribution. Named for early 19th century French mathematician Siméon Denis Poisson. See also spatiotemporal data, discrete variable, binomial distribution
 posterior distribution
 predictive analytics

The analysis of data to predict future events, typically to aid in business planning. This incorporates predictive modeling and other techniques. Machine learning might be considered a set of algorithms to help implement predictive analytics. The more businessoriented spin of “predictive analytics” makes it a popular buzz phrase in marketing literature. See also predictive modeling, machine learning, SPSS
 predictive modeling

The development of statistical models to predict future events. See also predictive analytics, model
 principal component analysis

“This algorithm simply looks at the direction with the most variance and then determines that as the first principal component. This is very similar to how regression works in that it determines the best direction to map data to.”^{[kirk]} See also regression
 prior distribution

“In Bayesian inference, we assume that the unknown quantity to be estimated has many plausible values modeled by what's called a prior distribution. Bayesian inference is then using data (that is considered as unchanging) to build a tighter posterior distribution for the unknown quantity.”^{[zumel]} See also Bayes' Theorem
 probability distribution

“A probability distribution for a discrete random variable is a listing of all possible distinct outcomes and their probabilities of occurring. Because all possible outcomes are listed, the sum of the probabilities must add to 1.0.”^{[levine]} See also discrete variable
 Python

A programming language available since 1994 that is popular with people doing data science. Python is noted for ease of use among beginners and great power when used by advanced users, especially when taking advantage of specialized libraries such as those designed for machine learning and graph generation. See also scripting, Pandas
 quantile, quartile

When you divide a set of sorted values into groups that each have the same number of values (for example, if you divide the values into two groups at the median), each group is known as a quantile. If there are four groups, we call them quartiles, which is a common way to divide values for discussion and analysis purposes; if there are five, we call them quintiles, and so forth. See also median
 R

An opensource programming language and environment for statistical computing and graph generation available for Linux, Windows, and Mac.
 random forest

An algorithm used for regression or classification that uses a collection of tree data structures. “To classify a new object from an input vector, put the input vector down each of the trees in the forest. Each tree gives a classification, and we say the tree ‘votes’ for that class. The forest chooses the classification having the most votes (over all the trees in the forest).”^{[breiman]} The term “random forest” is actually trademarked by its authors. See also classification, vector, decision trees
 regression

“...the more general problem of fitting any kind of model to any kind of data. This use of the term 'regression' is a historical accident; it is only indirectly related to the original meaning of the word.”^{[downey]} See also linear regression, logistic regression, principal component analysis
 reinforcement learning

A class of machine learning algorithms in which the process is not given specific goals to meet but, as it makes decisions, is instead given indications of whether it’s doing well or not. For example, an algorithm for learning to play a video game knows that if its score just went up, it must have done something right. See also supervised learning, unsupervised learning
 Root Mean Squared Error

Also, RMSE. The square root of the Mean Squared Error. This is more popular than Mean Squared Error because taking the square root of a figure built from the squares of the observation value errors gives a number that’s easier to understand in the units used to measure the original observations. See also Mean Absolute Error, Mean Squared Error,
 Ruby

A scripting language that first appeared in 1996. Ruby is popular in the data science community, but not as popular as Python, which has more specialized libraries available for data science tasks. See also scripting, Python
 S curve

Imagine a graph showing, for each month since smartphones originally became available, how many people in the US bought their first one. The line would rise slowly at first, when only the early adopters got them, then quickly as these phones became more popular, and then level off again once nearly everyone had one. This graph's line would form a stretchedout “S” shape. The “S curve” applies to many other phenomena and is often mentioned when someone predicts that a rising value will eventually level off.
 SAS

A commercial statistical software suite that includes a programming language also known as SAS.
 scalar

“Designating or of a quantity that has magnitude but no direction in space, as volume or temperature — n. a scalar quantity: distinguished from vector”^{[websters]} See also vector
 scripting

Generally, the use of a computer language where your program, or script, can be run directly with no need to first compile it to binary code as with with languages such as Java and C. Scripting languages often have simpler syntax than compiled languages, so the process of writing, running, and tweaking scripts can go faster. See also Python, Perl, Ruby, shell
 serial correlation

“As prices vary from day to day, you might expect to see patterns. If the price is high on Monday, you might expect it to be high for a few more days; and if it’s low, you might expect it to stay low. A pattern like this is called serial correlation, because each value is correlated with the next one in the series. To compute serial correlation, we can shift the time series by an interval called a lag, and then compute the correlation of the shifted series with the original... 'Autocorrelation' is another name for serial correlation, used more often when the lag is not 1.”^{[downey]} See also correlation
 shell

When you use a computer’s operating system from the command line, you're using its shell. Along with scripting languages such as Perl and Python, Linuxbased shell tools (which are either included with or easily available for Mac and Windows machines) such as grep, diff, split, comm, head, and tail are popular for data wrangling. A series of shell commands stored in a file that lets you execute the series by entering the file's name is known as a shell script. See also data wrangling, scripting, Perl, Python
 spatiotemporal data

Time series data that also includes geographic identifiers such as latitudelongitude pairs. See also time series data
 SPSS

A commercial statistical software package, or according to the product home page, “predictive analytics software.”^{[spss]} The product has always been popular in the social sciences. The company, founded in 1968, was acquired by IBM in 2009. See also predictive analytics
 SQL

The ISO standard query language for relational databases. Variations of this extremely popular language are often available for data storage systems that aren't strictly relational; watch for the phrase “SQLlike.”
 standard deviation

The square root of the variance, and a common way to indicate just how different a particular measurement is from the mean. “An observation more than three standard deviations away from the mean can be considered quite rare, in most applications.”^{[zumel]} Statistical software packages offer automated ways to calculate the standard deviation. See also variance
 standard normal distribution

A normal distribution with a mean of 0 and a standard deviation of 1. When graphed, it’s a bellshaped curve centered around the y axis, where x=0. See also normal distribution, mean, standard deviation
 standardized score

Also, standard score, normal score, zscore. “Transforms a raw score into units of standard deviation above or below the mean. This translates the scores so they can be evaluated in reference to the standard normal distribution.”^{[boslaugh]} Translating two different test sets to use standardized scores makes them easier to compare. See also standard deviation, mean, standard normal distribution
 Stata

A commercial statistical software package, not to be confused with strata. See also strata, stratified sampling
 strata, stratified sampling

“Divide the population units into homogeneous groups (strata) and draw a simple random sample from each group.”^{[gonick]} Strata also refers to an O'Reilly conference on big data, data science, and related technologies. See also Stata
 supervised learning

A type of machine learning algorithm in which a system is taught to classify input into specific, known classes. The classic example is sorting email into spam versus ham. See also unsupervised learning, reinforcement learning, machine learning
 support vector machine

Also, SVM. Imagine that you want to write a function that draws a line on a twodimensional xy graph that separates two different kinds of points—that is, it classifies them into two categories—but you can't, because on that graph they're too mixed together. Now imagine that the points are in three dimensions, and you can classify them by writing a function that describes a plane that can be positioned at any angle and position in those three dimensions, giving you more opportunities to find a working mathematical classifier. This plane that is one dimension less than the space around it, such as a twodimensional plane in a threedimensional space or a onedimensional line on a twodimensional space, is known as a hyperplane. A support vector machine is a supervised learning classification tool that seeks a dividing hyperplane for any number of dimensions. (Keep in mind that “dimensions” don't have to be x, y, and z position coordinates, but any features you choose to drive the categorization.) SVMs have also been used for regression tasks as well as categorization tasks. See also supervised learning, feature
 tdistribution

Also, student’s t distribution. A variation on normal distribution that accounts for the fact that you’re only using a sampling of all the possible values instead of all of them. Invented by Guiness Brewery statistician William Gossett (publishing under the pseudonym “student”) in the early 20th century for his quality assurance work there. See also normal distribution
 Tableau

A commercial data visualization package often used in data science projects.
 time series data

“Strictly speaking, a time series is a sequence of measurements of some quantity taken at different times, often but not necessarily at equally spaced intervals.”^{[boslaugh]} So, time series data will have measurements of observations (for example, air pressure or stock prices) accompanied by datetime stamps. See also spatiotemporal data, moving average
 UIMA

The “Unstructured Information Management Architecture” was developed at IBM as a framework to analyze unstructured information, especially natural language. OASIS UIMA is a specification that standardizes this framework and Apache UIMA is an opensource implementation of it. The framework lets you pipeline other tools designed to be plugged into it. See also computational linguistics, GATE
 unsupervised learning

A class of machine learning algorithms designed to identify groupings of data without knowing in advance what the groups will be. See also supervised learning, reinforcement learning, clustering .
 variance

“How much a list of numbers varies from the mean (average) value. It is frequently used in statistics to measure how large the differences are in a set of numbers. It is calculated by averaging the squared difference of every number from the mean.”^{[segaran]} Any statistical package will offer an automated way to calculate this. See also mean, bias, standard deviation
 vector

Webster’s first mathematical definition is “a mathematical expression denoting a combination of magnitude and direction,” which you may remember from geometry class, but their third definition is closer to how data scientists use the term: “an ordered set of real numbers, each denoting a distance on a coordinate axis”^{[websters]}. These numbers may represent a series of details about a single person, movie, product, or whatever entity is being modeled. This mathematical representation of the set of values makes it easier to take advantage of software libraries that apply advanced mathematical operations to the data. See also matrix, linear algebra
 vector space

A collection of vectors—for example, a matrix. See also vector, matrix, linear algebra
An open source set of command line and graphical user interface data analysis tools developed at the University of Waikato in New Zealand.
References
Carl Anderson, Creating a DataDriven Organization (Sebastopol: O'Reilly Media, 2015).
Naive Bayes for Dummies; A Simple Explanation, accessed 20150821.
Sarah Boslaugh, Statistics in a Nutshell, 2nd Edition (Sebastopol: O'Reilly Media, 2012).
David M. Bourg and Glenn Seeman AI for Game Developers (Sebastopol: O'Reilly Media, 2004).
Pedro Domingos, A Few Useful Things to Know about Machine Learning, accessed 20150905.
Leo Breiman and Adele Cutler, Random Forests, accessed 20150822.
Allen B. Downey Think Stats, 2nd Edition (Sebastopol: O'Reilly Media, 2014).
Larry Gonick and Woolcott Smith, The Cartoon Guide to Statistics (New York: HarperCollins, 1993)
S. N. Goodman, Toward evidencebased medical statistics. 1: The P value fallacy. Annals of Internal Medicine, 130:995–1004, 1999. (quoted in Reinhart)
Matthew Kirk, Thoughtful Machine Learning (Sebastopol: O'Reilly Media, 2014).
Matthew MacDonald, Excel 2013: The Missing Manual (Sebastopol: O'Reilly Media, 2013).
Michael Milton, Head First Data Analysis (Sebastopol: O'Reilly Media, 2009).
DJ Patil, A Memo to the American People from U.S. Chief Data Scientist Dr. DJ Patil, 20150215
Alex Reinhart, "An introduction to data analysis" in Statistics Done Wrong: The Woefully Complete Guide (San Francisco: No Starch Press, 2015)
Facts about Google and Competition, archive.org version accessed 20150905.
Joel Grus, Data Science from Scratch: First Principles with Python, (Sebastopol: O'Reilly Media, 2015).
David M. Levine, Statistics for Six Sigma Green Belts with Minitab and JMP (Upper Saddle River: Pearson, 2006).
Mahmoud Parsian, Data Algorithms, (Sebastopol: O'Reilly Media, 2015).
Toby Segaran, Programming Collective Intelligence, (Sebastopol: O'Reilly Media, 2015).
Shin Takahashi, The Manga Guide to Statistics, (Sebastopol: O'Reilly Media, 2008).
SPSS Software, accessed 20150822.
82. Stanton, J.M. (2012). Introduction to Data Science, Third Edition. iTunes Open Source eBook. Available: https://itunes.apple.com/us/book/introductiontodatascience/id529088127?mt=11
Victoria Neufeldt, Editor in Chief, Webster's New World College Dictionary , Third Edition (New York: Macmillan, 1997).
Wikipedia: Gradient boosting, accessed 20160228.
Wikipedia: Latent variable, accessed 20160228.
Alice Zheng, Striking parallels between mathematics and software engineering, accessed 20150911.
Nina Zumel and John Mount, Practical Data Science with R (Shelter Island: Manning Publications, 2014).