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

An open-source 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

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

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

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.

“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

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

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.

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

“Data-Driven 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

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 enterprise-oriented job with greater system architecture responsibility and less hands-on work with the data.)

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

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 yyyy-mm-dd 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

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.

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

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

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 terms property, attribute, or field instead of “feature.” See also 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

“General Architecture for Text Engineering,” an open source, Java-based 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 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 stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function.”^{[wikipediagb]}

See dependent variable

A scripting language (no relation to Java) originally designed in the mid-1990s for embedding logic in web pages, but which later evolved into a more general-purpose 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

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

“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 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]

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

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 non-linear 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

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

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

(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 two-dimensional array. As with its one-dimensional equivalent, a vector, this mathematical representation of the two-dimensional 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

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

“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

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

“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

The analysis of sequences of n items (typically, words in natural language) to look for patterns. For example, trigram analysis examines three-word 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

A database management system that uses any of several alternatives to the relational, table-oriented 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

“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

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

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

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

“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]

See prior distribution

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 business-oriented spin of “predictive analytics” makes it a popular buzz phrase in marketing literature. See also predictive modeling, machine learning, SPSS

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

“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

“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

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

An open-source programming language and environment for statistical computing and graph generation available for Linux, Windows, and Mac.

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

“...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

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,

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

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 stretched-out “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.

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

“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

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

“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

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, Linux-based 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

Time series data that also includes geographic identifiers such as latitude-longitude pairs. See also time series data

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 “SQL-like.”

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

“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 date-time stamps. See also spatiotemporal data, moving average

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 open-source implementation of it. The framework lets you pipeline other tools designed to be plugged into it. See also computational linguistics, GATE

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

A collection of vectors—for example, a matrix. See also vector, matrix, linear algebra