What are the differences between data science, data mining, machine learning, statistics, operations research, and so on?

Here I compare several analytic disciplines that overlap, to explain the differences and common denominators. Sometimes differences exist for nothing else other than historical reasons. Sometimes the differences are real and subtle. I also provide typical job titles, types of analyses, and industries traditionally attached to each discipline. Underlined domains are main sub-domains. It would be great if someone can add an historical perspective to my article.

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Data Science

First, let's start by describing data science, the new discipline.

Job titles include data scientist, chief scientist, senior analyst, director of analytics and many more. It covers all industries and fields, but especially digital analytics, search technology, marketing, fraud detection, astronomy, energy, healhcare, social networks, finance, forensics, security (NSA), mobile, telecommunications, weather forecasts, and fraud detection.

Projects include taxonomy creation (text mining, big data), clustering applied to big data sets, recommendation engines, simulations, rule systems for statistical scoring engines, root cause analysis, automated bidding, forensics, exo-planets detection, and early detection of terrorist activity or pandemics, An important component of data science is automation, machine-to-machine communications, as well as algorithms running non-stop in production mode (sometimes in real time), for instance to detect fraud, predict weather or predict home prices for each home (Zillow).

An example of data science project is the creation of the fastest growing data science Twitter profile, for computational marketing. It leverages big data, and is part of a viral marketing / growth hacking strategy that also includes automated high quality, relevant, syndicated content generation (in short, digital publishing version 3.0).

Unlike most other analytic professions, data scientists are assumed to have great business acumen and domain expertize -- one of the reasons why they tend to succeed as entrepreneurs.There are many types of data scientists, as data science is a broad discipline. Many senior data scientists master their art/craftsmanship and possess the whole spectrum of skills and knowledge; they really are the unicorns that recruiters can't find. Hiring managers and uninformed executives favor narrow technical skills over combined deep, broad and specialized business domain expertize - a byproduct of the current education system that favors discipline silos, while true data science is a silo destructor. Unicorn data scientists (a misnomer, because they are not rare - some are famous VC's)  usually work as consultants, or as executives. Junior data scientists tend to be more specialized in one aspect of data science, possess more hot technical skills (Hadoop, Pig, Cassandra) and will have no problems finding a job if they received appropriate training and/or have work experience with companies such as Facebook, Google, eBay, Apple, Intel, Twitter, Amazon, Zillow etc. Data science projects for potential candidates can be found here.

Data science overlaps with

• Computer science: computational complexity, Internet topology and graph theory, distributed architectures such as Hadoop, data plumbing (optimization of data flows and in-memory analytics), data compression, computer programming (Python, Perl, R) and processing sensor and streaming data (to design cars that drive automatically)
• Statistics: design of experiments including multivariate testing, cross-validation, stochastic processes, sampling, model-free confidence intervals, but not p-value nor obscure tests of thypotheses that are subjects to the curse of big data
• Machine learning and data mining: data science indeed fully encompasses these two domains.
• Operations research: data science encompasses most of operations research as well as any techniques aimed at optimizing decisions based on analysing data.
• Business intelligence: every BI aspect of designing/creating/identifying great metrics and KPI's, creating database schemas (be it NoSQL or not), dashboard design and visuals, and data-driven strategies to optimize decisions and ROI, is data science.
  

Comparison with other analytic discplines

• Machine learning: Very popular computer science discipline, data-intensive, part of data science and closely related to data mining. Machine learning is about designing algorithms (like data mining), but emphasis is on prototyping algorithms for production mode, and designing automated systems (bidding algorithms, ad targeting algorithms) that automatically update themselves, constantly train/retrain/update training sets/cross-validate, and refine or discover new rules (fraud detection) on a daily basis. Python is now a popular language for ML development. Core algorithms include clustering and supervised classification, rule systems, and scoring techniques. A sub-domain, close to Artificial Intelligence (see entry below) is deep learning.
  

• Data mining: This discipline is about designing algorithms to extract insights from rather large and potentially unstructured data (text mining), sometimes called nugget discovery, for instance unearthing a massive Botnets after looking at 50 million rows of data.Techniques include pattern recognition, feature selection, clustering, supervised classification and encompasses a few statistical techniques (though without the p-values or confidence intervals attached to most statistical methods being used). Instead, emphasis is on robust, data-driven, scalable techniques, without much interest in discovering causes or interpretability. Data mining thus have some intersection with statistics, and it is a subset of data science. Data mining is applied computer engineering, rather than a mathematical science. Data miners use open source and software such as Rapid Miner.
  

• Predictive modeling: Not a discipline per se. Predictive modeling projects occur in all industries across all disciplines. Predictive modeling applications aim at predicting future based on past data, usually but not always based on statistical modeling. Predictions often come with confidence intervals. Roots of predictive modeling are in statistical science.
  

• Statistics. Currently, statistics is mostly about surveys (typically performed with SPSS software), theoretical academic research, bank and insurance analytics (marketing mix optimization, cross-selling, fraud detection, usually with SAS and R), statistical programming, social sciences, global warming research (and space weather modeling), economic research, clinical trials (pharmaceutical industry), medical statistics, epidemiology, biostatistics.and government statistics. Agencies hiring statisticians include the Census Bureau, IRS, CDC, EPA, BLS, SEC, and EPA (environmental/spatial statistics). Jobs requiring a security clearance are well paid and relatively secure, but the well paid jobs in the pharmaceutical industry (the golden goose for statisticians) are threatened by a number of factors - outsourcing, company mergings, and pressures to make healthcare affordable. Because of the big influence of the conservative, risk-adverse pharmaceutical industry, statistics has become a narrow field not adapting to new data, and not innovating, loosing ground to data science, industrial statistics, operations research, data mining, machine learning -- where the same clustering, cross-validation and statistical training techniques are used, albeit in a more automated way and on bigger data. Many professionals who were called statisticians 10 years ago, have seen their job title changed to data scientist or analyst in the last few years. Modern sub-domains include statistical computing, statistical learning (closer to machine learning), computational statistics (closer to data science), data-driven (model-free) inference, sport statistics, and Bayesian statistics (MCMC, Bayesian networks and hierarchical Bayesian models being popular, modern techniques). Other new techniques include SVM, structural equation modeling, predicting election results, and ensemble models.
  

• Industrial statistics. Statistics frequently performed by non-statisticians (engineers with good statistical training), working on engineering projects such as yield optimization or load balancing (system analysts). They use very applied statistics, and their framework is closer to six sigma,quality control and operations research, than to traditional statistics. Also found in oil and manufacturing industries. Techniques used include time series, ANOVA, experimental design, survival analysis, signal processing (filtering, noise removal, deconvolution), spatial models, simulation, Markov chains, risk and reliability models.
  

• Mathematical optimization. Solves business optimization problems with techniques such as the simplex algorithm, Fourier transforms (signal processing), differential equations, and software such as Matlab. These applied mathematicians are found in big companies such as IBM, research labs, NSA (cryptography) and in the finance industry (sometimes recruiting physics or engineer graduates). These professionals sometimes solve the exact same problems as statisticians do, using the exact same techniques, though they use different names. Mathematicians use least square optimization for interpolation or extrapolation; statisticians use linear regression for predictions and model fitting, but both concepts are identical, and rely on the exact same mathematical machinery: it's just two names describing the same thing. Mathematical optimization is however closer to operations research than statistics, the choice of hiring a mathematician rather than another practitioner (data scientist) is often dictated by historical reasons, especially for organizations such as NSA or IBM.
  

• Actuarial sciences. Just a subset of statistics focusing on insurance (car, health, etc.) using survival models: predicting when you will die, what your health expenditures will be based on your health status (smoker, gender, previous diseases) to determine your insurance premiums. Also predicts extreme floods and weather events to determine premiums. These latter models are notoriously erroneous (recently) and have resulted in far bigger payouts than expected. For some reasons, this is a very vibrant, secretive community of statisticians, that do not call themselves statisticians anymore (job title is actuary). They have seen their average salary increase nicely over time: access to profession is restricted and regulated just like for lawyers, for no other reasons than protectionism to boost salaries and reduce the number of qualified applicants to job openings. Actuarial sciences is indeed data science (a sub-domain).
  

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