16 analytic disciplines compared to data science

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.

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.