Well-known Duke Economics professor Dan Ariely once said about big data: “Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” This concept applies to much of data terminology. While many toss around terms like data science, data analysis, big data and data mining, even the experts have trouble defining them. Here we will focus on one of the more important distinctions in the vocabulary as it relates your career: the often-muddled difference between a Data Analyst and a Data Scientist.

The responsibility of a data analyst can vary across different industries and companies, but the common component is using data to draw meaningful insights and solve problems. Data analysts generally analyze well-defined sets of data using an arsenal of different tools to answer tangible business needs: e.g. why sales dropped in a certain quarter, why a marketing campaign fared better in certain regions, how internal attrition affects revenue. Data analysts have a range of fields and titles, including (but not limited to) database analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst. The best data analysts have both technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients.

To complicate matters, Pandora’s Research Director Michael Holster would describe this type of professional as a Type A Data Scientist (as quoted by Robert Chang, a former data scientist at Twitter): “The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.”

  • Profile: Data analysts can have a background in mathematics and statistics, or they can supplement a non-quantitative background by learning the tools needed to make decisions with numbers.
  • Skills and Tools: Data mining/data warehouse, data modeling, R or SAS, SQL, statistical analysis, database management & reporting, data analysis.

Data Scientists estimate the unknown by asking questions, writing algorithms, and building statistical models. The main difference between a data analyst and data scientist is heavy coding; according to John D Cook, “some say a data scientist is a statistician who can program, and data science is statistics on a Mac.” Data scientists can arrange undefined sets of data using multiple tools at the same time, and build their own automation tools and frameworks.

This kind of data scientist is a bona fide Michael Holster Type B Data Scientist: “The B is for building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data ‘in production.’ They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).”

  • Profile: Drew Conway’s acclaimed Data Science Venn Diagram describes a Data Scientist as someone who has mathematical and statistical knowledge, hacking skills, and substantive expertise.
  • Skills and Tools: Machine learning, software development, hadoop, java, data mining/data warehouse, data analysis, python and object oriented programming.
Drew Conway's Data Science Venn Diagram
Drew Conway’s Data Science Venn Diagram

No matter how you phrase it, people are talking about data. Data analysis is the cornerstone for working with data in any capacity, whether as a data analyst, data scientist, business intelligence analyst, marketing manager, project manager, strategic consultant, or any of the innumerable other roles that we will save for another time.

Big Data: Key Jobs and Salaries (Data Science Central)
My Two Year Journey as a Data Scientist at Twitter (Robert Chang)
What is the Difference between a Data Analyst and a Data Scientist (Quora)
A Rose by Any Other Name: Data Science (John D. Cook)
The Data Science Venn Diagram (Drew Conway)