Dan Ariely, a well-known Duke economics professor, 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 a great deal of data terminology. While many people toss around terms like data science, data analysis, big data, and data mining, even the experts have trouble defining them. Below, we focus on one of the more important distinctions as it relates your career: the often-muddled difference between a data analyst and a data scientist.
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Working in Data Analytics
The responsibility of data analysts can vary across industries and companies, but fundamentally, data analysts utilize data to draw meaningful insights and solve problems. They 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.
Some industry leaders, such as 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.”
- Typical background: 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.
Working in Data Science
Data scientists, on the other hand, estimate the unknown by asking questions, writing algorithms, and building statistical models. The main difference between a data analyst and a data scientist is heavy coding. Data scientists can arrange undefined sets of data using multiple tools at the same time, and build their own automation tools and frameworks. According to John D Cook, “some say a data scientist is a statistician who can program, and data science is statistics on a Mac.”
This kind of data scientist, according to Michael Holster, is a 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).”
- Typical background: Drew Conway’s 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.
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, or strategic consultant.
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