3 Differences between a Data Scientist and a Statistician
The supply of data scientists cannot keep up with the demand as companies across industries want to use data to power their products, services, and decision-making abilities. Even though the position is popular, defining the role of a data scientist can be difficult, especially in the way that it relates to the role of a statistician. These are three ways that data scientists differ from statisticians.
Knowledge of Base Languages
Both statisticians and data scientists work with programming languages. Statisticians use languages, such as R, S, ado, and Mata, that help then analyze data. These popular languages form the base of the code that statisticians write to get statistical software programs to generate models and output. Data scientists would know some of the statistical programming languages common to statisticians, especially R, but also other computer programming languages. As data scientists need to use data to create programs, they have a core knowledge base of C++, SQL, Julia, Java, and Python. This knowledge lets them use data to build apps, websites, and machine learning models.
The tools that statisticians and data scientists use also differ. Statisticians primarily rely on tools that help them collect data and build mathematical models. Statistical software popular to them includes SPSS, STATA, and R. These programs help them design, check and produce output for quantitative models. Data scientists rely on tools that help them explore, mine and manage big data, such as Hadoop. Software, such as Tableau, helps them create data visualizations. Both data scientists and statisticians commonly use R to create statistical output. Data scientists can also rely on a mix of qualitative and quantitative data and need different tools for examining both.
Application versus Theory
Statistics as a field focuses on the theory of developing quantitative models. Data science uses those models and applies them to challenges in the business world. The work they do requires isolating problems and coming up with solutions that can work. Statisticians tend to communicate their findings to other statisticians, especially academics. They do this by using the jargon and theoretical concepts of their field. Data scientists communicate with managers and other employees to relay results and suggestions for solutions in everyday language.
Statisticians and data scientists have a lot in common. Both rely on the power of data analytics in their work. Strong differences between them do exist. Whereas statisticians primarily focus on mathematical models and improving methodology itself, data scientists take an applied approach that integrates other disciplines and skills.