2.1 Comparison between Statistician and Data Scientist
Statistics as a scientific area can be traced back to 1749 and statistician as a career has been around for hundreds of years with well-established theory and application. Data Scientist becomes an attractive career for only a few years along with the fact that data size and variety beyond the traditional statistician’s toolbox and the fast-growing of computation power. Statistician and data scientist have a lot of in common, but there are also significant differences.
Both statistician and data scientist work closely with data. For the traditional statistician, the data is usually well-formatted text files with numbers and labels. The size of the data usually can be fitted in a PC’s memory. Comparing to statisticians, data scientists need to deal with more varieties of data:
- well-formatted data stored in a database system with size much larger than a PC’s memory or hard-disk;
- huge amount of verbatim text, voice, image, and video;
- real-time streaming data and other types of records.
One unique power of statistics is to make statistical inference based on a small set of data. Statisticians spend most of their time developing models and don’t need to put too much effort on data cleaning. Today, data is relatively abundant, and modeling is only part of the overall effort, often a small part. Due to the active development of some open source communities, fitting models is not too far from button pushing. Data scientists instead spend lot of time preprocessing and wrangling the data before feeding them to the model.
Different from statisticians, data scientists often focus on delivering actionable results and sometimes need to fit model on the cloud. The data can be too large to read in laptop. From the entire problem-solving cycle, statisticians are usually not well integrated with the production system where data is obtained in real time; while data scientists are more embedded in the production system and closer to the data generation procedures.