Analytics is the field of using software tools to analyze and quickly summarize large data sets. An analyst might be given a huge data set with billions of data points on cancer patients, for example, and their role is to find insights and possible correlations within the data. They may look for patterns or trends in the data, such as whether there is a correlation between diet and cancer or if certain patients have abnormal levels of iron in their blood.
On the other hand, a statistician takes a more deliberate approach to analyzing data and uses mathematical rigor to determine whether there is causation in any of the factors identified by the analyst. While the analyst is judged on the speed of discovering insights in large and messy data sets, the statistician is measured based on the thoroughness of their conclusions. They are more cautious in making conclusions and act like quasi epistemologists.
It’s important to note that the role of an analyst is to find insights as quickly as possible and to present the data in a clear and concise way, whereas a statistician’s role is to determine if there is actual meaning in these insights by using statistical methods. Therefore, when hiring for these positions, it’s important to be aware of the specific skills and responsibilities of each role.
This blog was inspired by Cassie Kozyrkov who is a Chief Decision Scientist at Google. I highly recommend checking out her articles on medium. She possesses exceptional ability in clearly and effectively communicating the intricacies and complexities of the field of data science.
