What is the difference between the two? Is there any crossover between them? Can anyone provide any examples of how each of them work? Thanks.
In: Mathematics
Statistics is a branch of mathematics which deals with analyzing and recognizing patterns in information. Often focusing on predicting future events from past data or estimating results for large group based on results from a smaller group.
Data Analytics is an applied skill set that includes statistics but also includes some side elements like computer programming, communications, and business acumen.
At it’s core Data Analytics uses applied statistics skill set to determine “What Happened?” “What Will Happen?” and “What Would Happen *if…”* Much of those questions and their answers are derived from a rich understanding of applied statistics.
But to answer that question you need data first right? You’ll need to know to how read that data, how to recognize errors and typos and how to eliminate them or fix them. That takes some computer science and programming know how and probably a programming language or two.
Then you need to communicate that information to someone else (like say your CEO), should you use a box plot or a histogram? What about those outliers, do you show them? Crap, maybe you screwed up your cluster classification and need to apply a different algorithm. So there is a communications and visualization component as well.
Finally, you need to know your business to recognize obvious facts “hidden” in the data. According to my research the average tree on my farm has 1,000 apples so I could make a business plan off of that. *Or* I could know enough to look at the data and realize by trees come in 2 clusters, one with 4,000 apples per tree and the other 0. *Maybe those other trees aren’t fucking apple trees, Doug*. Or maybe that ground wasn’t fertilized properly, or maybe there are pests, who knows. In order to make a *decision* from our analysis I can’t just be a numbers guy, I need to actually know the nuances of my business and make an informed decision.
Statistics is a mathematical theory based on probability that seeks to evaluate if a claim about data is supported by the data, and to make predictions from this data.
Data analysis is an application of statistics. But it also involves things like data collection, data preparation and filtering, data visualization, and interpretation. These things are not statistics, but needed for work that uses statistics.
Statisticians fall into two branches: those who develop new mathematical theories for statistics, and those who do data analysis. Some people do both.
Non-statisticians also do all sorts of data analysis. Engineers, scientists, doctors, marketing analysts, stock brokers, etc all use data analysis as a core part of their job, even though they may have a different title and are not statisticians.
Generally any data analysis job uses statistics, but statisticians are specially trained to develop and mathematically prove the theories they use, whereas other jobs have a more applied training.
There are also jobs like data science, which tends to focus on building data analysis pipelines into software and websites. So there is often a high emphasis on automation and efficiency.
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