Data analytics is the field that deals with the analysis of data, usually for business-related objectives, though its scope covers any organization. A data analyst handles data with various tools, such as a spreadsheet program (usually MS Excel), a data visualization program (e.g. Tableau), a database program (e.g. PostgreSQL), and a programming language (usually Python), and then presents her findings using a presentation program (e.g. MS PowerPoint). This aids business decisions and provides useful insights into the state of an organization. It's akin to the Business Intelligence role, though a bit more hands-on and programming-related.
What about Statistics though? Well, it is a potent data analytics tool but whether it's something a data analyst actually needs is quite debatable. Apart from some descriptive stats that you are bound to use in one way or another, the bulk of Statistics is way too specialized and irrelevant to a data analyst. It doesn't hurt knowing it but it would highly biased to promote this sort of knowledge (in most cases it doesn't even classify as know-how) when there are much more efficient and effective tools out there. For example, being about to handle the data coming from various sources and organize it, be it through an ETL tool or some data platform, is far more of a value-add than trying to do something that's often beyond the scope of your role as an analyst (e.g. an in-depth analysis of the data at hand, through advanced data engineering or predictive modeling).
Having said that, Statistics is useful in data science, particularly if you are not well-versed in more advanced methodologies, such as machine learning. That’s why most data science courses start with this part of the toolbox along with the corresponding programming libraries. Also, most time-series analysis models are Stats-based and data scientists are often required to work with them, at least as a baseline before proceeding to build more complex models. Moreover, if you want to test a hypothesis (something quite common in data science work), you need to make use of statistical tests.
In data analytics, however, where the objective is somewhat different, Stats seems to be a somewhat unnecessary tool. Perhaps that's why most data analysts focus on other more practical and guaranteed ways to add value, such as dashboards, intuitive spreadsheets, and useful scripts, rather than building statistical models that few people care about. Besides, if someone needs something more in-depth and scientifically sound, they can always hire a couple of data scientists to work alongside with the analysts.
Regardless of your role, you can learn more about data science and the mindset behind it in my book Data Science Mindset, Methodologies, and Misconceptions. Although it was published about 4 years ago, it remains relevant and can shed a lot of light on Statistics' role in this field, as well as other methodologies and tools used by data scientists. Check it out when you have some time. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.