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Data analytics and data science often get mixed up amongst newcomers in the field. Although there is a lot of overlap between the two, there are also some major differences. In this article, we will go over the differences (and similarities) between data analytics and data science.
First, let’s get into data analytics. The goal of a data analyst is to use pre-existing data to solve current business problems. Typically, the primary responsibility of a data analyst is to use data to create reports and dashboards. Data analysts do this by using tools like Microsoft Excel, structured query language (SQL), and visualization software such as Tableau or Microsoft Power BI.
As for data science, things get a bit more complicated. The goal of a data scientist is to develop machine learning models and analytical methods. Data scientists help gather data, which they review afterward, to find trends and patterns that could affect business. Another big responsibility of a data scientist is data cleaning and data testing. Data scientists also use Excel, SQL, and visualization tools – however, they also heavily rely on programming languages like Python and R.
Read: Python versus R for Data Analytics
Data Scientist versus Data Analyst
Depending on the industry and/or company, the gray area between a data analyst and a data scientist often gets large enough to where the two titles become virtually interchangeable. For instance, data analysts could find themselves cleaning data, or getting into the extract, transform, and load (ETL) process. On the other hand, a data scientist could be responsible for creating dashboards or coding SQL queries for already-existing data.
In a perfect world, though, there is a dedicated data analytics team and data science team. Generally speaking, data scientists are required to know most of a data analyst’s responsibilities, with the addition of machine learning (ML). Machine learning is an advanced method of data analysis that utilizes artificial intelligence (AI) to predict outcomes. For this reason, data science is often viewed as a step above data analytics.
It is worth mentioning that the word “analyst” is thrown around a lot these days. Not everyone who works in Excel is a data analyst. However, there are some exceptions when it comes to less technical data analyst positions that are often given different names, such as business analyst or marketing analyst. These types of roles will almost never do any kind of advanced data analysis like machine learning.
To become a data analyst, it usually requires a bachelor’s degree in STEM. However, it’s not unheard of for someone to transition into data analytics from another field, especially if they have extensive domain knowledge in a specific industry. In fact, it’s not impossible to become a data analyst with no degree at all (not saying it will be easy). As long as you know the three core tools of Excel, SQL, and a visualization tool – you could have a shot at becoming a data analyst. As for becoming a data scientist, it is almost guaranteed that you will need a bachelor’s degree in STEM, with a master’s degree preferred in most cases.
Read: Introduction to Machine Learning in Python
The difference between data analytics and data science is significant. Ironically, the difference between a data analyst and a data scientist isn’t as significant. As previously mentioned, the responsibilities of each can be quite fluid at times, so it can create some confusion as to what role it actually is. Hopefully, this article cleared up some of the differences between data analytics and data science. Don’t get so hung up on labels though – if you are interested in both, try learning the core skills of Excel, SQL, and visualization tools first. From there, you could decide if you want to go the extra mile and learn a programming language that excels at data manipulation and statistics, like Python or R. Either way, knowing the differences between these two disciplines will help you a lot throughout your journey in the data world!
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