Data analytics and data science often mix with newbies in this field. While there is a lot of overlap between the two, there are also some major differences. In this article, we’ll look at the differences (and similarities) between data analytics and data science.
Let’s start with data analysis. The goal of a data analyst is to use pre-existing data to solve current business problems. Typically, a data analyst’s primary role is to use data to create reports and dashboards. Data analysts do this using tools such as Microsoft Excel, structured query language (SQL), and visualization software such as Tableau or Microsoft Power BI.
When it comes to data science, things get a little more complicated. The goal of a data scientist is to develop machine learning models and analysis methods. Data scientists help gather data that they then review to identify trends and patterns that could affect the business. Another major responsibility of a data scientist is data cleansing and testing. Data scientists also use Excel, SQL, and visualization tools – but they also rely heavily 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 becomes so large that the two titles become practically interchangeable. For example, data analysts could clean up data or get into the extraction, transformation, and loading (ETL) process. On the other hand, a data scientist could be responsible for creating dashboards or coding SQL queries against existing data.
However, in a perfect world there is a dedicated data analysis team and a data science team. In general, data scientists need to understand most of the responsibilities of a data analyst, augmented by machine learning (ML). Machine learning is an advanced method of data analysis that uses artificial intelligence (AI) to predict outcomes. For this reason, data science is often viewed as a level above data analysis.
It’s worth noting that the word “analyst” gets 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, which are often given different names, such as business analyst or marketing analyst. These types of roles will almost never do advanced data analysis like machine learning.
A bachelor’s degree in STEM is usually required to become a data analyst. However, it’s not uncommon for someone to move into data analytics from a different field, especially if they have extensive domain knowledge in a particular industry. In fact, becoming a data analyst without a degree (without saying it will be easy) isn’t impossible. As long as you know the three core tools Excel, SQL and a visualization tool, you could have the chance to become a data analyst. To become a data scientist, it is almost guaranteed that you will need a bachelor’s degree in STEM, with a master’s degree being 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 is not that significant. As mentioned earlier, each individual’s responsibilities can be quite fluid at times, which can lead to confusion as to which role they actually play. Hopefully this article has cleared up some of the differences between data analytics and data science. Don’t get hold of labels like that, though – if you’re interested in both, learn the core competencies of Excel, SQL, and visualization tools first. From there, you can 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 on your journey in the data world!
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