Data Science vs Data Analytics Infographic – Choose Your Career

As we further ourselves technologically, there are times when a new technology comes in and changes the whole picture. Big Data is visibly the trend for the 21st century as we continue to best ourselves in the amount of data we generate every year.

For those working in the IT industry, the terms “Data Science” and “Data Analytics” appear both- lucrative and exciting. However similar they may sound, they explain mildly different disciplines.

To clear the air, we have curated this Data Science Vs Data Analytics infographic for you so you no longer confuse the two.

This can be of help to you whether you’re a fresher looking to build a career, an employee with a mid-career crisis, or a data scientist or analyst yourself. Read on to find out how the two are different, and which one is the right pick for you.

Data Science vs Data Analytics Infographic

Data Science is a field that makes use of scientific methods and algorithms in order to extract knowledge and discover insights from data (structured on unstructured).

Data Analytics is the process of using specialized systems and software to inspect information in datasets in order to derive conclusions. This focuses on specific areas with specific goals. Briefly, the two perform the following roles:

Data Scientist:

• Perform exploratory data analysis
• A process, cleanse, and verify the integrity of data
• Identify trends in data and make predictions
• Generate insights using Machine Learning techniques

Data Analyst:

• Perform exploratory data analysis
• Discover new patterns using statistics tools
• Develop KPI’s and visual representations of data
• Clean dirty data

On average, a data scientist takes home around $117,345 every year, where a data analyst does around $67,377.

An easy way to remember the difference: Data Science looks to the future, while Data Analytics looks at the past. In an infographic comparison, Data Science includes tools like Python, R, and machine learning. Data Analytics uses SQL, Excel, and reporting tools. Both roles are important, but they solve different problems in a business.

Intrigued? Now that you can tell the two apart, which one would you go for? Tell us below.

Got more to add in this Data Science vs Data Analytics Infographic? Go right ahead and comment below; let’s grow together.