What is the difference between Data Science and Data Analytics?

Posted by Aman Khan on March 27th, 2020

Data science and data analytics, most trending words of this era, both work in the same field but differently. People often consider the data science and data analytics are the same, and even they are interchangeable but completely different from each other.

“One thing we have noticed is students are not aware of these two terms because a number of students came to us for a Data Science Training Course in Noida while they wanted to become a data analyst”, a popular training institute says. The reason could be these two words are interchangeable, the institute said further.

Before knowing the difference, you need to understand the definition of these two words.

What is data science?

Data science mainly focuses on finding insights from unstructured data or huge amounts of data. This field focuses on discovering the answers with several techniques. In other words, data science includes machine learning, various tools, programming concepts, principles to find answers.

What is data analytics?

Data analytics is mainly used to derive the insights that are needful for businesses to improve productivity. Here, a mass of data is analyzed while keeping in mind the business needs. Data analysis requires many tools and particular approaches to extract meaningful data.

Data science vs Data analytics

 

Skills

Data scientists are supposed to have outstanding knowledge of machine learning, algorithms, big data, databases, programming languages such as Python, Ruby, etc. On the whole, you need to have advanced programming knowledge and technical skills to become a data scientist.

While data analytics is learning of databases and tools that are used for the statics and data wrangling means less knowledge of programming and a better understanding of data.

Job roles

In data science, you need to develop and provide appropriate solutions for businesses. The data scientist’s work includes managing big data and creating databases or solutions with the relevant data models to aid the businesses.

On the other hand, data analysts collect the data in various forms and then process the data with the tools that might be developed by data scientists, to help the organization to make better decisions.

Technical knowledge

Data scientists are considered highly educated professionals having a good grip on programming languages, mathematics, statistics, and technical tools. Data science is a field where you are required to have knowledge of multiple programming languages and tools. And is an umbrella term that incorporates data analytics too.

While working as a data analyst, you are not restricted to learn advanced programming concepts, though, there are various tools used to manage the data. With data analytics, you only work with the data using several tools which is not true in the case of the data scientists.

Application

The data science use has brought advancement in several industries. Social platforms are able to perform large scale research easily with the help of data science to get useful insights. Fraud detection, predictive analytics, network management, are a few use cases of data science.

Data analytics is applied in e-commerce, financial services, agriculture, market research. Today the market cannot afford not to use data analytics as now companies are operating globally.

Conclusion

We hope you have found this article helpful and now you have understood the difference between data science and data analytics. Both are shining career options today. With a Data Analytics Training Course in Noida, you will not be able to work in the big companies but also, you can make a handsome amount of salary. Moreover, you have a clear idea of what you want to pursue. The data scientists are highly paid professionals compared to the data analysts.