logo
logo
Sign in

Difference Between Data Analytics and Data Science

avatar
SynergisticIT1
Difference Between Data Analytics and Data Science

One of the most in-demand professions today are data analysts and data scientists. In terms of professional growth and scope of work, they are unmatched. Even in the Future of Jobs report, it is mentioned that the demand for data professionals will increase tremendously across industries.


Many of you must be interested in working as a data scientist and data analyst. But do you know that both roles are different? Let's look at the differences between the two and how these two are different.


Data Analysts Vs. Data Scientists

Role

The biggest difference between data scientists and data analysts is in the tasks they perform. Data analysts usually work with structured data and use various tools such as SQL, R, Python, data visualization, and statistical analysis to solve business problems. They acquire data from multiple sources, clean and organize it for analysis, find patterns and trends, and communicate the findings with organizational leaders to help them make decisions.

Data scientists, on the other hand, use advanced data techniques to make predictions for the future. They design predictive modeling processes and automate ML algorithms to handle not only structured but unstructured and semi-structured data. It's a more specialized role, comprising tasks a step ahead of data analysts. They gather, clean, and process the data. Besides, they work in developing tools to analyze and monitor data. Their task is also to build visualization tools, reports, and dashboards.


Educational Requirements

Data analysts must have a bachelor's degree in computer science, mathematics, statistics, finance, etc. On the other hand, data scientists typically have a master's or doctoral degree in information technology, mathematics, and statistics.


Skill and tools

Data analyst skills include database management and reporting, data mining, data warehousing, data modeling, R, Python or SAS, statistical analysis, SQL, and data analysis. A data analyst course will help you learn these skills.

Data scientist skills include software development, object-oriented programming, Hadoop, Java and Python programming, machine learning, data mining, data warehousing, and data analysis. Every data science course includes these skills.


You can clearly see there are some fundamental differences between the two professions. So, when choosing a data science bootcamp, consider your educational background to align the role with your skills and knowledge. A good data science bootcamp can help you acquire the skills to pursue a career as a data scientist or data analyst.  

collect
0
avatar
SynergisticIT1
guide
Zupyak is the world’s largest content marketing community, with over 400 000 members and 3 million articles. Explore and get your content discovered.
Read more