Data is building hugely day after day. Hence, an immediate need lies in hiring skilled professionals who can perform data analytics effectively.
Data analyst, data engineer, business analyst, and data scientist are some of the key roles concerning data analysis. Though these terms appear similar, each has its significance and a different role.
Knowing the required skill sets and mastering them helps gain a better position and be more challenging in their performance. In particular, the role preferred by the data analysts is usually data scientist.
Let us move ahead to know the key job roles of a data analyst and a data scientist:
Firstly, we will brief about the important functions of a data analyst:
- Extract data from significant sources
- Analyze data through proper study and tools to derive solutions
- Consider only the vital information and remove any unnecessary data
- Generate reports helping businesses to make effective decisions
Below are stated are some of the key job roles of a data scientist. Let us see how they differ from a data analyst’s job:
- Develop models according to the business needs to simplify complexities and generate solutions
- Develop machine learning techniques and algorithms to test the data
- Use different visualization methods to represent data and derive valuable information
- Dive deep into the data to solve business problems with ease
After knowing about the unique roles of the data analyst and data scientist, let us know how to grow as a data scientist.
First of all, one has to be clear that the job is not an overnight process but includes significant efforts and time. A data scientist should be strong in maths, statistics, and programming skills. Above all, understanding the business problems and rendering the right solution through data analysis is important.
The prominent steps of our transition journey include the following:
Enhance the coding skills
A data scientist might not be excellent at coding but must be good, though. The prominent tools used by the data scientists include R and Python, which help derive a valuable understanding of the large volumes of data.
Machine learning and data visualization
Tools usage is, of course, fetching. Along with this, it is suggested to gain familiarity with machine learning and deep learning algorithms that would support career growth. Moreover, having good knowledge about databases, fundamental maths, algebra, statistics, and coding skills in python is required.
Participate in contests
As part of skill-building, take part in various contests and competitions. This helps you gain more knowledge about the subject. No worries if you could not prove up to the mark. Understand where things have been lacking, and try to improve in those areas.
Interact with a data scientist
The best opportunity to grow as a data scientist lies in interacting with the existing data scientists. Know their approach to building algorithms. Also, find their ideas and opinions on any assignment. Consequently, this would build your thinking ability.
Conclusion :
Data science is evolving. So, it is always suggested to stay tuned with the latest updates. The effective option that lies for the growing data analysts is to get oneself connected to the data scientists on the social media platform, gain knowledge about their research, attend data science webinars and conferences.
This being said, how about sources providing training sessions on data science? Sounds good! ONPASSIVE, an AI-enabled organization, has something unique to give to the interested aspirants in data science. O-Cademy is an online training platform providing a wide variety of courses. Gain its best advantages with its optimal usage.
