Skills needed to be a Data Scientist
The field of Data Science comprises of many stages and in no way is limited to a person belonging to the field of computers. Many people out there consider data science to be a part of the field of Information Technology. One must have heard that you need to be good at coding or have some experience in coding to even start with the data but guess what… NO! YOU DO NOT. When I was a newbie to the field, I had the same ideology pinned in my brain but as I gained some experience and interacted with multiple people, the cloud of myths scattered. Now all I say is: ANYONE CAN BE A DATA SCIENTIST.
Big data is not about the data. — Peter Sondergaard
Believe it or not, these skills combine together to only be a 20 percent of what data science is. The list of skills that I feel arranged according to importance are:
- Knowledge about the field of data.
- Basic statistics
- Microsoft Excel
- Understanding of the client requirements
- Visualization
Knowledge about the field of data.
The most important skill is the knowledge of the field on which data science is being applied. If you are from the field of healthcare, you know what terminologies you are dealing with. If you are from psychology, you understand what the emotional scoreboard means. The more you are attached to the field you love, the more motivated and innovative results you will thrive for.
Basic statistics
Stats is a necessity. But do not be scared if you hate mathematics. The statistics required for the understanding, prediction and general exploratory analysis is not that complicated. There are many software and websites that produce the result for you. All you need to do is understand the result that is being generated. For example, if you have a dataset whose column’s variance is high then it means that the recorded data does not have much consistency. These small things can help you understand the result being generated which mean much more than just a few numbers.
Microsoft Excel
This the first go-to tool for many data scientists. It is so easy to learn and perform a quick glimpse about the data. The most important feature of this tool is the ease of access. It does not require any prior coding skills or the a deep knowledge of data science. Most of us have been using this tool since a small age. But believe me, this tool sometimes can beat python to perform a small analysis.
Understanding of the client requirements
This is the important part of a Data Scientist’s job. If you are not able to serve your clientele at the end of the day, you are not doing great. Clientele can be anyone like citizens, businesses, banks, NGOs, senior people and even children. Each clientele needs a unique way of presentation. Selecting what represents you and your clients’ interests should be one’s top priority.
Visualization for everyone’s needs
Visualization is an integral part of representing all your work. The companies might demand a detailed report on the analysis but what the real world sees is mostly graphs and pictures. In the 21st century, people do not have time to read a couple of thousand words but they sure try and understand graphs. The type and display of graphs vary according to requirements. If you are generating graphs for kids, they need to be cute and colorful. If a graph is for the banks and companies, they can be complicated comprising of many components or subplots.
The world now has many softwares developed that can generate analysis just by a couple of clicks. These softwares have pre-designed dashboards that generate analysis by selection, drag and drop and a few mouse clicks to ease everyone's access to the field.
The content above is my personal opinion of the field and might differ from person to person. My aim is to provide an insight that I gained after a year in the field. I’d really appreciate a feedback and suggestions from my readers. Please feel free to follow me and share your views:
