Today, data science is all that organizations are busy focusing on. That’s because, over the last decade, there has been so much data generated as well as retained by companies, also known as ‘big data’ that can be used in many innovative ways to make smarter decisions.
The power of big data is evident in multiple industries now. In a healthcare company, big data can actually save and improve lives. By having plentiful data of a patient and the surroundings they stay in, it is possible to construct models that can be trained to anticipate various results of interest. For instance, models can be designed to foresee the progression of a disease that can be incredibly helpful in disease planning as well as management.
The people who are responsible for making sense out of the data available to any organization are known as data scientists. They are the ones who can figure out how and where to use data in the best possible manner. The role of a data scientist is a blend of a statistician, an engineer, and an analyst. No wonder that it is declared as the “sexiest job title of the 21st century” by Harvard Business Review.
Due to recent development in the field of data science, the position of a microsoft power automate vs uipath has begun to seem quite enticing to loads of people who now aspire to attain a data scientist jobs. To cater the need of such people, various institutes are introducing data science certification courses for imparting data science knowledge to those who wish to have a successful career in big data.
Now let us look the tasks carried out by a data scientist.
- Data analysis
- Modeling/statistics
- Engineering/prototyping
The work of a data scientist begins with cleaning the data. As there are large bulks of data available with an enterprise that is not in the proper format. A data scientist makes this data presentable and put it in a format that is easy to use. Now comes the analysis part.
Data analysis
This is the part where a data scientist works with large sets of data that are too huge for a spreadsheet or even a whole computer. By creating plots of the data, a data scientist is able to explain the data in a manner that it becomes extremely easy to act on it. For instance, at Facebook, the data scientists were able to evaluate that having at the minimum ten friend’s guarantees that a user will keep operating the website, hence, there are multiple types of machinery applied by Facebook to help us find friends.
Modeling/statistics
It is during this time that theoretic knowledge crawls into data science. When you reach a state when the data is already clean and understandable, you need to make predictions from that data. This step involves a lot of complications.
Thanks to machine learning, we can avail various strong algorithms to build a model for tackling problems, however, there is no surety whether that model will work best for you. So, data scientists analyze those models and also go back and refer the data in order to come up with unique features to make improvements in the model.
Engineering/prototyping
In this part, a data scientist builds a kind of data product for those who don’t have a detailed learning of data science. This involves many patterns that comprise of a visualization, an application or a metric on a dashboard.
If a data scientist will make a full-fledged application or only a proof of concept will bank on the amount of data available and the final consumers.
