The Scope of Data
Traditionally, we have seen data as something that tells what happened and it has helped us categorize the result of an event or action and evaluate whether we succeeded or not. It was like showing us the history in a detailed way. At the present time, however, since the technology is evolving at an immense rate, the combination of software engineering and statistics has enabled data to not just show us the past, but the future as well. This technique is known as Data Science, and it was introduced to the world after the term “Big Data” was coined.
Data Science is in its infant stage, but the rate at which it is taking over the industries and businesses, Glassdoor and Harvard are not wrong to call it as the best career of the future.
What are the Jobs of Data Scientists?
People who are the experts on dealing with the data and manipulate it in order to solve industrial problems are called Data Scientists. Their job includes the following points:
- Understanding the Problem and Collection of Data: It is said that a problem is half-solved the moment it has been understood properly. For example, if a beverage company is looking to expand the business, Data Scientists have to understand the possible ways of expanding benefits. They have to collect Data about the company’s resources, capital, targeted customers, demographics of people in which new shops have to be opened etc.
- Redefining of Data: Many times the collected Data is not clear, or there are some missing points and disparities. It may seem like the end, but this is the beauty of Data Science. Data Scientists have many tools which they use to predict the missing values and eliminate the anomalies. This process includes integration, cleansing, discovering hidden information etc.
- Transformation of Data: After the Data has been redefined, it’s the time to work on it and figure out some solution. Here, the Data Scientists modify and analyze the data to develop a model. This is actually the most important part of Data Science, and Machine Learning tools such as Python prove to be a great help.
- Displaying and Communicating the Data: Once the model has been constructed, it has to be converted into a form which can be read and understood by others. Most often Data Scientists try to create more than one model so that they can be tested before deploying in industries. Most widely used tools for this purpose are Tableau, R etc.
Industrial Demand for Data Science
Today, the business environment is of setting trends i.e. foreseeing the future and becoming prepared for it, and since the Data Scientists are an expert of doing exactly that, their demand is only going to rise. Big industries such as E-commerce, Social Media, retail etc. are kind of devouring Data Science. But these are not the only ones since Aviation, Healthcare, Sports, Education, Public Administration, and Agriculture etc. have also understood that Data Science is the magic key to the future.