Data Science — Understanding the Concept and Why It Is Important
What does a data scientist actually do? A simple, candy-shop analogy for the four core tasks — cleaning, analysis, statistics, and engineering — that make data science matter.
Updated May 27, 2020
Over the last decade, there has been a massive growth in both data generated and data retained. These data are retained by companies as well as you and me, isn’t it? Sometimes, we call this the “Big Data”.
Nowadays, the term “Data Science” is gaining wide recognition. But what does a data scientist do? Data scientists are the people who make sense out of all the big data and determine what can be done with it in order to increase productivity.
Let’s understand with an example:
Consider you are visiting a candy shop. Generally a person takes those candies that he likes; in contrast, data scientists are the people who will get all the flavors of the candies and analyze them because they really need to know what each one tastes like. In short, the title “Data Scientist” encompasses different flavors of the work. According to me, that is the major difference between a “Data Scientist”, “Statistician”, “Analyst” or an “Engineer”. A data scientist is one who does a little of those tasks done by a statistician, analyst and an engineer.
To be more specific, a data scientist is one who does the following primary tasks:
- Data Cleaning
- Data Analysis
- Statistics
- Engineering
Let’s have a look at each of the tasks in brief:
Data Cleaning
The data coming from different sources may contain a lot of noise, might be unformatted, and might not be useful for generating valuable insights. This task ensures that all the data is well formatted and also conforms to some set of rules and standards.
Data Analysis
In this task, lots of plots of data are made in order to understand the pattern of the data. Through this process, some theories regarding the data behavior are crafted in a way that will be easy to communicate and easy to act on.
Statistics
A data scientist develops different models by understanding the data patterns through data analysis and develops some strategies based on the understood or developed statistics. But the most challenging aspect of this task is that the models or statistics cannot act as a permanent solution to the defined problem. Therefore, a lot of time is dedicated to this task in which a data scientist may need to evaluate and make some changes in the existing models, as well as going back to the data and bringing out new features to help make better models.
Engineering
The above-discussed tasks can just be defined or act as the tip of the iceberg. This is because even if we have state-of-the-art data models for different applications, it doesn’t do anyone much good if the insights are not given to the customers or users, and done consistently. This means building a sort of a data product that can be used by people who are not data scientists. This can be implemented in many forms like chart visualizations, metrics on a dashboard, or an application.
Understanding all the above tasks of a data scientist, in brief, it can also be understood that a long-term life cycle of a data science project may involve going back and re-analyzing the data models if there is always a new source of data coming in and there is a need to incorporate them.
Analyzing such traits and tasks of a data scientist, it can be concluded beyond doubt how great an importance data science and the data scientist may have in the growth of any organization in the era of highest competition and the need of constant improvement in the services of organizations.
