Data science is one of the most appealing fields of the 21st century. A few decades ago, the term Data Science got little attention. People had access to limited data in the past. Everything changed after the internet came. With more people using the internet, they left their footprints on the internet with information like their search preference, likes, and dislikes. Today there are enormous servers to store data. The clearest example of data science is Google, which is user-driven. Drawing information from each user and enhancing their search experience by providing sufficient information to each individual based on their preference.

So how can we define data science? Well, there is no fixed definition but different analogies. In simple words, we can say that,


“Data science is collecting, analyzing, and processing raw data to derive essential insights.”


Data is an essential component in the modern world. With the right tools, we can use the data to our advantage. Data science provides you with the information you need for your business. It helps you to avoid any significant monetary losses.

It assists you in making more reliable and quicker decisions.

Process of Data Science

Data science is essential for corporate businesses. For this purpose, they hire a data specialist to do all the work related to data and present them with meaningful insights.

Data Scientist

A data scientist collects, analyzes, and processes all the raw data and drives meaningful insights.

There is an increased demand for data scientists as more and more people are using the internet. People leave their footprints on the internet. A data scientist collects, analyzes, and processes all the data to meaningful insight from the internet.

A data scientist goes through the following process:


The first step is discovery. It involves the collection of data from all the known internal and external sources. We can extract the data from,

webserver logs

From social media

Census data sets

Data streamed from online sources using APIs


This data can have too much irrelevant information, so it’s better to filter and refine the data.

Model Preparation

Then you prepare a model from all the information you derived from the raw data. Planning is done by using different statistical formulas and visualization tools. Data scientists use Tools like SQL analysis services, R, and SAS/access for this purpose.

Model Building

After preparing the model, the model-building procedure starts. The data scientist tests the data that he collected and analyzed during model preparation.


After model-building, the data scientist presents the filtered data with reports and documents. They place the model into a real-time production environment after thorough testing.


The result helps to decide whether it’s a success or a fail.

Components of data science

Following are the units of data science


The essential part of data science is statistics. Drawing together and analyzing the numerical data in large numbers to derive fruitful insights.


With the help of visualization, you can access vast data in a way that is easy to comprehend and understand.

Machine Learning

With machine learning, you explore and study the algorithms that make predictions about future data. 

Tools for Data Science

A data scientist requires various tools and programming languages to drive the results from structured and unstructured data. We list some of them below:


Apache Spark 

Big ML





SAS is a data science tool specially designed for statistical operations. Organizations of large scale use SAS to analyze data. It uses the SAS programming language for performing statistical modeling. SAS offers tools and statistical libraries that you can use for modeling and organizing data.

Apache Spark

Apache Spark is the commonly used Data Science tool. It is an all-powerful analytics engine particularly planned to handle group processing and Stream Processing. 

Apache Spark includes many APIs that help data scientists to make continuous access to data for Machine Learning, Storage in SQL, etc. It can perform 100 times faster than MapReduce. 

Spark performs much better than other Big Data Platforms in its ability to handle streaming data. This means it can also process real-time data compared to other tools that only process historical data in batches. 

 Its effective union is with Scala programming language based on Java Virtual Machine and is cross-platform in nature.


It is another widely used Data Science Tool. You can process Machine Learning Algorithm with its intractable, cloud-based GUI environment. For industry requirements, it provides standardized software using cloud computing. 

Companies can employ Machine Learning algorithms across various sectors of their company. It also specializes in predictive modeling. 

BigML offers a user-friendly interface using Rest API. You can visualize data efficiently and export visual charts on your mobile or IoT devices.


With D3.js, you can make interactive visualizations on your web browser. With the help of several APIs, you can create dynamic visualization and analysis of data in your browser.

You can make your documents dynamic with animated transitions. Providing updates on the client-side and actively handling the change in data to display visualizations on the browser.


It facilitates matrix functions, algorithmic implementation, and statistical modeling of data. Data scientists use it in many scientific disciplines. 

In Data Science, MATLAB is used to simulate neural networks and dubious logic. You can also create powerful visualizations with the MATLAB graphics library.

Data scientists use it in image and signal processing. MATLAB is a very versatile tool as it tackles all the problems.


Ggplot2 is a data visualization pack for the R programming language. This tool replaces the native graphics of R and uses powerful commands to create illustrious visualizations. Data scientists commonly use it to create visualizations from analyzed data.

ggplot2 is a package of R designed for Data Science. ggplot2 is also much better than other data visualizations in terms of creativity. Data scientists can sketch customized visualizations to fasten in enhanced storytelling. It is one of the most used data science tools. 

Data Science Jobs Roles

The most prominent Data Scientist job titles are:

· Data Scientist

· Data Engineer

· Data Analyst

· Statistician

· Data Architect

· Data Admin

· Business Analyst

· Data/Analytics Manager

Future Of Data Science

With passaging time, the technologies and market are growing at a rapid pace. The technology providers are selling platforms that are doing most of the work that a data scientist had to do. It will not become obsolete, but the job nature of the data scientist will also change for sure depending on technological advancement.