What is Data Science in Simple Words? A Beginner’s Guide

Data science is a vast field, and it would be challenging to explain it. The more in-depth you get, the more perplexing it could become. In this article, we will brush through the critical definition and primarily utilize laymen’s terminologies. It will enable you to understand data science and its relevance in today’s world better.

What is Data Science? — In Simple Words

Our modern world is growing exponentially in the digital universe. There is a countless stream of data in the form of actions, search queries, content, images, photos, and much more. Even if you disconnect yourself from the internet, your smartphones and even the latest machines utilize some data. In simple words, data is the flow of information available for our technology. The devices, smartphones, TVs, PCs, and everything utilize data to work.

The study of this data and gaining expertise to control, modify, hypothesize, predict, or utilize this data are called data science. It is a ‘digital’ counterpart of science like Physics, Chemistry, and Biology. You cannot call it digital science because that would mean the study of technology, engineering. Therefore, Data Science is entirely different. It does not study anything ‘physical.’ It studies the intangible information present in the digital world.

The Significance of Data Science

Modern technology, like Smart devices, Apps, Virtual Reality, even your Gaming Console, utilizes data science to thrive. You might have heard of the terms like Machine Learning, Artificial Intelligence, and Data Analytics. These are all branches of Data Science.

Through data science, you can achieve almost anything in the digital dimension. Hypothetically, if a person could control data, they could be the god in that world. But let us not get ahead of ourselves. Let us look over some roles and examples of where Data Science is used:

Prediction:

Prediction is when the data is utilized to determine the next action or movement. The accuracy prominently relies on the amount of data available. My business analytic software uses it to predict the potential outcome of a business tactic or a marketing campaign. The more information a predicting module has, the better results it could showcase.

The best example of this is your keypad on smartphones. As you type, you get the suggestion for the next word. Similarly, if you type a search query on Google, some relevant questions will appear for you to type in the drop-down. This is a prediction.

Classification:

Recognizing the type of data and grouping is classification. How do you store your files in a particular folder without them getting mixed and malfunctioning? That is classification.

Once again, if you notice your phone, it categorizes images in a particular folder and videos in another. You can sort things on your PC via Name, Images, Size, etc. It is a classification. Your email classifies what spam is and what is not.

Recommendations:

Have you noticed how a shopping website stars showing recommendations of things you might like? The same applies to streaming apps like Netflix. Suddenly they are analyzing your data usage and pattern to determine what you might like.

By studying the data’s behavior or pattern and using predictive measures, data science enables people to receive various recommendations.

Automation:

There is no reason for you to overlook the most integral part of data science. The automation allows you to initiate an automated process that would have been impossible in the past, like how you have a printer that can print the image. Modern advancements have led us to have surgical robots and machines.

These reduce the requirement of human interaction and force. Something as simple as your phone’s alarm ringing in the morning is an example of automation. Anything that does not require human interference directly is automation.

The prominence of Data Science

The above-given branches are a few examples of what Data Science can achieve. If you look at the prominent of Data Science, it has gifted us with some of the most impactful technology and measures:

Machine Learning

Machine learning is a collection of data utilized by a piece of technology. According to the use of that tech, a new string of data is consistently added to grow the database. Over time, machine learning is an integral aspect of Artificial Intelligence. It enables technology like surgical machines to perform their task more efficiently.

Artificial Intelligence

Many people correlate machine learning with artificial intelligence, but it is slightly different. Artificial Intelligence utilizes more strands of data for a comprehensive collection. AI enables technology to learn, predict, and even think using ML and other user experiences.

Data Analytics

Thanks to digital expansion, Data Analytics has become recognized by gigantic enterprises. Today, to succeed in any endeavor, it has become indispensable to use data analytics. Thus, you will see people in every industry utilizing some form of data analytics.

For example, an eCommerce platform might use it to determine user behavior. It will suggest or recommend new items. Alternatively, to reduce the need for a human workforce, AI customer support might answer some common questions. Thus, showcasing a huge promise.

The Big Three of Data Science

Countless job profiles in the data science sector could get confusing. To break through all the fancy titles, the prominent three professions are responsible for overviewing data science to break through all the fancy titles. If you seek any form of expertise or a blooming career for the future, these three professions will never let you down:

Data Analyst

A data analyst profession primarily requires the person to examine and understand the data. They might be responsible for controlling and redirecting the data to the relevant receivers. Often, data analysts are responsible for the precision of prediction, pattern recognition, and recommendations. These are the frontlines of understanding and processing the data.

Data Engineer

A data engineer is often confused with an analyst and scientist. This person oversees making some tweaks and changes in the data. They can use various new modules that they gain access to. It is their job to find a solution to the problem, enhance the data’s proficiency, or anything else. In other words, these people are responsible for modifying and editing the data in any manner.

Data Scientist

The primary difference between a data scientist and an engineer or analyst is that scientists can create new data. Data scientists can hypothesise or test new theories or mechanisms to see if it is efficient. These people are responsible for thinking outside the box and making discoveries or inventions, like scientists in other fields.

Of course, if you look over at the three of these pillars, the higher you aim for, the more qualifications you require.

The Science of Data Science

Are you wondering why it is called ‘Data Science’ and not something like ‘Data Expertise’ or ‘Data Study?’ It is due to the utilization of science, specifically math's and computer language, which have become the backbone of data.

In simple words, programming languages like JAVA, HTML, SQL, Python, and many more are integral to data science. Similarly, driving new algorithms would require you to have a firm grasp of mathematics. If you combine these two aspects, you get data science.

Now you might even have a clearer understanding that data science is closely related to professions like web development, computer science, and so on. You are not wrong! It is an enhanced or improved version that requires more qualifications to become a member of data science.

Understanding Data Science Through Example

If you are still unclear or feel confused, let us explain everything through a simple yet prominent example of data science that many enterprises have consistently used. Let us talk about Business Analytics Software.

Depending on the database library it has access to. A business analytics software can be a robust integration to any business. It can:

• Predict the current revenue and performance of a business to predict the future. You will know where the company is headed.
• Simulate new strategies for business that you plan on implementing. Thus, you will see a glimpse of the future results of any tactics or strategy you might want to introduce.
• Automate processes like updating inventory by easily keeping track of the goods you have in the company and where your other goods were transported. Thus, you will always know how much resources your company has.
• Provide a solution to customers through an AI integrated service that satiates their queries like ‘Where’s my order.’
• Incorporating GPS tracking will automate tracking goods, people, and delivery, wherever the GPS is in use.

Imagine all the above-given processes available at the ease of your device, even smartphone. It drastically reduces human errors or the need to have an excessive workforce. The people responsible for this brilliant software are Data Scientists. Any new updates or changes you get are the responsibility of Data Engineers. Finally, the actual data you have; it is the entire transfer and collection relies on a data analyst.

Bottom Line

The future of data science is bright. It is currently a lesser-known stream or profession, but you can see its exponential growth shortly. People are already asking for professionals in this spectrum in any way possible. If you have the right skills, you can benefit enormously from data science.