Data Science: The Future of the World We Know

The world around us is built using the many principles laid out by science. Mathematics, physics, chemistry, geology, biology, and so on. Each of these disciplines has been worked on for centuries and the findings have changed the world as we know it. Physicists have made sense of the world around us, explaining the basic workings of the universe and what makes everything tick. Chemistry has explained the natural combination and mixture of the different building blocks of the world around us. Biology has explained the way natural organisms work, and behave, and what they are made of. The list goes on and on.

When the first computer was invented many decades ago, no one could have predicted it would revolutionize everything. The concept of such vast processing power and the ability to store the information somewhere other than books and in our minds seemed like fiction. 

In 2022, our world is more advanced than it has ever been. We have seen the vast expanse of the universe and the inside of the atom, each in all of its glory. However, there is another discipline that has arisen in recent times – data science. 

Data Science – Explained 

The term data science is defined as the scientific actualization and classification of data. It is, very simply, defined as the process of taking data in its rawest form, cleaning it, organizing it, and presenting it. This sentence does a great injustice to the field because there is so much more that goes into it. There is no single definition for data science because there is so much that goes into it. Think of data science as a term that encompasses many disciplines. A few of these are:

  • Machine learning
  • Artificial intelligence
  • Business Analytics

At the core of it all, data scientists will take the data they are given and extract sense from it. They will take the data, sift through it, extract what they need from it, organize it into a readable format, and then present it to be used. In a world where data is king, having the right data shown in the right way is paramount. 

The Process 

As mentioned above, data science takes raw data and makes sense of it to be used later on. To do this, there are 5 basic steps to be wary of. These are: 


In this phase, the data is collected from different sources. This could be data that already exists or data being actively generated in the field. Thanks to advancements such as the Internet of Things (IoT) and 5G, the process of data generation and transmission is virtually automatic. You can have sensors collecting data 24/7 and barring any outrage, it will be sent back and stored. Moreover, other sources being programmed to automatically send over data can be incorporated into systems to ensure no one has to manually request or deliver the data. This could use a lot of time and we all know that in the world of business, time is money.


Once the data has been generated and received, it needs to be stored. This process is known as data warehousing, data staging, data cleansing, data processing, or data architecture. Think of this as taking a tangled web of wires and separating them to then organize them. 


After storing data, the data scientist needs to get to work. This step is also known as data mining, data modeling, data summarization, classification, or clustering. As a data scientist, you will study this data to determine different patterns, changes, redundancies, and other metrics which will allow you to deduce what this data means and how it can be used further. 


Once you have your processed data, you run it through different processes to generate different outcomes. Predictive analysis, regression, qualitative analysis, and text mining are a few examples. The data is really put through the grinder and this is the final stage before the data is processed. The entire notion of making more data readable occurs in this process. 

As a stakeholder, you want to know how good or bad a business is doing. Field data will make little sense to someone who isn’t an expert so they need it to make sense. After running tests on the data, you get a set of results that are interpreted. For instance, if you own a retail business and want to understand your consumer’s behavior, you will gather data such as the number of customers visiting, which products are selling more, how different facilities affect customer experience, and so on. The findings from this data can be interpreted to show you what stays and what goes. 

This is the gist of data science. Making sense of complex data.


This step is known as data visualization, data reporting, decision-making, or business intelligence. As a data analyst, you take the cleaned, organized, and tested data and present it. This could be in the form of a written report, charts, graphs, and so on. All of the effort made by a data scientist is shown in this stage. 

Read Also: Potential Effects of Blockchain Technology on Organizations

Data Science – Prerequisites 

Before you get into data science, it is important to have the right knowledge. These disciplines are:

Machine Learning

If you wish to make a career in data science, machine learning is the bare minimum. You need to understand how the system works in order for you to use it. Machine learning is considered the backbone of data science. 


Data science makes extensive work of different programming languages notably Python and R. You do not immediately need to be an expert and can learn as you go. These two languages are on the easier end of the spectrum compared to others. As you learn more, you will understand how Python works and how its support for different libraries of data works in tandem with machine learning. 


The data that goes through the wringer in the analysis phase is the different statistics that are collected. You need to understand how these statistics work in order to sift through the data and extract them. To process the data, it is important to know what it means and how to organize it. 


These models refer to the different mathematic models used during the testing phase. Different calculations and tests are done on the data, using different methodologies which yield different results. You need to know how to use certain algorithms to do certain jobs on autopilot. 


The world as we know it has never been more connected. There has never been more data being generated at any given moment and it has never been more important to make sense of it. moving forward, almost all decisions are being made using big data which makes it one of the most exciting fields for the future. 

Data Science is now taught at the university level but there is a plethora of resources available online to at least get you started. If you want to embark on your data science journey, you need to have a strong, stable, and quick internet connection. Luckily, AT&T is here for you and if you want to learn more, click here to find out what they have to offer you!

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