data on computer screen

What is Data Science?

Data is everywhere in modern society. When you run a search on Google, data is created; when you send a message to a friend, data is created; when you track your exercise data using a Bluetooth tracker, data is created.

But, by itself, data is disorganized and has very limited utility. That’s where the field of data science comes in, which is concerned with collecting, storing, and analyzing the data we all create when using technology.

You may have heard people talking about “data” science, but you may have found yourself asking: What exactly is data science? What do data scientists do? Those are questions we’re going to answer in this article.

Why do We Need Data Science?

When the internet was first being developed, most of the data computer scientists worked with was small, structured, and as a result it was easy to analyze. Today, however, as technology has become an increasingly crucial part of all our lives, data has become more complex – there’s more of it to collect, and it is not all ideally structured.

Data collection has become almost a necessity for modern businesses who want to leverage technology within their organizations.

Suppose you are an online retailer. You could use data to gain a firmer understanding of  your average customer. For instance, when customers submit their addresses, you can use that data to see where your products are most popular. If you did not analyze this data, then you would not have been able to figure out this information.

Or suppose you are working for the government. You could use data to run demographic analysis on various regions of the country to learn more about the average income in certain areas, employment levels, and more. Without gathering this data, we’d only be able to guess these statistics.

In short, data science allows businesses, governments, and other organizations to solve problems using data. Instead of making educated guesses about the answers to problems, organizations can use data to solve problems based on evidence.

Now that we know why we need data science, let’s explore what data science is.

What is Data Science?

Data science is an umbrella term for a wide range of concepts related to collecting, processing, and analyzing datasets.

Data scientists are responsible for figuring out how data can be gathered, creating data gathering systems, and will then use the collected data to answer questions.

They also rely heavily on concepts from machine learning, deep learning, and artificial intelligence. These fields are focused on making predictions using algorithms, and all highly involve working with data.

What is the Data Science Process?

There is a concept called the data science lifecycle, which refers to how data scientists approach problems using data. This lifecycle is a standard process that gives data scientists a series of steps to follow in their analysis.

Let’s break down each phase in the data science process to give you a better understanding of what tasks are common in data science work.


The first step of any data science project is to understand the specifications and requirements for the project. A data scientist must ask: What problems do we need to solve? Can data help us solve those problems? What data do we need to collect to solve these problems?

This step is focused on assessing how a project should be executed, and developing what is referred to as an initial hypothesis. This is the question that a data scientist wants to test in their analysis.


After the scope and boundaries of a project have been set out, the data capture stage begins. This involves figuring out what data needs to be gathered – and what data to leave out – then capturing that data. During this stage, a data scientist will outline how data should be gathered, then put the systems in place to gather targeted data.


Here, a data scientist will ensure the gathered data is sufficient for their needs before beginning to clean the data. “Cleaning” data refers to a technique where a data scientist removes errors or outliers from a dataset, and prepares it for processing.


During the processing stage, a data scientist will use the data they have gathered and apply statistical and mathematical models to prove or disprove the initial hypothesis they developed in the discovery stage.

This stage can involve clustering data, using association and classification techniques and  building models to prove the initial hypothesis.


Once the processing stage has been completed, the communication stage begins.

At this stage, a data scientist will evaluate whether they were able to prove or disprove their initial hypothesis. Then, they will compress their findings into a report to be presented to relevant stakeholders involved with the project.

Where is Data Science Used?

As we mentioned earlier in this article, data is everywhere. It powers search engines, social media platforms, medical devices, and more.

Over the years, as technology has become more advanced, our ability to gather data has improved. As a result, the potential applications of data science have grown. To give you a better idea of where data science and data analysis is used, here is a list of a few common applications of data science in the world today:

  • Fraud detection at payment companies
  • Analysis of creditworthiness
  • Sales and revenue forecasting within companies
  • Facial, voice, and text recognition
  • Recommendation engines on platforms like Amazon and Netflix
  • Classification systems to detect spam in emails
  • Analysis systems to predict behavior in the stock market

After reading this list, one thing becomes clear: data has the capacity to solve so many problems.

In addition, note that data science has become a crucial part of many different industries around the world. While you may think that data is only used within the technology industry, businesses in healthcare, finance, and other fields rely on data science.

Let’s explore a few different fields that use data science, and briefly discuss how they use data within their respective fields.


Today, data is used throughout the healthcare industry. For instance, data is gathered by the personal fitness trackers that we use every day. Medical professionals also use data to find ways to better understand diseases, and practice preventative medicine.


Companies like UPS and FedEx rely on data to streamline their operations. For instance, these companies use data to calculate the best delivery routes for drivers based on traffic, weather, and package destination.


In finance, data is used for a wide range of purposes. For instance, data is used to detect and prevent fraudulent activity on payments networks. Data is also used by investors to learn more about the performance of their investments, and to analyze potential investment opportunities. And, data is used to mitigate and quantify risk, which is a core part of all the work done in the finance industry.


If you go onto Spotify, you’ll notice that there are “recommendations” on your dashboard that know just what kind of music you like. Netflix has a similar engine, which tells you what types of movies and TV shows you may want to watch.

These companies rely on data to understand what types of genre you like, so they can recommend similar content.

What Does a Data Scientist Do?

Data scientists collect and report on data gathered by an organization, and share their findings to relevant stakeholders within an organization. In other words, data scientists find solutions to problems with data, and share what they have discovered with those who can implement a solution to that problem.

Data scientists have a strong background in mathematics, computer science, and industry knowledge. These skills are all essential because data science crosses many different fields, and involves a high degree of statistical thinking and programming.

A data scientist will typically identify data to track, process that data, and create visualizations and reports based on discoveries. A data scientist may spend days, weeks, or even months going through a dataset with a team of other data analysts and scientists, in order to prove or disprove a hypothesis.

The typical responsibilities for a data scientist include:

  • Identifying data sources and creating systems to collect data from those sources using their programming skills
  • Building models and algorithms to analyze a dataset
  • Collaborating with design, engineering, and product teams to implement changes based on the result of a data investigation
  • Present data using data visualization
  • Analyse large data sets to identify patterns and common themes

Data scientists are not the only people who work in the data field. There’s also data analysts, who are more concerned with analyzing existing data sets, and data engineers, who are focused on building the systems necessary to collect data for a project.


Over the last few years, data science has become an essential field in a wide range of organizations.

As technology has become more advanced, businesses and governments have spent more time analyzing how data can be used to make their organizations more efficient. As a result, organizations in industries ranging from healthcare to finance have started to make more use of data within their day-to-day operations.

Data scientists, data analysts, and data engineers are all workers responsible for collecting, analyzing, and evaluating the data that we all generate on a daily basis. These workers take an initial hypothesis, and aim to prove or disprove it using data.

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