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March 11, 2024 - 5 minutes

From Data to Insights: The Journey of a Data Scientist in the Modern World

Here’s what a data scientist does in today’s world

Ironhack - Changing The Future of Tech Education

Data Science & Machine Learning

You know that the best decisions are backed by data and it’s really quite simple: data helps us fully understand the circumstances surrounding our company and make a decision that’s backed by facts. And in recent years, the amount of data we have has increased exponentially, allowing us to access even more information about what’s working, what’s not, who our audience is, and what they’re looking for. 

As the amount of data has increased, however, so has the need for skilled professionals who know how to make the data usable, turning it into actionable insights, and this is exactly why there’s been a sharp rise in demand for data scientists. Separately from data analysts who take the already processed and sorted data and turn it into steps forward, data scientists are those who actually create the algorithms and systems that are capable of handling and analyzing such large amounts of data. 

Today, the role of data scientists has never been more important; new advancements that allow us to collect so much data are only useful if we can actually turn that data into useful information. In this article, we’ll explore the significance of data science in our modern world and how you can take the first steps towards becoming the world’s next data scientist. 

The Importance of Data Science Today

Let’s make it as clear as possible: our potential lies in being able to understand the vast amounts of data to make better business decisions. In addition, data science: 

  • Helps companies better understand their users: it may seem like a total mystery why some products perform better over others, but data can help you see the clientele that is most likely to buy a certain product, understand why they’re choosing a competitor over you, and provide an altogether better vision of what the user wants and why. 

  • Assists with cost-saving processes: data doesn’t exist just to help explain user behavior; it can also identify steps in the production process that are redundant and help better allocate resources to save time and money, increasing efficiency. 

  • Helps identify new trends and patterns: as the number of users increases and companies have more data to look at, it’s easy for certain things to fall through the cracks and with skilled data scientists that know how to pull out the most important figures, companies can see patterns that were previously ignored. 

  • Identifies potential problems before they occur: to avoid expensive and time-consuming processes when risks or problems arise, data science is tasked with flagging potential issues so that they can be mitigated before becoming a problem. 

As you can see, data scientists play an important role in the lifecycle of a tech project, giving the decision makers more information when it comes to making decisions so that the company is better situated to be successful. And that’s exactly why demand for skilled data scientists has increased in recent years, with more and more companies in practically all sectors looking for data scientists that can help organize and analyze their data to make better decisions. 

But companies aren’t the only ones on the lookout for data scientists; more and more tech professionals are seeing the value in becoming a data scientists, thanks to: 

  • The high salaries of data scientists: as in-demand, skilled professionals who can help companies create the algorithms needed to get the most out of their data, data scientists are well compensated. 

  • The growing demand for skilled data scientists: knowing how to look at data and identify trends isn’t all that data scientists need anymore; with the large amounts of data available, companies need data scientists that are capable of creating the proper machine learning algorithms for their specific data sets. 

  • A wide range of career options: data scientists aren’t just needed by tech companies; as more and more companies realize that they can better serve their users with a deeper understanding of what they want and need, companies in practically every sector will be looking for data scientists. 

  • A changing field: as new artificial intelligence tools and machine learning algorithms are developed, data scientists who are able to stay on top of what’s new are needed.

  • A bright future: we’re at the beginning of all that data can do and the field of data science will only continue to expand and grow, leading to a bright and promising future for those who choose to get into the field. 

The Journey of a Data Scientist 

Your specific tasks as a data scientist will depend heavily on your exact role and experience, but there are generally seven steps of the data science lifestyle that you’ll become quite accustomed to as you advance throughout your career. Take a look below and read carefully through each stage; if you move forward with your aspirations to become a data scientist, this will be a major part of your professional life! 

Understanding the problem at hand 

As you can imagine, the first step as a data scientist is identifying the problem at hand and seeing what exactly you’re there to do. During this stage, you need to ask any questions you have about the users, challenges that the company is facing and what their ultimate goals are to fully understand your role as a data scientist on this project. 

Data mining 

With a clear idea of what the data scientist needs to do, they can gather the necessary data sets and make sure it’s able to be analyzed, fixing any issues that arise such as incorrect information, missing fields, inconsistencies, or more. This step, although possibly tedious, is absolutely essential for the success of the project. 

Exploring the data 

Before the data scientist gets started on creating the algorithm or program that will actually analyze the data, they first will conduct a preliminary, high-level analysis that identifies patterns, trends, and possible hypotheses that help guide what they choose to include as features in the algorithms they create.

Building the data model 

The data scientist knows their purpose and what kind of data they’re working with and can therefore start to build their model with the purpose that they determined while exploring the data. The exact purpose of the model will depend on the company’s current need, but examples would be to identify patterns or classify and sort large amounts of data. 

Testing the model 

Once the model has been built, it needs to be tested and evaluated to ensure that it’s working properly; here, data scientists will look to see if there’s any bias in the model and that it’s accurately providing results. And if the model isn’t completing the purpose for which it was designed, this stage allows the scientists the chance to correct any issues and reposition its goals.

Deploying the model 

After the model has been given the go ahead and tested, it’s time to deploy it and actually put it to use for the company’s purpose, allowing them to use it to better evaluate their data and make better business decisions.

As you can see, data scientists have an incredible future ahead of them and that’s exactly why we’re introducing our next bootcamp; Ironhack’s Data Science and Machine Learning Bootcamp seeks to prepare the next generation of data scientists to enter the workforce and take advantage of the demand for data scientists that currently exists. 

Ready to become a data scientist? We’re waiting for you! 

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