data science

Technology for data science students and professionals

Are you looking for an exciting new career offering job stability and security as technology continues to grow? For many, the shifting business landscape can be overwhelming. Luckily, some career fields thrive with change – and data science is one of them. Data science is an exciting career path that utilizes the latest information technology to gather, parse, and synthesize large data pools to provide valuable insights to individuals and entities. As they use new gadgets to help streamline the data management process and visualize their results, data scientists grow with the technology.

This article looks at some of the latest gadgets and technology helping data science students and professionals.

What is data science?

Before we look at what gadgets prove helpful to data science students and professionals, understanding the field is imperative. What exactly is data science, and why is it important?

As the world evolves and businesses strive to better identify and target their audience, the need for information-driven decision-making is increasingly important. Data science exists to help gather, parse, and explain large batches of data. Some data scientists take raw customer data, for example, process the information to ensure that it is relevant and accurate, parse the data to identify trends and any other notable relationships, and then explain the results to their clients (typically business owners or managers).

Once the information has been gathered and examined, the insights gleaned can be used to make intelligent business decisions in several different ways. From more clearly defining potential customers to revising marketing approaches and a whole host of other applications, data science is quickly becoming an indispensable part of today’s business landscape. That is part of the reason the industry is expected to grow rapidly over the coming years, rising from an estimated worth in 2021 of $95.3 billion to a 2026 estimated worth of $322.9 billion with a compound annual growth rate of nearly 30%.

Would you like to learn more about the tools that data science students and professionals use to drive their conclusions? Here are some of the cutting-edge technology and gadgets you should know about.


One of the most important elements of data science is visualization. While garnering insights from data is critical, the work will fall flat if no one other than data scientists understands the conclusions. The data must be broken down in a way that makes sense to professionals in other fields, including executives and stakeholders. One of the most effective ways to do this is to use data visualizations.

D3.js is an open-source JavaScript library that makes creating custom visualizations from a simple web browser easy. More commonly known simply as “D3”, this tool utilizes web standards like CSS, scalable vector graphics, and HTML to create a user experience familiar to most users. This dynamic tool doesn’t take much time to learn and use, with D3’s designers describing it as requiring “minimum effort” to create beautiful, custom data visualizations.

Jupyter Notebook

Before data scientists can generate visualizations to relay the results of their data examinations, they must first collect and edit the data. This often requires collaboration with other professionals, which can sometimes be unwieldy depending on the size of the data set. Sometimes there is simply too much data to share without lengthy explanations. This is where Jupyter Notebook is useful.

Jupyter Notebook is designed to make collaboration among data engineers, data scientists, researchers, mathematicians, and other users easy. They do this by offering an interactive “notebook” experience that allows users to create, share, and edit code with a host of “extras” such as adding comments, software code, computations, rich media representations, and data visualizations all within the digital notebook itself. This information can then be easily shared and edited by other users.

This tool is handy for data science students collaborating on projects and working towards their Online Master’s in Data Science. It can also be invaluable to professional data scientists. Students interested in continuing their education beyond undergrad might find Worcester Polytechnic Institute an excellent higher education choice with a history of excellence in mathematics and science.

Apache Spark

Apache Spark is a data analytics and processing engine with open-source code and the ability to handle an immense amount of data without stuttering. The tool’s developers claim that users can efficiently process “several petabytes” of information at a time, and it does so quickly. Its speed and stable processing ability have led to the platform’s rapid adoption since its initial launch in 2009. In fact, it is one of the biggest open-source communities on par with “big data” technologies.

Whether users are interested in continuous intelligence applications using almost real-time processing or need a solution for transform, extract, and load uses, Apache Spark is a popular tool in the industry.


First released in 1984 by software vendor MathWorks, Matlab is a high-level analytics environment and programming language used by data scientists and conventional engineers to design algorithms, analyze data, and create embedded systems needed for wireless communications, among many other vital uses.

While Matlab isn’t the most widely used language in data science, it supports deep learning and machine learning, predictive modeling, computer vision, and big data analytics in an environment that is well known to science and mathematics professionals. This widespread use makes it especially popular as a collaboration tool, and Matlab also offers speedy data preparation and exploratory data analysis.

Data science is a field currently experiencing sustained and increasing growth. It is an excellent choice for anyone interested in a job that will grow in demand as time passes, especially if they enjoy working through puzzles and drawing conclusions from masses of outside information. If you are interested in pursuing data science as a career choice, keep our tips above in mind, and you’ll be ready in no time!