The data science landscape has changed considerably in the last few months. 

The rise of AI tools and user-friendly data platforms has democratized access to data-driven insights, giving even non-experts access to an essential toolkit that enhances efficiency and decision-making. 

As data science becomes more integral to business operations, employers expect data literacy from more of their workforce. A recent study found that nearly 25% of all job postings in the United States require at least some data science skills. And these skills deliver a big payoff; data-literate applicants can expect about 15% more compensation than their untrained counterparts. 

This is especially true for IT professionals. Often data responsibility resides, to some extent, with the IT department. IT is responsible for database maintenance, for contracts with cloud providers, for the protection of sensitive data, for data access permissions for employees, and so on. In one way or another, IT professionals within the company will be in contact with the data science professionals. 

So, why not grow their data science skills? These skills would make any IT professional more competitive in the job market, better integrated within the company’s data science and AI strategy, and highly sought after by management. It is one thing to help data scientists access the data and build their reports; it is another thing to understand their goals and actively collaborate with the data science project execution.

By developing data science skills, IT professionals remain competitive and stay at the forefront of technology, leveraging data-driven insights to optimize operations and support organizational goals. The changes to job listing requirements underscore the importance of continuous independent learning and adaptation, ensuring that IT professionals remain indispensable in an increasingly data-centric world.

What Can IT Pros Learn to Improve Their Data Science Skills?

Candidates looking to enhance their resumes don’t need to return to university; many options are available to develop data science skills online and even get hands-on experience. 

There are three main directions where your data science upskilling can go:

• Data analysts: Learning how to process data and visually present the results to allow domain experts to make the right decision.
• Data scientists: This course of study usually focuses on machine learning algorithms and predictive analytics. This path requires the most math skills.
• Data engineers: This direction will teach candidates how to access and process data and to make them available to the other two types of data professionals. Usually, data engineering profiles are the closest to IT professional profiles.

Of course, each specialization has many different levels of complexity. You can start with a basic data literacy program and then progress to a more advanced program covering more sophisticated data structures. 

A special place in educational paths is reserved for productionization of data science applications. This part of the data science cycle can be tricky and requires a good amount of testing to ensure the productive application delivers correct results. Finally, you can conclude your upskilling path with a specialized course in either math, visualization techniques, best practices, effective communication, project management, or something else relevant to your business.

Notice that with the current availability of many high-quality, low-code tools for data science, coding is not a requirement anymore. Your upskilling can progress quicker and more effectively by adopting a low-code solution and skipping the coding training.

Indeed, data science is more than just coding. It encompasses a diverse skill set that is essential for advanced problem-solving and strategic decision-making. Data literacy empowers professionals to surface the information needed to tackle complex challenges, accurately contextualize and interpret data, and effectively communicate findings to stakeholders.

When integrated with domain-specific expertise, these skills enable IT professionals to harness insights that drive innovation and enhance efficiency within their fields. While data science has a high skill ceiling in terms of technical depth, the advent of AI-based platforms and no-code tools has significantly lowered the barriers to entry. 

Key Competencies to Develop Data Literacy

The three professional paths described above cover slightly different competencies. 

1. Data analysis for data scientists

The heart of data science is the ability to analyze and interpret data. For IT professionals, this goes beyond numbers in a spreadsheet. It involves using advanced machine learning and statistical methods and AI tools to uncover patterns, trends, and correlations within datasets.  

2. Data organization for data engineers 

Fragmented and unorganized data can undermine effective analysis. Understanding how to clean, normalize, structure, protect, version, and archive data is crucial. Proper data structuring ensures data integrity, making it easier to update, query, and maintain over time. 

3. Data visualization for data analysts

Generating actionable insights from data requires storytelling and visualization. Effective data visualization enables the clear communication of trends, patterns, and anomalies, facilitating better decision-making and strategic planning. Data visualization is arguably one of the most important competencies as it empowers teams to present complex data to stakeholders, bridging the gap between data analysis and business objectives. 

How Do You Learn Data Science Skills?

So, how do you become data literate? Even developing a foundational understanding of data science can seem daunting. While practical experience is irreplaceable, many online resources are available to kickstart your journey.

For those seeking to independently enhance their data science expertise, many online platforms offer comprehensive courses and certification opportunities – providing structured learning paths and covering essential topics such as data analysis, machine learning, and data visualization. By completing these courses, you can earn certifications that demonstrate your understanding of key data science concepts.

Gaining hands-on experience in data science is an important next step to validate your new skills. Consider pursuing internships, part-time roles, or volunteer opportunities (such as data-for-good). These experiences allow you to apply your new knowledge to real-world problems, enhancing your data analysis, structuring, and presentation skills.

By leveraging these resources, you can build a solid foundation in data science, giving you the tools you need to excel in data-driven roles. Whatever path you choose, the internet has a wealth of information and structured learning programs to help you develop core competencies and earn a data science certification.

Enhance Your Resume with Data Science Skills 

The demand for data literacy is surging across both industries and job titles. To remain competitive in today’s job market, you must develop a baseline of data science skills. While you don’t need to become an expert data scientist, developing proficiency in gathering, interpreting, organizing, and presenting data is crucial.

Acquiring these skills will enhance your understanding of your domain, help you make more informed business decisions, and position you for higher compensation compared to those without such knowledge. Embrace the opportunity to invest in data science training and see what opportunities follow.