According to the head of the International Monetary Fund (IMF), it’s likely that a recession is on the horizon, leaving many concerned about upskilling and driving business outcomes during an economic downturn.
But while there’s no guarantee that any skill is entirely recession-proof, there is some good news: according to research from Harvard Business Review and Emsi Burning Glass, employers are placing more emphasis on skills rather than degrees when it comes to hiring.
But what skills are highly in-demand? Data from Glassdoor reports many of the most in-demand skills in 2022 are data science related.
Below, we’ll dive deep into 10 data science skills that can help recession-proof your career.
1. Data analysis
If you’ve ever wanted to improve business processes and make strategic decisions for your organization, you’ll need to understand (and most likely perform) data analysis. Data analysis, or extracting insights from clean data, uses tools and statistical techniques and draws from many fields like mathematics and computer science.
Yet surprisingly, many workers lack the capabilities to perform basic analysis–in fact, a recent report by Deloitte indicates there is a shortage of workers with data analytics skills.
“[Extracting] information from data is actually a very powerful position to be in with data being collected in all aspects of society, ranging from marketing to health and even to sports and entertainment."
“Whatever your field of interest is, I can assure you that there is data to make it better,” said Philippe Rigollet, associate professor in the MIT mathematics department and Statistics and Data Science Center and instructor of MIT’s MicroMasters® Program in Statistics and Data Science. “[Extracting] information from data is actually a very powerful position to be in with data being collected in all aspects of society, ranging from marketing to health and even to sports and entertainment."
In Georgia Tech’s MicroMasters® Program in Analytics, you’ll gain an interdisciplinary understanding of the essential fundamentals of analytics, including analysis methods, analytical tools, such as R, Python and SQL, and business applications.
2. Data management
Today’s businesses generate vast amounts of data—but most of it is unstructured, poorly managed, and unused. With data management skills, you can help your organization assess and manage its existing data to make improved business decisions.
3. Machine learning
Machine learning, or systems that self-learn and automate insights from data, is a subset of artificial intelligence. The demand for machine learning is so great that employers consider it a top skill because several industries want to gain insights and develop applications like facial recognition.
Developing machine learning expertise requires specialized knowledge in mathematics, programming, and artificial intelligence. However, you don’t need to master machine learning to understand its importance.
“I think machine learning is relevant to anyone who wants to understand one of the most important technologies changing our world today,” said Thomas Malone, Director of the MIT Center for Collective Intelligence.
Understand the advantages, limitations, and scope of machine learning from a management perspective in this transformative, six-week online program from the MIT Computer Science and Artificial Intelligence Laboratory’s Machine Learning in Business.
4. Python and R
Python and R are the most popular programming languages for data science professionals. While the R language is more specialized, Python is a general-purpose programming language designed for a variety of use cases.
Python is generally considered easier to learn and is usually more broadly applicable. However, if you already have some understanding of programming languages or want to perform more complex data analysis, you may want to consider learning R.
5. Data storytelling
Communicating insights from data to stakeholders is an essential skill for both data and experienced business professionals. Familiarity with tools such as Tableau or Google Charts is a must-have to improve decision-making and answer questions about past performance.
Statistics is a core part of data science. Generally, statistics can be used to describe insights pulled from big data and/or forecast and make predictions. Many core data analysis techniques such as regression, time series, and factor analysis heavily rely on statistics.
“Learning statistical principles gives you a set of tools to tackle all kinds of challenges or you want to extract knowledge from data.”
“Statistics is essential to data science, machine learning, and artificial intelligence,” said Rigollet. “Learning statistical principles gives you a set of tools to tackle all kinds of challenges or you want to extract knowledge from data.”
7. Artificial intelligence
Artificial intelligence has already held a significant impact on today’s global business environment, particularly when it comes to improving business efficiencies and operations.
“AI is the biggest shake-up in business and society of our lifetimes, and we have to know about it, learn about it, and be there to guide its development,” said Kenneth Cukier, Senior Editor at the Economist Said Business School at the University of Oxford.
Learning how to influence others within your organization in conjunction with top-notch data skills can make you a very powerful leader. “Nobody has more power than the person with the data,” said Sam Bishop, Head of Analytics at edX. “Even if you feel like you have imposter syndrome, well-analyzed data is your confidence-booster.”
"Nobody has more power than the person with the data."
SQL is the most popular programming language to communicate with databases and extract data for application development, reporting, and analytics. It is an important skill for data science professionals and business leaders looking to conduct complex calculations and forecasts.
Spreadsheet tools like Excel can perform data manipulations, data processing, and even visualizations. If you don’t have the time to perform complex data analysis, you may want to consider investing your time in learning Excel.
How to become recession proof: 3 ways
If a recession is at the forefront of your concerns, you can always take steps to increase your chances of employability. Read more to learn about three ways you can become recession-proof.
1. Research in-demand industries
Different industries are more in-demand during a recession. These verticals include healthcare, cybersecurity, government, and education.
2. Hone transferable skills
During a recession, honing your transferable skills can make it easier for you to pivot jobs or distinguish yourself in your organization. Transferable skills are generally associated with soft skills such as communication and leadership, but hard skills such as programming and problem solving also fall under this category.
3. Develop your social media presence
Developing a strong presence on social networking sites like LinkedIn can be a great way to build your personal brand, and help you stand out against the crowd.
Learn recession-proof data science skills on edX
The first step to overcoming a recession in your career is to learn in-demand skills. Explore industry-aligned courses and programs on edX today.