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Technical Skills to Have on Your Data Science Career Resume

 If you’re trying to break into the field of data science, there’s some good news: it’s an in-demand field for employers and there are lots of opportunities out there. The bad news is that as a tech-intensive job, employers are going to want to see certain skills demonstrated on your resume. If you’re new to the field, it can be difficult to know what you should learn and practice. Optimizing your resume is important for getting an interview. If you’re just starting out in the field or you’ve been struggling to get hired, these are the technical skills you need to have for your data science resume.  


Image for part: SQL

 Knowing how to use SQL, which stands for Structured Query Language is a must for any data science resume. SQL is a programming language that is necessary for retrieving and updating data in relational databases. It’s required for nearly every job in the field of data science.  


Hadoop is another open-source tool that’s necessary for working with big data. It allows data experts to work with large amounts of data across multiple locations. Like SQL, it’s considered a basic technical skill for data scientists that employers will expect to see on your resume.  


 Data scientists need to have a coding background to be successful in their career. Python is the most common language used in the field of data science. Fortunately, it’s popular and open-source, meaning that there are tons of resources available for learning it.


 Another basic programming language for data scientists, R programming is often used to incorporate statistical models and machine learning during data processing. It’s more often used in academic contexts but can be a requirement for many data science jobs.  

Advanced Mathematics

Most people don’t have a use for the algebra they learned in high school, but data scientists do. It’s important to have a good understanding of advanced mathematics, including linear algebra and multivariable calculus. Advanced math knowledge is necessary for machine learning and other skills needed in data science.  

Statistical Analysis

To get the most value out of the data they’re working with, data scientists need to have an in-depth understanding of statistics. Applied statistics are required for executing data science tasks on a daily basis. You can easily sign up for an applied master of statistics online to learn these skills.

Data Visualization

Data can be extremely complex and being able to visualize it can make a huge difference in understanding. Data scientists need to know how to use data visualization tools for understanding the data in front of them and for sharing insights with others.  

Machine Learning

Having familiarity with machine learning on your data science resume can really give you a competitive edge in the field. Although this skill set is extremely helpful in the field, it’s still not that common for data scientists to have it. You don’t have to be an expert before you start applying for jobs, but learning the basic principles and how machine learning technology can be applied in data science is an ideal way to enhance your resume.  

Soft skills are important too

Even if they won’t hold as much weight on your resume, it’s important to remember that soft skills are important for data scientists as well. It’s important to be able to communicate with executives and others on your team in order to properly leverage corporate data. Yes, most of the job is about crunching numbers and computer science tasks but there’s also a fair amount of working with people involved.  

Look at job postings for inspiration

If your resume feels a little thin, then you might be wondering where to focus your professional development efforts. The skills on this list are a good place to start, but another way to find out what employers are looking for is to go straight to the source: job postings.

Looking at job postings is a great way to figure out what companies are looking for NOW in the field of data science. Technology changes quickly, so taking a look at job postings can help you understand what’s currently important in the field.

Getting into data science can feel daunting. But once you’ve got your first job and continue to build your expertise, things should get a lot easier. All that hard work will pay off when you build a fulfilling career in an in-demand field.  

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