9 Data Science Skills to Have on Your Resume
If you’re pursuing a career in data science, you’ve made a smart choice: according to Glassdoor, ‘data scientist’ has been one of the top two jobs in America since 2016.1 On top of the high earning potential, there are multiple job titles and specializations to explore (e.g. data architect, machine learning engineer, etc.) in several different industries to match your interests.
Because of expanding opportunities and evolution in the field, it can be hard to keep track of exactly what employers are looking for when hiring someone in data science. Here, we’ve compiled nine of the most sought-after skills for the role to make sure you’re set up for success.
For anyone familiar with data science, this skill is a no brainer. However, it’s an important skill to highlight: you can’t always assume it coincides with understanding technical analytics tools. Just because you know how to extract and organize data doesn’t mean you know how to thoughtfully analyze it.
Wading through large quantities of data and emerging with meaningful insights is a skill that every employer needs, especially as data sources continue to grow. For reference, Apache Spark and Hadoop are useful and popular tools in this arena.
2. Mathematics & Statistics
Though you’ll often be working toward high-level learnings, you might have to get deep into the weeds with numbers and spreadsheets, depending on your seniority level. Before you move on to more sophisticated data science topics, make sure you have a firm grasp of foundational mathematical concepts like statistics, probability, linear algebra, and calculus. This will be crucial for when you start to build on those skills with complex algorithms, statistical tests, and functions.
3. Machine Learning
Machine learning is a branch of data analysis that helps to automate the analytical model building process. Essentially, systems collect and learn from data to identify patterns, spot errors, and make recommendations without human intervention.2 For example, banks use machine learning to spot unusual account activity and detect fraud.
Understanding machine learning principles, processes, and outcomes will be expected for any data scientist. If you need an introduction or want to level up your skills, EmergingEd offers courses that will help you become more confident in this area.
4. Computer Science
It’s no surprise that data science and computer science are closely intertwined, especially since both cover a majority of skills on this list. Computer science is also a common college major for people entering the data science field, and shows up frequently in job requirements.
Since computer science can have specific focuses (theoretical or practical), be sure to state if you have experience in one area or both.
5. Natural Language Processing/Programming Languages
Being able to speak the “languages” of big data is a prerequisite for any job in data science. Because these technologies are constantly evolving, you’ll impress your peers if you’re able to learn new languages that are being adopted.
6. Deep Learning
While deep learning is similar to machine learning (and is often called a subset of it), it has different approaches and capabilities that warrants its own distinction. Deep learning combines advances in computing power with special types of neural networks to act and adapt entirely independently. With deep learning, computers can perform human-like tasks such as voice recognition, image identification, and prediction (e.x. Siri, Alexa, and Cortana).5
Deep learning expertise will benefit you for years to come, especially as it relates to AI and customer analytics.
All of your hard, technical skills are important, but complementing them with soft, interpersonal skills is what will set you apart. Not everyone can read and understand data the way that you can, so you have the power to determine its value for a business.
Employers will like to see that you can effectively communicate and work well with others, whether it’s within your team or across departments. This goes hand-in-hand with management or leadership experience, which also conveys the value you’ll add to their team.
8. Data Visualization & Storytelling
Once you have all of your data cleaned and organized, it’s time to show others what you’ve found. Stakeholders don’t have the time (or often, the interest) to try to understand spreadsheets and calculations; they’ll look to you for the high-level insights they can use to make business decisions.
Charts, graphs, and other visuals let you tell the story in a clear, engaging way. Get to know tools such as Power BI, Tableau, and Quire to help craft your presentations.6
While it’s often a sort of buzzword in pop culture, AI is a crucial part of modern data science that represents technology’s migration toward human abilities. Understanding AI is the first step to moving deeper into machine learning and deep learning, which fall under its larger umbrella.
Show your proficiency in AI by mastering one or two of the following popular tools: Microsoft Azure, Google Cloud, Infosys Nia, and IBM Watson.7
Stay Up to Date and In Demand
In today’s job market, the only constant is change. New systems, tools, and languages develop almost daily and have the power to create new jobs and even new industries. Make sure you don’t fall behind by learning new skills or improving old ones through the 100% online data science courses at EmergingEd.
- Retrieved on November 8, 2021 from forbes.com/sites/louiscolumbus/2019/01/23/data-scientist-leads-50-best-jobs-in-america-for-2019-according-to-glassdoor/?sh=13d8d6007474
- Retrieved on November 8, 2021 from sas.com/en_us/insights/analytics/machine-learning.html
- Retrieved on November 8, 2021 from towardsdatascience.com/the-most-in-demand-tech-skills-for-data-scientists-d716d10c191d
- Retrieved on November 8, 2021 from tiobe.com/tiobe-index/
- Retrieved on November 8, 2021 from sas.com/en_us/insights/analytics/deep-learning.html
- Retrieved on November 8, 2021 from techradar.com/best/best-data-visualization-tools