The Data Science Skills You Need To Succeed

Posted by Develop Admin
June 14, 2021

Data science is one of the hottest career paths right now, and professionals with the right skills are finding jobs in nearly every industry, from healthcare to retail to IT and everything in between. As these jobs can vary between companies, it’s useful to know the key tech skills you’ll need for a career in data science

While using data and analytics to make better business decisions is nothing new, data science as a practice only emerged a little over ten years ago, as the amount of raw data that companies have available began to explode. Data scientists are specially trained to take the vast amount of structured and unstructured data that organizations are collecting and organize it. This organization of data allows companies to spot trends and pull actionable insights much better.

Today’s Data Scientists typically have a background in mathematics, statistics, coding, and now artificial intelligence and machine learning. They’ll use these skills to build complex algorithms and visualizations to make data easier to read and understand. 

There are a host of skills, both technical and “soft” skills, that a Data Scientist needs to succeed:

Data Science Technical Skills

The technical skills, sometimes called “hard skills”, that are crucial for a Data Scientist are deeply rooted in mathematics and analytics. You don’t need a master’s degree, but being familiar with exploratory data analysis and computer science will be a tremendous help.

Statistics

You should have a good understanding of the core elements of statistics, including statistical analysis, probability, and distribution curves. Knowing how and when to apply these elements will be critical to being able to work with any data set.

Calculus and Linear Algebra

As data science is very focused on using various mathematical models to better understand your data set, it’s important to be skilled in calculus and linear algebra.

Predictive Modeling

While data science involves training machines and neural networks to look for trends and patterns, understanding the core concepts will make this much easier. You’ll also need to have deep knowledge of the various tools, techniques, and models to know which is appropriate for your project.

Coding

Many Data Scientists will tell you that being able to code is a necessity. You don’t necessarily need to be an expert, but having a good understanding of Python, R, and SQL will be extremely helpful in building the automation and machine learning needed for advanced data analysis.

Machine Learning and Deep Learning

With the volume of data that is being collected and analyzed today, it’s imperative that you’re able to create and train algorithms to do the heavy lifting. Being skilled with supervised learning, unsupervised learning, and reinforcement learning methods will allow you to build complex analytical models.

Data Wrangling

While you need skills in analyzing and interpreting data, the vast majority of your time is likely to be spent wrangling and preparing data to be input into the various systems. You may or may not have a team of data engineers available to help with this, but it’s still a skill that you need to invest in.

Data Visualization

When it comes to sharing your data, analysis, and insights with others, being able to create advanced visualizations is going to be key. Using data storytelling to showcase and explain your findings will help you communicate with executives and other key stakeholders who may not be as intimate with the data as you. Popular data visualization tools such as Tableau are great to learn and be proficient with.

Data Science Non-Technical Skills

Non-technical skills, also called “soft skills”, can be a bit more elusive, but are just as important to learn. Soft skills will help you translate your work for others, improve your ability to work with others and on teams, and can provide the career growth opportunities you need to advance. 

General Business Skills

It’s always easier to work on a project or task when you know how it will be used when it’s finished. Having a keen business sense will help you as a Data Scientist understand the true needs of your stakeholders, so you can design more helpful algorithms, visualizations, and reports. You don’t need to have the skills to be a CEO, but understanding how and why your CEO or other executive leadership is making decisions can definitely be helpful.

Collaboration

Very seldom do you find a “team” of one Data Scientist. More commonly, you’ll work on a team with other data analysts and data engineers, so knowing how to collaborate and work together is very important.

Leadership and Management Skills

While a Data Scientist will need to be able to work independently, it’s rare that you’ll work alone, on a team of one. Additionally, you’ll likely want to progress your career, and will quickly find yourself in a position where you are leading a group of team members. Understanding how to lead and manage a team of professionals will help you map out a career path and move into leadership roles.

Communication

Similar to the technical skill of data visualization, more general communication skills can make any Data Scientist stand out from the crowd. Being able to clearly communicate your findings – either verbally or in a written presentation or whitepaper will work in your favor every step of the way. If you can’t communicate the data analytics and insights that you’re discovering, then your coworkers and executive leadership won’t be able to use them to make data driven decisions.

Start Learning Today

Don’t be intimidated by the knowledge and skills required for a job in data science. Whether you’re just starting your job search or looking to advance your career, we have high-quality, expert-led data science skills training that’s right for you. 

Foundation Subscription

Perfect for beginners, with a full range of Data Science skills training.

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Data Academy Subscription

Become an expert with workshops and hands-on practice exercises using real world data sets.

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