Back in 2016, Glassdoor declared that being a Data Scientist was the best job in America.

Four years later, the world of big data has evolved rapidly, and the role of Data Scientist is still a top job. It’s currently ranked as the number three job for 2020 by Glassdoor, and it’s only surpassed by Java Developer and Front End Engineer.

What is it about being a Data Scientist that makes it so appealing, and what skills does someone looking to get into this field need to have? 

Let’s take a look.

What’s a Data Scientist?

A Data Scientist is responsible for compiling and analyzing large data sets — both structured and unstructured. These roles combine math, statistics, and computer science skills to make sense of big data and then  use the information to create business solutions. 

Data Scientists gather, process, model, and then interpret data using everything from technology to industry trends in order to develop actionable plans. Plus, they ensure the data is properly cleaned and validated, and that it is accurate and complete.

8 Must-Have Skills for Data Scientists

Like most careers, the more advanced your position, the greater suite of skills you’ll need to be successful. However, when looking at becoming a Data Scientist, there are certain skills you’ll need to be proficient in regardless of your role. 

#1. Math and Statistics

Any good Data Scientist is going to have a strong foundation built on both math and statistics. Any business, especially those that are data-driven, will expect a Data Scientist to understand the different approaches to statistics — including maximum likelihood estimators, distributors, and statistical tests — in order to help make recommendations and decisions. Calculus and linear algebra are both key as they’re both tied to machine learning algorithms. 

#2. Analytics and Modeling

Data is only as good as the people performing the analytics and modeling on it, so a skilled Data Scientist is expected to have high proficiency in this area. Based on a foundation of both critical thinking and communication, a Data Scientist should be able to analyze data, run tests, and create models to gather new insights and predict possible outcomes. 

#3. Machine Learning Methods

While having expert level knowledge in this area isn’t always necessary, a level of familiarity will be expected. Decision trees, logistic regression, and more are key elements that machine learning enables and potential employers will be looking for these skills.

#4. Programming

To move from the theoretical into creating practical applications, a Data Scientist needs strong programming skills. Most businesses will expect you to know both Python and R, as well as other programming languages. Object-oriented programming, basic syntax, and functions, flow control statements as well as libraries and documentation all fall under this umbrella. 

#5. Data Visualization

Data visualization is a key component of being a Data Scientist as you need to be able to effectively communicate key messaging and get buy in for proposed solutions. Understanding how to break down complex data into smaller, digestible pieces as well as using a variety of visual aids (charts, graphs, and more) is one skill  any Data Scientist will need to be proficient in order to advance career-wise. Check out our Creating Data Visualizations with Tableau post to learn more about Tableau and why data visualization is so important.

#6. Intellectual Curiosity

At the heart of the data science role is a deep curiosity to solve problems and find solutions — especially ones that require some out of the box thinking. Data on its own doesn’t mean a whole lot, so a great Data Scientist is fuelled by a desire to understand more about what the data is telling them, and how that information can be used on a broader scale. 

#7. Communication

Data doesn’t communicate without someone manipulating it to be able to do so, which means an effective Data Scientist needs to have strong communication skills. Whether it’s disseminating to your team what steps you want to follow to get from A to B with the data, or giving a presentation to business leadership, communication can make all the difference in the outcome of a project. 

#8. Business Acumen

For a Data Scientist to effectively use data in a way that’s meaningful to their employer, there’s a level of business acumen that’s required. You need to fully understand the key objectives and goals of the business and how it impacts the work you’re doing. Also, you also have to be able to create solutions that meet those goals in a way that’s cost-effective, easy-to-implement, and ensures broad adoption.