Demand for data science talent is growing, and with it comes a need for more data scientists to fill the ranks. While the application of data science is its own field, it’s not relegated to one industry or line of business. Data scientists can make an impact just about anywhere in any organization. If you’re a burgeoning data scientist or heading down that path, you know that education is the first step. However, outside of the technical curriculum, there are data science skills that will transcend disciplines. Practicing and developing these skills will help separate you from the crowd of job applicants and scientists as the field grows.

Non-Technical Skills

These skills won’t require as much technical training or formal certification, but they’re foundational to the rigorous application of data science to business problems. Even the most technically skilled data scientist needs to have the following soft skills to thrive today.

1. Critical thinking

With this skill, you will:

• Objectively analyze questions, hypotheses, and results
• Understand what resources are critical to solve a problem
• Look at problems from differing views and perspectives

Critical thinking is a valuable skill that easily transfers to any profession. For data scientists, it’s even more important because in addition to finding insights, you need to be able to appropriately frame questions and understand how those results relate to the business or drive next steps that translate into action. It’s also important to objectively analyze problems when dealing with data interpretations before you form an opinion. Critical thinking in the field of data science means that you see all angles of a problem, consider the data source, and constantly stay curious.

2. Effective communication

With this skill, you will:

• Explain what data-driven insights mean in business-relevant terms
• Communicate information in a way that highlights the value of action
• Convey the research process and assumptions that led to a conclusion

Effective communication is another skill that is sought just about everywhere. Whether you’re in an entry-level position or a CEO, connecting with other people is a useful trait that helps you quickly and easily get things done. In business, data scientists need to be proficient at analyzing data, and then must clearly and fluently explain their findings to both technical and non-technical audiences. This critical element helps promote data literacy across an organization and amplifies data scientists’ ability to make an impact. When data offers a solution to various problems or answers business questions, organizations will rely on data scientists to be problem solvers and helpful communicators so that others understand how to take action.

3. Proactive problem solving

With this skill, you will:

• Identify opportunities and explain problems and solutions
• Know how to approach problems by identifying existing assumptions and resources
• Put on your detective’s hat and identify the most effective methods to use to get the right answers

You can’t be a data scientist without the skill or desire to solve problems. That’s precisely what data science is all about. However, being an effective problem solver is as much a desire to dig to the root of an issue as it is knowing how to approach a problem to solve it. Problem solvers easily identify tricky issues that are sometimes hidden, and then they quickly pivot to how they’ll address it and what methods will provide the best answers.

4. Intellectual curiosity

With this skill, you will:

• Drive the search for answers
• Dive deeper than surface results and initial assumptions
• Think creatively with a drive to know more
• Constantly ask “why” — because one answer is usually not enough

A data scientist must have intellectual curiosity and a drive to find and answer questions that the data presents, but also answer questions that were never asked. Data science is about discovering underlying truths and successful scientists will never settle for “just enough,” but stay on the hunt for answers.

5. Business sense

With this skill, you will:

• Understand the business and its special needs
• Know what organizational problems need to be solved and why
• Translate data into results that work for the organization

Data scientists perform double duty: not only must they know about their own field and how to navigate data, but they must know the business and field in which they work. It’s one thing to know your way around data, but data scientists should deeply understand the business—enough to solve current problems and consider how data can support future growth and success. "Data science is more than just number crunching: it is the application of various skills to solve particular problems in an industry," explains Dr. N. R. Srinivasa Raghavan, Chief Global Data Scientist at Infosys.

Technical skills

These are more required skills that you typically see listed closer to the top of job descriptions for data scientists. Many of the areas will be developed and covered in educational courses or formal business trainings. And many organizations are increasingly emphasizing them as their analytics and data staff evolve.

6. Ability to prepare data for effective analysis

With this skill, you will:

• Source, gather, arrange, process, and model data
• Analyze large volumes of structured or unstructured data
• Prepare and present data in the best forms for decision-making and problem-solving

Data preparation is the process of getting data ready for analysis, including data discovery, transformation, and cleaning tasks—and it’s a crucial part of the analytics workflow for analysts and data scientists alike. Regardless of the tool, data scientists need to understand data preparation tasks and how they relate to their data science workflows. Data prep tools like Tableau Prep Builder are user-friendly for all skill levels. Learn more about best practices for data prep.

7. Ability to leverage self-service analytics platforms

With this skill, you will:

• Understand the benefits and challenges of using data visualization
• Basic knowledge of market solutions
• Know and apply best practices and techniques when creating analyses
• Ability to share results through self-service dashboards or applications

This skill falls in line with the non-technical skills, because it relates to critical thinking and communication. Self-service analytics platforms help you surface the results of your data science processes and explore the data, but they also help you share these results with less-technical people. When you create a dashboard in a self-service platform, end users can tune parameters to ask their own questions and evaluate their impact on the analysis in real time as dashboards update.

8. Ability to write efficient and maintainable code

With this skill, you will:

• Deal directly with the programs that analyze, process, and visualize data
• Create programs or algorithms to parse data
• Collect and prepare data through APIs

This skill is almost a given. Since data scientists are knee-deep in systems designed to analyze and process data, they must also understand the systems’ inner workings. There are many different languages used in data science. Learn and apply the languages that are most relevant to your role, industry, and business challenges.

9. Ability to apply math and statistics appropriately

With this skill, you will:

• Perform exploratory data analysis and identify important patterns and relationships
• Apply rigorous statistical thinking to extract signal from noise
• Understand the strengths and limitations of various tests models and why they fit a given problem

Much like coding, math and statistics play a critical part in data science. Data scientists deal with mathematical or statistical models and must be able to apply and expand on them. Having a strong knowledge of statistics enables data scientists to think critically about the value of various data and the types of questions it can or cannot answer. At times, problems require the design of novel solutions, which may merge or modify off-the-shelf analytic techniques and tools. Understanding the underlying assumptions and algorithms is critical in using these applications.

10. Ability to leverage machine learning and artificial intelligence (AI)

With this skill, you will:

• Understand how and when machine learning and AI is appropriate for the business
• Train and deploy models to implement productive AI solutions
• Explain models and predictions in terms useful to the business

Neither machine learning nor AI will replace your role in most organizations. Using them, however, will enhance the value you deliver as a data scientist and help you work better and faster. As one Chief Data Officer recently shared: “In order to realize the promise of AI and machine learning, you’re going to need a number of quintessentially human skills.” As he conveyed, your biggest challenge in AI is knowing if you have the right data, when the ‘right data’ shows the wrong things, and finding ‘good enough’ data for AI before deciding on a trained AI model that will be most useful.

Why data skills should be on your resume

In this blog post, part of the Generation Data series on the Tableau blog, author Midori Ng offers practical reasons and advice for including data skills in job resumes. Check it out and be on your way to mastering a mix of non-technical and technical data science skills that will bring you personal and professional satisfaction and success. Read the whitepaper, Advanced Analytics in Tableau, to also learn about advanced analytics capabilities and scenarios in the Tableau platform.