10 Must-Have Skills You Need
to Be a Data Scientist

See also: Creative Thinking

As a data scientist, your main role will be to work closely with your company to identify issues and use data to create solutions for better decision making.

Data scientists are also responsible for building algorithms, designing experiments, and managing and extracting data to support colleagues, customers, and the wider organization.

If you are interested in becoming a data scientist, alongside the credentials and qualifications you will need to undertake to be considered for the role, there are numerous key attributes and skills that you must possess to help you perform your job correctly and efficiently.

Whether it’s knowing how to communicate effectively or how to manage your time better, here are 10 must-have skills that data scientists need.

Effective Communication

Companies on the search for data scientists require individuals who can easily and clearly translate their technical conclusions to non-technical team members, such as those working in sales and marketing.

Data scientists must be able to communicate effectively with people from all different backgrounds, which can help strengthen relationships and improve productivity. As well as speaking a language that the business understands, data scientists must communicate their findings using data storytelling. Using storytelling will help you communicate your findings properly and cohesively so that everyone in the team knows where they stand.


Data scientists cannot work alone, so it’s vital that you can work well as part of a team for projects and tasks to be completed correctly and on time.

Data scientists work with business executives to devise strategies, create better products, launch improved conversion campaigns, and work alongside server and client software developers to improve workflow and create data pipelines. Regardless of the field you’re in, data scientists work with everyone in the company, including the customers. There will be numerous challenges along the way, so it’s important that you take other people’s opinions and thoughts into consideration, even though they may differ from your own.

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Business Acumen

To flourish in a data scientist role, you must have a solid grasp of the industry you are working in and have a clear understanding of what company problems your business is trying to solve.

Regarding data science, you must know how to discern which issues are important to fix for the company to thrive, as well as identify new strategies the business should implement to leverage its data correctly. To do this effectively, data scientists must know the ins and outs of how the company functions.

Intellectual Curiosity

Data scientists need to be passionately curious about their role in order to perform it to the best of their ability.

There is always something new to learn and room for improvement, so regularly updating your knowledge by reading relevant books, online content, and trends in data science, or enrolling on a data science course, can keep you up to date and ensure you use the right practices to perform your role.

Unstructured Data

It’s critical that data scientists know how to work with unstructured data, which is undefined content that doesn’t fit into database tables.

Whether it’s blog posts, social media posts, videos, or customer reviews, they tend to be heavy texts that are lumped together, so it’s important that you can sort these kinds of data quickly. Unstructured data is complex and often referred to as ‘dark analytics’, so having the ability to understand and interpret unstructured data from various platforms is crucial.

Machine Learning and AI

Many data scientists aren’t proficient in machine learning areas and tools, such as reinforcement learning and neural networks.

If you want to stand out from other candidates and open more career windows, you need to have a solid understanding of machine learning techniques like decision trees and supervised machine learning. These kinds of skills will allow you to solve a variety of data science problems.

Data Visualization

The business sector produces a large quantity of data frequently.

This data then needs to be translated into a format that can be easy to follow and understand. Most people understand pictures in forms of graphs and charts, rather than raw data, so to flourish in a data scientist role, you must be able to visualize data and use specialist tools like Tableau and ggplot.

Using these tools will allow you to translate complex results from your tasks to a structure that is easy to grasp. Most people don’t understand p values or serial correlation, so it’s your responsibility to show them in a visual manner what these terms represent.


Data scientists have a wealth of education behind them, with 88% obtaining at least a master’s degree in the field, and 46% possessing a Ph.D.

There is a broad depth of knowledge that you must have to successfully work as a data scientist, so getting the right credentials and qualifications is critical for success. There are numerous options at your disposal to become a data scientist, with many students earning bachelor’s degrees in computer science, statistics, physical sciences, and social sciences. The most common areas of study are math and statistics, engineering, and computer science. Any of these programs will teach you invaluable skills and attributes to help you process and evaluate big data. Whether you want to study business analytics or data science at a school like Suffolk University, you can check out the linked blog from Suffolk Online which may be able to help you make a decision.

Analytical Tools

There is a range of analytical tools that you must have a deep understanding of to help you manage and interpret big data.

Whether it’s Hive, Pig, Spark, SAS, or R, these five are the most popular data analytical tools that are used by data scientists. Gaining certifications can help you further establish your expertise and help you become more confident with these tools and software.


All data scientists must be experts in programming languages such as Java, SQL, and Python. The latter is the most common coding language used in data science roles, which helps you clean, massage, and organize unstructured sets of data.

There is a growing need for data scientists, especially as every company has data that needs to be analyzed and monitored. To flourish in this role, data scientists must possess all the analytical, technical, and communication skills listed above, which can elevate your career and help you work to the best of your ability.

About the Author

Content marketing and inbound marketing expert, Steve Conway is passionate about discovering new tech which will enhance his already well-honed digital marketing skills.