What qualifies as big data

Ten Essential Skills for Any Data Scientist

The need for data science qualifications is increasing and with it the demand for data scientists in companies. The application of data science is a separate field, but it is not restricted to specific industries or business areas. Data scientists can make a big impact in any business in any area.

If you are a prospective data scientist or are interested in it, you know that the appropriate qualification is the first step. In addition to the purely technical training, there are data science skills that go beyond the subject-specific qualification. By using and acquiring such skills, you can stand out from the crowd of applicants and data scientists as this area continues to grow.


Skills of this type require less technical training and no formal certification. However, they form the basis for a consistent application of data science to business problems. Even the most technically competent data scientists today must have the “soft skills” listed below if they want to be successful.

1. Critical thinking

This skill includes the following skills:

  • Objective analysis of questions, hypotheses and results
  • Knowledge of the resources required to solve a problem
  • Assessment of problems from different points of view and perspectives

Critical thinking is an important skill that, in principle, is desirable in any profession. For data scientists, however, it plays a particularly important role. In addition to establishing insights, they must be able to ask appropriate questions and evaluate the results in terms of business operations or initiate the next steps so that they are translated into appropriate action.

It is also important to be able to objectively analyze problems interpreting data before making a judgment. Critical thinking in data science means paying attention to all aspects of a problem, checking the data source, and always staying curious.

2. Effective communication

This skill includes the following skills:

  • Explaining the data-driven insights in business terms
  • Communicate information in a form that highlights the importance of the actions involved
  • Transparent communication of the research process and the assumptions that led to a conclusion

Effective communication is another skill that is in demand everywhere. Whether as a beginner or CEO: The ability to connect to people is a quality that enables you to get things started quickly and easily.

In business life, data scientists need to be competent in analyzing data and be able to explain their findings to both a technical and a non-technical audience in a clear and understandable way. This important trait promotes data literacy across the company and expands the ability of data scientists to make a difference. If data offers a solution to various problems or provides an answer to business questions, every company will rely on data scientists as problem solvers and helpful multipliers who show others how to take things into their own hands.

3. Proactive problem solving

This skill includes the following skills:

  • Identification of business opportunities and understandable explanation of problems and solutions
  • Knowledge of solving problems by identifying existing assumptions and resources
  • Detective instinct to determine the most promising method for obtaining the correct answers

If you are unable or have no passion for solving problems, you are not necessarily suitable as a data scientist. This is exactly what data science is all about. However, an effective problem solver must not only be highly motivated to get to the bottom of a problem, but also be able to solve it. Problem solvers must effortlessly identify difficult problems that are sometimes invisible at first glance, and quickly know how to solve them or which methods are most promising.

4. Curiosity

This skill includes the following skills:

  • Encourage the search for answers
  • In-depth analysis of results and initial assumptions instead of surface treatment
  • Creative thinking with a desire to know more
  • Constant question about the “why”, since one answer alone is usually not enough

A data scientist needs a thirst for knowledge and a high level of motivation to ask and answer questions that arise from the data, but also to answer questions that have not been asked before. Data science is ultimately about uncovering deeper truths. The research never ends for a successful data scientist; on the contrary, he is always looking for answers.

5. Business acumen

This skill includes the following skills:

  • Understand the business operations and its specific needs
  • Knowledge of the business problems that need to be resolved and the reasons for them
  • Transformation of data into results that benefit the company

Data scientists face a double challenge. They don't just have to be knowledgeable about their own field and how to handle data. You also need to know about the company and how it operates. Data competence is one thing. However, data scientists must also have a deep understanding of the company's activities, at least to the extent that it is necessary to solve current problems and to consider how future growth and success can be supported with data.

"Data science is more than just arithmetic: It is about bringing different competencies into solutions for special problems in a certain industry," explains Dr. N. R. Srinivasa Raghavan, Chief Global Data Scientist at Infosys.


Technical skills are skills that typically top the list of job descriptions for data scientists. Much of this is taught in training courses and formal business training. As the analytics and data workforce grows, many organizations are now placing greater emphasis on these skills.

6. Prepare data for effective analysis

This skill includes the following skills:

  • Management, collection, compilation, processing and modeling of data
  • Analysis of large volumes of structured or unstructured data
  • Processing and sharing of data in the optimal format for decision making and problem solving

Data preparation includes data discovery as well as conversion and processing tasks and is intended to provide data for analysis. It is therefore an essential part of the analytics workflow for analysts as well as for data scientists. Regardless of the tool used, data scientists need to know about data preparation tasks and what they mean for their data science workflows. Data preparation tools like Tableau Prep Builder are suitable for users of all skill levels.

For more information, see Data Preparation Best Practices.

7. Use of self-service analytics platforms

This skill includes the following skills:

  • Knowledge of the advantages and requirements of data visualization
  • Basic knowledge of the solutions on the market
  • Knowledge and application of best practices and techniques for creating analyzes
  • Disseminate results for sharing via self-service dashboards or applications

This skill is a non-technical skill as it requires critical thinking and communication skills. With platforms for self-service analytics, the data science results can be applied in practice and the data explored. These platforms also offer the possibility of making the results available to less technically competent people. With a dashboard in a self-service platform, end users have the opportunity to ask their own questions and see the impact on the analysis in real time when the dashboards are updated.

8. Creation of efficient and maintenance-friendly code

This skill includes the following skills:

  • Direct processing of the programs with which data is analyzed, processed and visualized
  • Creation of programs or algorithms for parsing data
  • Collection and preparation of data via APIs

This skill almost goes without saying. Since data scientists work intensively with systems for analyzing and processing data, they must also know the inner workings of these systems. A variety of languages ​​are used in data science. Learn and use the language most relevant to your job or industry and business needs.

9. Appropriate use of mathematics and statistics

This skill includes the following skills:

  • Perform exploratory data analysis and identify key patterns and relationships
  • Consistent use of a statistical way of thinking to extract the signal-to-noise ratio
  • Knowledge of the strengths and limitations of various test models and their suitability for a particular problem

Like coding, mathematics and statistics also play a crucial role in data science. Data scientists deal with mathematical or statistical models. You need to be able to apply and extend them. Good statistical knowledge gives data scientists the opportunity to critically question the value of various data and the type of questions that are to be answered with it. Sometimes problems require the development of new solutions with which analytics techniques and tools "off the shelf" have to be added or changed. Understanding the underlying assumptions and algorithms is essential to using these applications.

10.Use of machine learning and artificial intelligence (AI)

This skill includes the following skills:

  • Assess when the use of machine learning and AI makes sense for the company
  • Training and provision of models for the implementation of high-performance AI solutions
  • Explaining models and forecasting in the language of business users

Neither machine learning nor AI make your job superfluous in most companies. Rather, using them increases the quality of your work as a data scientist and enables you to work faster and more efficiently. Or as a Chief Data Officer recently put it in a nutshell: "In order for the expectations of AI and machine learning to be met, a number of originally human skills are required". So z. For example, the main requirement for AI is whether the right data is available, when this “right data” reflects the wrong things, and how “sufficient” data for AI can be found. Only after these questions have been clarified can a decision be made on the appropriate trained AI model.

Importance of data skills on your resume

In this blog post, part of the “Data Generation” series on the Tableau blog, author Midori Ng offers practical reasons and advice for portraying data skills in resumes for applications. See for yourself and acquire a mix of non-technical and technical data science skills that will guarantee you personal and professional satisfaction and success.

You can also read more about advanced analytics functions and scenarios in the Tableau platform in the Advanced Analytics with Tableau white paper.