An in-depth look at the skills needed to be a data scientist! Also introduces useful qualifications


  • Industry Information

Data scientists analyze all kinds of data within companies and organizations and use it for management and business purposes. There are a wide range of industries in which they can work, and in recent years, the demand for data scientists is a growing trend as more and more companies handle diverse data.

At the same time, however, the range of skills and information required is broad and highly specialized. Therefore, to become a data scientist, you will need to have a certain level of knowledge.

In this issue, we will introduce the skills required to become a data scientist. We also explain the qualifications that will be useful when changing jobs.

Role of the Data Scientist


A data scientist is a position that utilizes information engineering and statistics to analyze data to help solve business problems and support management decisions. Simply analyzing data is not the only role of a data scientist.

The data scientist has two major roles.

  • Creating an environment for analyzing data.
  • Utilizing big data for business benefit.

The first, building an environment for analyzing data, involves collecting data, organizing and analyzing it, and then transforming the information into a form that is easy to use. It involves designing systems and databases for collecting, analyzing, and storing data, and then maintaining and operating them.

The second, big data utilization, involves helping clients make logical and rational decisions based on the analyzed big data. This judgment and decision-making show an important role in business profits and management strategies.

Skills required for data scientist


What specific skills should a data scientist who handles vast amounts of data have? There are three main skills that should be acquired

  • Business skills
  • IT skills
  • Math skills

Let's take a deep dive into each of these and know what skills you want to acquire.

Business Skills Needed for Data Scientists

In addition to simply analyzing data, you will be supporting the decision-making process of a company or team. Therefore, if you do not have basic business skills, you may not be able to provide answers to what is asked of you. The main business skills required are as follows

Communication skills

In working on data analysis, you will also need to identify current issues and requirements. Issues and requirements will be interviewed from people actually working in the field, so the ability to listen is also necessary.

It is also important to explain the results of the analysis in an easy-to-understand manner when sharing the results. While the analysis itself requires advanced and broad knowledge, it must be understood by management and employees in order to actually make use of the results.

Having the communication skills to explain the results in an easy-to-understand manner without using jargon will enable you to incorporate the results of your analysis and work more smoothly.

Management Skills

Data scientists often work in teams with sales, technical, and other business staff and are often assigned as project leaders. When appointed as a leader, data scientists must not only analyze data, but also have management skills to ensure that the project proceeds smoothly.

Management skills vary widely. For example, you may have to handle everything from managing the schedule of the entire team, to managing the progress of each individual member, to managing the project budget. With management skills, you will be able to successfully organize your team and carry out projects.

Presentation Skills

After analyzing data, you will most likely have to summarize the results in an easy-to-understand report or give a presentation at a meeting to make the analysis results useful for business and management. There is no point in going to the trouble of analyzing data if it is not put to good use. Therefore, presentation skills are also necessary to make the analysis useful for business and management.

Presentation skills tend to focus on explaining things in an easy-to-understand manner, but it is also important to prepare materials. It is necessary to make sure that the materials are free of jargon and easy for anyone to understand, as well as to clarify what you are trying to convey.

IT Skills Needed for Data Scientists

As you work with vast amounts of data, IT skills can also be acquired to make processing more efficient. In particular, programming skills, skills related to algorithms such as machine learning, and skills necessary for system development and design are easily applied to the work of a data scientist.

Programming skills such as Python and SQL

If a single person were to collect, mold, and analyze and process a huge amount of data, it would take a considerable amount of time and effort. Therefore, it is useful to have programming skills to make the work more efficient. In particular, languages used in data science include Python, R, and SQL.

Python is a strong language for statistical processing as well as artificial intelligence. In recent years, there is a wide range of libraries that can be used for data science, so it is a language that should be mastered, including the use of libraries.

R is a language specialized in graphical fields such as statistical analysis, histograms, scatter plots, etc. Although less versatile than Python, it can be used in the fields of time series analysis and bioinformatics. SQL is an important language in data science, essential for data extraction.

Machine Learning Skills

Data scientists may be involved in a wide range of tasks outside of analysis, including the development and implementation of advanced algorithms. Especially in recent years, there has been a high level of interest in artificial intelligence and machine learning, and knowledge in these fields may be required.

Machine learning is a method of data analysis in which a machine automatically predicts results through iterative learning based on an algorithm. Therefore, data scientists need to be able to analyze data while also utilizing machine learning.

System development and design skills

In the course of their work, data scientists may use systems and tools to process, manage, and analyze data. When using systems and tools, they may be involved in design and development. Therefore, they should also acquire skills in system development and design.

If you are a systems engineer and want to become a data scientist, this is not a problem because you have already acquired development and design skills. However, you should consider learning a new programming language so that you can handle as wide a range of systems as possible.

Math Skills Needed for Data Scientists

Data analysis also requires mathematical skills. In particular, statistics, probability, calculus, and matrices are essential, so it is a good idea to review them once if you are unsure if you remember your knowledge.


In order to analyze and correctly consider a large amount of data, it is necessary to learn about statistics. In particular, if you do not know the methods of statistical processing, you will not know which method is appropriate for the actual statistics.

The fundamentals of statistics include estimation of population values, statistical tests, correlation analysis, and regression analysis. For example, regression analysis, which is used to verify causal relationships such as "x is the cause of y," makes it possible to predict things that have not occurred before.

When a company operating a chain of stores plans to open a new store, it is possible to use regression analysis to predict sales when the new store is opened. By using data on the average daily sales of each store and the number of users at the station where the new store is planned to open, the relationship between the two can be easily grasped and applied to the regression equation to forecast sales.

Relying solely on the ambiguity of experience can lead to failed decision-making. Therefore, it is important for data scientists to also make full use of statistics to guide them so that they do not make mistakes in decision making.

Data Analysis

An important aspect of developing data analysis skills is the analytical methods used. There are a great many methods utilized in data analysis, and if you do not choose the right analysis method, you may not be able to get the answers you need.

For example, cluster analysis is a method that can simplify large amounts of data together, but it is divided into hierarchical and non-hierarchical methods, and the choice of which to choose depends on the amount of data. There are also a number of data analysis methods that utilize machine learning and deep learning, which have different characteristics and require an understanding of analysis methods.

Qualifications to help you become a data scientist


If you are considering a career change as a data scientist, it will be easier to appeal to companies to which you apply if you can objectively prove your achievements. However, there may be cases where you do not have experience in data analysis, or where you have work experience but have not yet achieved any concrete results. In such cases, qualifications are useful.

Here are some qualifications that will help you prove your skills as a data scientist.

Statistics Certificate

The Statistics Certification Examination is a certification approved by the Japan Statistical Society that allows you to prove your knowledge and skills in statistics. It tests not only basic knowledge of statistics, but also the ability to analyze using statistics.

There are different types of statistical certification tests, and they are divided into levels from Level 1 to 4. Level 1 is the most difficult, requiring study in specialized areas of university mathematics. Level 1 of the Statistics Proficiency Test requires an applied knowledge of university mathematics, while Level 2 requires a basic knowledge of university mathematics.

Reference: Statistics Certification

Data Analyst

The Data Analyst certification is granted to those who have completed the "Practical Course in Multivariate Analysis," a social distance learning course offered by the Institute for Practical Education, a general incorporated association approved by the Cabinet Office. The course is accredited by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), and focuses on multivariate analysis, mainly regression analysis.

The difficulty level of the certification is equivalent to Level 1 of the Statistics Proficiency Test, which requires an applied knowledge level of university mathematics. The course is designed on the assumption that students have a basic understanding of statistics, so it is a very difficult certification for those who have not been exposed to statistics before.

The curriculum of the course is designed for practical use, so students can immediately apply the skills they have acquired to data analysis after completing the course.

Reference: Practical Course in Multivariate Analysis

Database Specialist Examination

The Database Specialist Examination is one of the national examinations for information processing engineers administered by the Information-technology Promotion Agency, Japan (IPA). There are also the IT Strategist exam, the System Architect exam, and the Project Manager exam, but the Database Specialist exam is considered to be the most difficult of them all. Despite its difficulty, it is recognized within the industry, making it easier to prove your advanced knowledge and skills when seeking a new job.

The scope of the Database Specialist exam includes not only database-centric technical questions, but also basic knowledge of the IT industry in general and more practical questions about database systems. The exam outline and syllabus are distributed, so be sure to prepare properly before taking the exam.

Reference: Database Specialist Examination

Data Scientist Certification Examination

The Data Scientist Certificate (DS Certificate) is a test of basic knowledge and practical skills as a data scientist. In addition to data science skills, the DS certification requires data engineering skills to implement and operate data science, as well as business skills to organize and solve business problems, and exam questions will be asked.

The number of examinees has been increasing every year since its inception in 2021, with approximately 3,050 examinees taking the 4th session (June 2023). The pass rate for the 4th session was approximately 44%.

Since the exam requires advanced statistics and SQL, it is a highly challenging exam for those who have no knowledge at all. In addition, the scope of the questions is broad and includes many calculation questions, so even those with knowledge of the subject matter need to prepare for the exam.

Reference: Data Scientist Certification Examination

G certification and E certification

The G certification and E certification are both tests and qualifications that can evaluate knowledge and skills related to AI technology. In particular, the G certification requires AI application skills, while the E qualification requires development skills. In other words, the G certification is for business, while the E certification is for engineers.

The G certification test has no specific qualifications, so anyone can take the test. The E certification, on the other hand, requires that you first take and complete a course at the JDLA certification program. Therefore, even those who already have knowledge of AI development skills may take several months to acquire them.

Reference: G certification
Reference: E certification

In summary, acquire the skills and qualifications needed to become a data scientist and change careers!


Because data scientists handle vast amounts of data, and because their work is based on the assumption that the data will be used for business and management purposes, it is necessary to have a variety of knowledge in addition to data analysis. If you are considering a career change to become a data scientist, it would be easier to appeal to the market if you have qualifications that can prove your knowledge and skills that can be applied to data analysis.

If you are looking for a job as a data scientist, please consider using UNITED WORLD, where our dedicated career advisors will support you one-on-one in your job search. If you are interested in becoming a data scientist, UNITED WORLD is the place for you.

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