What is a data scientist? Main job description and how to become one explained.


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In recent years, data has been used in all fields. The job that has been attracting attention as a result is that of data scientist. Data scientists are specialists in the use of data, and their fields of activity are expanding year by year in today's increasingly DX-oriented society.

In this article, we will explain the job description and introduce the attractiveness of data scientist and how to become one. If you are interested in becoming a data scientist by making use of your experience and the skills you have cultivated, this is a must-see.

What is a data scientist?


A data scientist is a highly specialized position that implements algorithms and builds analytical models. They have in-depth knowledge of statistics, machine learning theory, distributed processing, and other technologies.

Data scientists need skills to work with machine learning frameworks and to process a large amount of data quickly. They select an appropriate framework for each situation and use it to build analytical models, or they conduct trial and error to determine how to process data more speedily.

Specifically, you will need to understand and be able to use frameworks such as TensorFlow, Keras, PyTorch, and Chainer, which are often used in deep learning. It is also important for data scientists to understand how technologies such as distributed processing frameworks (Hadoop and Spark) work, which are needed for speedy processing of data, and to be able to use them appropriately.

Attraction of Data Scientists


The appeal of being a data scientist includes the ability to learn highly in-demand skills such as data analysis and to gain an advantage in the job market. The following is a deeper look at the appeal of data scientists.

The ability to acquire much-needed skills such as data analysis

Data scientists prepare and organize data for effective analysis. Data preparation and organization is the process of making data available for analysis and includes tasks such as data discovery, transformation, and cleaning.

Data discovery is the analysis of data to reveal insights that might otherwise be lost, and cleaning is the process of cleaning and organizing the data according to an algorithm that allows machine learning to learn the subject matter of the analysis.

Thus, becoming a data scientist provides the skills necessary to analyze data using machine learning. Machine learning has a wide range of applications, including demand forecasting, image recognition and image processing, natural language processing, and recommendations (one method of presenting product recommendations to similar users).

With such a wide range of applications, it is a great opportunity to learn the much-needed skill of data analysis.

Advantageous in the job market

In recent years, the demand for data scientists has increased in many industries and business categories. If you have the skills and experience that companies are looking for, there are many inquiries regardless of age. In particular, there are many openings for DX-related personnel.

If you are considering a career change as a data scientist, it will be easier to make your job search go smoothly if you are aware of the qualifications that will be advantageous to you. We have picked up a few qualifications that you should acquire in order to make your job search as a data scientist easier.

Information Processing EngineerThe Information Technology Engineer exam, a national certification, is administered by the Information-technology Promotion Agency, Japan (IPA), and certifies IT expertise. It is a valid certification as proof of expertise as a data scientist.
Statistician and Data AnalystStatisticians and data analysts can be obtained after completing courses offered by the Institute of Practical Education (Modern Statistics Practice Course for statisticians and Multivariate Analysis Practice Course for data analysts). There are no set exam qualifications. It is recommended as it provides knowledge of statistics.
Python3 Engineer Certification Basic ExamThe Python3 Engineer Certification Basic Exam tests knowledge of data analysis using Python, a programming language widely used in the field of data science.

Other recommended certifications include the Statistics Certification Level 2, which is officially recognized by the Japan Statistical Society, and the G-Certificate and E-Certification, which are offered by the Japan Deep Learning Association (JDLA) and focus on AI technology.

Main Responsibilities of a Data Scientist


The work of a data scientist is primarily data analysis. However, data analysis covers a wide range of areas. The following describes the main tasks of a data scientist.

Problem identification and goal setting

In order to collect and analyze data, it is necessary to identify issues that need to be solved and clarify goals. Some of the challenges that companies face include forecasting sales of new products, increasing awareness of products and services, improving products, converting their websites, and improving productivity. However, it is not possible to set appropriate goals without identifying the factors behind the issues, rather than just listing them.

To achieve clear goal setting, issues should be subdivided and prioritized. Proper analysis of data will clarify the goals to be achieved. To further clarify the goals, it is also important to deepen the understanding of the company's business, as interviews with management and relevant departments may be conducted.

The purpose of designing KPIs, which are important business indicators, is to visualize the progress of operations, clarify issues, and standardize evaluation criteria to ensure fair evaluation.

Data collection and data processing

There are a number of data related to issues that companies are facing. Even if data is collected and analyzed unnecessarily, it is unlikely that the results will be useful in solving the issues. There is no guarantee that the data needed to achieve goals will always be available, and even if it is, there are often cases where it cannot be used due to restrictions imposed by internal rules, laws, and regulations.

Therefore, it is important to collect data after researching where the data is located and whether it can be used. Furthermore, since it is difficult to analyze the collected data even if it is presented as is, part of the data scientist's job is to extract the necessary parts of the data for the company and process the data.

The collection and processing of data can be facilitated by utilizing BI tools and other tools to speed up the process.

Data analysis

Once data collection and processing are complete, we move on to the phase of analysis. Since we also operate data collected from business system logs, websites, and SNS, it is essential to build an analysis environment.

Specifically, this includes creating programs, unifying the format of the collected data, and building databases for storing the data. For access analysis, which is performed to determine the number of inflows, routes, conversion rates, etc., tools such as Google Analytics can be utilized. Another advantage of using analysis tools is that you can quickly obtain basic statistics such as averages from a large amount of data.

Once a system for operating the collected data has been established, it is possible to analyze the data from various angles.

hypothesis testing and evaluation

Once the data has been analyzed, hypothesis testing and evaluation are conducted.

Hypothesis testing is the process of considering multiple causes of an issue and proving them through data analysis.
For example, when selling a product, we can predict customer response and formulate a hypothesis based on that response.

If the hypothesis is proven to be correct, the next step can be taken. If the results do not meet your expectations, you will have to start over from the very beginning with the hypothesis.

In any case, the results of the analysis will be reported to the relevant parties. It is then necessary to evaluate the analysis results, the analysis model, and the process used to produce the results.

When the expected results are obtained, the applicability of the results to the business is discussed to improve the accuracy of the analysis and to discover if there are other issues that need to be addressed. When the expected results are not obtained, we examine where the problem was in the process and try to apply it to the next time.


Finally, we create a report based on the results of the data analysis, and report and make recommendations to management.

By sharing the results with management, we can reconfirm whether we are achieving the issues and goals we initially identified. This is a job that comes with a sense of responsibility, as any data omissions or errors can affect decision-making.

How to become a data scientist?


There are several ways to become a data scientist. Finally, we will pick four ways to become a data scientist.

Major in data scientist at university

To become a data scientist, you need to have knowledge about data science. Therefore, it is recommended to study at a university where you can learn about data science. In recent years, an increasing number of universities have established data science departments, so you have a relatively wide range of options.

Departments where you can study data science include the Department of Information Engineering, the Department of Information Systems Engineering, and the Department of Information Science. Some universities also offer courses in informatics and statistics, as well as knowledge and skills in programming languages.

Changing careers from engineering

Some people who change careers from systems engineers have experience with programming languages such as Python and R.

Programming languages such as Python and R are also used by data scientists, so if you have experience in these languages, your new employer is likely to expect you to be an immediate asset. It is also easy to make use of experience as a database engineer who is responsible for tasks such as database construction, operation, and maintenance.

Changing jobs from a marketer or analyst

Marketers and analysts have similar jobs to data scientists. Both are professionals who make full use of data in business.

In recent years, marketing operations have generally analyzed big data before formulating business strategies. In companies that do not have data scientists, marketers and planning departments are responsible for this area. Analysts are primarily responsible for analyzing the business conditions and markets of companies in securities firms and other financial companies.

If you gain experience as a marketer or analyst, which are similar in that they handle data, you will acquire the skills to utilize data in business, which you can easily apply in your work as a data scientist. However, if you do not have knowledge of programming languages or engineering, you will need to learn them before changing jobs.

Utilize internal systems after changing jobs

If the company you are changing jobs with has a career change program or other system, you can use it to become a data scientist.

If you take advantage of in-house programs, you can receive subsidies for the cost of acquiring qualifications or training to acquire specialized knowledge that will help you in your work.

Becoming a data scientist requires a wide range of knowledge, including programming. Therefore, it is difficult to cover all the necessary knowledge by self-study. Even if you can acquire the skills, without work experience, the hurdles to changing careers are high.

In summary, the work of data scientists is now in the spotlight!


Data scientist is a job that is gaining more and more attention as the shift to DX continues. If you have knowledge and skills in data science, you will be valued by companies aiming to DX. If you are a certified information processing engineer, statistician, data analyst, or Python3 engineer certification basic exam, you can easily expand your field of activity even further.

If you are considering changing jobs to become a data scientist, we also recommend changing jobs to a foreign company. If you want to succeed in changing jobs at a foreign company, register with United World, which provides support for changing jobs at foreign-affiliated and global companies.

Talk to United World about 
career change.

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