データサイエンティストに英語力が必要?求められる業務やスキルを解説Do Data Scientists Need English Proficiency? Explanation of required tasks and skills.

Data scientists are increasingly working on a global scale and English language skills are now required. Until now, data scientists were active only in Japan and could work only in Japanese, but times are changing.
Surprisingly, however, there are those who are unaware of the fact that English language skills are required of data scientists. In this article, we will explain the reasons why English is required and specific English skills from the basic skills required of data scientists.

English proficiency is required for data scientists, but here are some of the basic skills that are required before that.

General IT Skills

In order to efficiently collect, analyze, and process data, data scientists need general basic IT skills.
First, since most of the work involves programming skills and working with databases, they need to be able to set up and use these development environments. If you cannot set up an editor and debugging environment, you cannot be a data scientist.
In addition, specialized tools are often used to analyze and utilize data. It is also necessary to have the skills to use these tools without difficulty.
In addition, the results of the analysis are often compiled into documents, so it is better to be able to use Word and PowerPoint. It is desirable not only to be able to create documents, but also to be able to create easy-to-read documents that make use of charts and graphs.

Mathematics and Statistics

Data science is supported by mathematics and statistics. Therefore, as a data scientist, you must master these skills. If you cannot do mathematics, you will not be able to perform the various calculations required in data science.
For example, linear algebra and differential and integral calculus are used in model training, and discrete mathematics is important in algorithm evaluation. Probability and statistics are also required for modeling and hypothesis testing. For mathematics, at least the level of Math IIIB studied by high school students is required, and for statistics, a level of difficulty similar to that studied by university students is required.
However, in recent years, with the use of tools, computers are able to do the calculations for you. Therefore, it is no longer necessary to have the skills to be able to perform calculations and come up with “answers” on one's own, as long as one understands the theory.

Programming

Programming skills are needed to process and handle data. Programming skills are also required for the implementation of data analysis, which is not a task that can be handled by humans.
The programming languages used are “R” and “Python,” although there are slight differences depending on the situation. If you want to improve your programming skills, learn these at the core. In particular, Python has many libraries suitable for data science, so you should learn these at the same time. For example, “Nump,” “Pandas,” and “Matplotlib” are examples.
R is also often used in data science and statistical analysis. Although it is currently being replaced by Python, R remains popular in research institutions. By improving your skills in this area as well, you will be more likely to work in a wide range of fields.

Understanding of the industry and business

Industry understanding and practical skills are necessary to collect the data needed for analysis and to determine the perspective of the analysis. For example, you will understand what business models generate revenue and what the day-to-day workflow typically looks like. It is important to understand the industry and keep in mind that the output should take into account the industry and the type of business to be able to assist decision makers.
In other words, no matter how good your data science skills or English language skills are, if the results of your analysis are not useful to management, they will not be valued. It is meaningless to simply improve your math and programming skills and be able to analyze data. A deep understanding of the industry is required in order to be able to guess and understand what kind of data decision makers are looking for and prepare outputs that are suitable for them.

Data scientists are increasingly required to have English language skills as well. The reasons for this are explained below.

Information is increasingly being transmitted from overseas.

Technical information, including that required of data scientists, is increasingly being transmitted from overseas. Information from overseas is often written in English, and it is difficult to read and understand it smoothly if you do not understand English. From the perspective of gathering the latest information in a short period of time, data scientists also need to be proficient in English.
In particular, various theories and ideas are now being created in different countries, and it is necessary to acquire diverse knowledge. The risk of falling short of domestic information alone has increased, and it is imperative to look overseas as well. As a result, English language skills are now required much more than in the past.

Growing Demand for Globally Competent Human Resources

The demand for data scientists is growing, and this is due to the need for human resources who can be active on a global scale. There is a shift to the idea that English is essential for them to be active not only in Japan but also overseas. Since English is a language with more speakers than Japanese, it has come to be thought that “high English proficiency = a wide range of activities”.
The reasons why English proficiency is required can be subdivided into two categories: “working with overseas clients in Japan” and “working for overseas companies or offices. In recent years, the number of overseas clients and vendors has been increasing, and this is the main reason why English proficiency is required. For example, there are companies overseas that specialize in data collection, and English language skills are required to work with these companies. Also, if you work for an overseas company or office, the official language is likely to be English, which you must necessarily be able to handle.

Jobs requiring English are emerging.

With the changing times, jobs that require English are appearing, and English proficiency is necessary to apply for these jobs. The specific English proficiency requirements vary from job to job and are not always quantifiable. For example, some jobs may require a TOEIC score of 750 or higher, while others may state that it is at a business level. It may also state that the candidate must be able to communicate with overseas clients. It is impossible to determine the level of English proficiency required unless it is quantified, but in general, English proficiency is required.
Some jobs require English proficiency, while others give extra points for English proficiency. In the latter case, English proficiency can be an advantage. In other words, having English language skills will make it easier to change jobs and advance to other positions with more favorable conditions. It is a time when you should improve your English skills in anticipation of what is required of you as a condition, because you will not lose anything by having high English skills.
 

This section describes the situations in which data scientists use English and the English language skills required for these situations.

Understanding overseas documents

A typical situation in which English proficiency is required is when understanding overseas documents related to data science. In recent years, a variety of information on data science has been disseminated around the world. Since these are often published in English, it is impossible to collect the necessary information without English language skills.
For example, someone might write and publish a web article on how to efficiently apply algorithms overseas. In order to quickly understand this content, one must have sufficient English language skills to be able to read it without translation tools. Others will disseminate information via social networking sites, and in this case, they will also need to understand colloquial documents. In the past, information was mainly disseminated through books, but now it is necessary to have the ability to deal with a variety of English, including websites and SNS.

Interacting with overseas vendors

There are times when you will communicate with overseas vendors, and at this time, English language skills are essential. At a minimum, you need to be able to communicate in English via email and chat, and ideally, you should also be able to communicate in conversation.
An example of interacting is requesting work from an overseas data collection vendor. Data scientists need to collect large amounts of data for analysis, and this may include data from overseas. If they cannot collect the data themselves or with a domestic vendor, they have no choice but to ask an overseas vendor to do the work. Communication is required as to what data is needed and in what format.
At a minimum, you should be able to explain the data you are seeking in text form. However, if the requirements are complex, you may need to make a presentation for the vendor. In this case, you will need to be able to prepare materials in English, as well as have the speaking and listening skills to explain the content.

Read through overseas studies and other research.

When new algorithms are discovered in overseas studies, they are likely to be published as papers of some kind. If you do not have a good command of English, you will not be able to read and understand the content smoothly, and it will be difficult for you to try out the algorithms yourself. This can make you inferior to data scientists who are proficient in English and proactively incorporate previous research.
When understanding foreign literature, it is important to be familiar with technical terms and formal expressions used in papers. For example, we tend to use words like “paper,” “study,” and “article” to refer to research and articles. Sometimes we refer to research that is not yet known, as in “Much is known about. Different expressions from business English tend to be used and require additional English skills to business English.

This section introduces how data scientists should proceed with their studies to improve their English language skills.

Study Methods to Improve Basic English Proficiency

To improve your basic English skills, start by studying at the junior high or high school level. It is often assumed that data scientists need high business-level English skills, but in fact, they are supported by the level of English skills that students acquire. It is important to learn from the basics, because if you have not mastered the basics, you will not be able to acquire the more challenging English skills.
Although it depends on your current English ability, you should first purchase a reference book at the high school level and look through it to determine if you can understand the content. If you cannot understand half of the content, you should relearn English at the junior high school level. Therefore, you should also purchase a junior high school level reference book to strengthen your foundation. You cannot improve your language skills without a foundation, so learning at a lower level is a shortcut.

Learning Methods to Improve English Skills as a Data Scientist

Data scientist is a position that requires specialized knowledge. Therefore, they are required to improve their English language skills for their respective expertise. In particular, you will need to understand as much as possible of the technical terms related to data science to be able to play an active role. For example, “coursera” introduces many terms related to data science. A possible learning approach would be to be able to understand and explain these in English. All of these are things that you may use as a data scientist, and improving your English language skills will make it easier for you to do your job.
Reference: https://www.coursera.org/resources/data-science-terms

Learning Methods to Deepen English Proficiency Related to the Industry or Sector

Data scientists need to have business skills in the industry they are responsible for. Since it is important to be able to understand this part of the business in English, you should also improve your English skills with business skills in mind.
For example, study to be able to understand all words that are frequently used in the industry in English. If there are many opportunities to be used in writing and other forms of communication, you will need to understand as much as possible to be able to do your job. It is important to keep up with technical terms by searching on websites.
In addition, it is important to learn the latest knowledge of the industry you are analyzing in English. For example, if a trade journal is published in English, read it to collect the latest information. Also, if you are interested in the industry, please contact us,
 

Data scientist is an internationally active position, and English language skills are now required. In the past, this position could be performed only in Japanese, but the situation is changing now. In order to be active in a wide range of environments, you should try to improve your English skills.
The English proficiency required depends on the environment, but surprisingly, it is mainly at the high school level. To this, you should add technical terms related to data science, as well as terms used in your industry or sector. English proficiency is needed not only by data scientists, but by the IT industry as a whole, so we recommend aiming as high as possible.
 

United World also handles job openings for data scientists who can work internationally. If you would like to put your English skills to good use, please feel free to contact us.

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