データサイエンティストの将来性は?将来性がある理由と必要なスキルを解説What is the Future of Data Scientists? Why the future is promising and the skills you need.

The use of big data is expanding in a wide variety of fields, and the demand for data scientists is increasing year by year. Against this backdrop, many people may be considering a career change to data scientist in the future.

In this article, we will discuss the future of data scientists. The article also introduces the essential skills and characteristics of people who are suited for data scientist.

First, there are three reasons why the future of data scientists is bright
 

• More active use of big data
• AI technology is developing at a remarkable pace
• The Ministry of Economy, Trade and Industry is focusing on training data scientists due to a shortage of data scientists

Let's look at them one by one.

More active use of big data

In recent years, the use of big data for management in companies has become more active. Against this backdrop, the future of data scientists who can handle big data is bright, and the demand for this type of work is increasing and high income is expected.

Recently, various industries are expanding their digitalization efforts using big data to improve operational efficiency and customer service.

According to the "Results of the 2021 Telecommunications Usage Trends Survey" published by the Ministry of Internal Affairs and Communications, 26.5% of companies have introduced or plan to introduce systems or services to collect and analyze digital data by 2021, up from 23.9% in 2019 and 22.2% in 2020. The figure is up from 23.9% in 2019 and 22.2% in 2020.
 

(Reference: Ministry of Internal Affairs and Communications, "Results of the 2021 Telecommunications Usage Trends Survey")

Big data is expected to be used in a wide range of fields in the future, and its utilization is expected to promote the development of new businesses and collaboration among different industries.

Remarkable Development of AI Technology

Advances in AI technology have led to the widespread use of IoT devices and sensors, and the volume of data collected is rapidly increasing. In addition to traditional structured data, the volume of unstructured data such as images, voice, and text continues to grow. These data are becoming increasingly complex and cannot be handled by traditional statistical analysis methods.

AI models such as deep learning are needed to analyze this complex data, and while AI models can analyze data with greater accuracy than traditional methods, their development and operation requires data scientists with expertise in data collection, preprocessing, model building, evaluation, and management. They are indispensable.

Some believe that advances in AI technology will take away the work of data scientists, but AI is only a tool to improve the efficiency and accuracy of data analysis and will never completely replace the work of data scientists.

Selecting appropriate AI models, preparing high quality data that can be trained, and interpreting the results of the analysis to apply to business are tasks that can only be done by humans at this time. While the job description of data scientists may change in the future, the profession itself will not disappear. Rather, it is expected to coexist with AI as a technology that supports humans.

Data Scientists are in short supply.

As digitization progresses, the shortage of human resources in the digital field specializing in AI and data analysis has become a serious problem.

According to the "DX White Paper 2023" published by the Information-technology Promotion Agency, Japan (IPA), 49.6% of companies answered that they have a "significant shortage" of digital human resources needed to advance DX. Including companies that answered that there is a "slight shortage," more than 80% of companies are concerned about the shortage of digital human resources.
 

Reference: Information-technology Promotion Agency, Japan (IPA), "DX White Paper 2023

The reality is that there is a shortage of specialized human resources, including data scientists, who are the drivers of digital transformation.

The Ministry of Economy, Trade and Industry is focusing on

Against the backdrop of the advancement of digital technology and the accompanying increase in demand for digital human resources, the Ministry of Economy, Trade and Industry (METI) is focusing its efforts on the development of digital human resources, including data scientists.

Specifically, METI has created a roadmap as a concrete measure to develop digital human resources and is providing support for educational programs and research institutions. In addition, the Ministry of Education, Culture, Sports, Science and Technology (MEXT)-led "Accreditation System for Education Programs in Mathematics, Data Science, and AI" will begin in 2019, promoting specialized education at universities and technical colleges.

In fact, some universities, such as Shiga University, Yokohama City University, and Waseda University, have established faculties and graduate schools specializing in data science and are making positive efforts to train data scientists.

As a result of these efforts, the number of people aspiring to the data scientist profession is increasing every year. However, the shortage of digital human resources remains a serious issue, and the demand for data scientists is expected to continue to grow.

Here are some answers to common questions about the future of data scientists.

Will AI eliminate data scientist jobs?

In recent years, there has been frequent talk about how AI may replace the work of data scientists who handle large amounts of data. Indeed, collecting, processing, and analyzing data is a relatively easy task for modern AI. The possibility that such technological advances will automate some of these tasks cannot be ruled out.

However, the work of a data scientist is not limited to data aggregation and analysis. They are responsible for a wide range of tasks, from identifying issues to creating analytical models and making business proposals based on the analysis results. In order to perform these tasks, the data scientist must be able to decipher the meaning behind the data and convert it into business value, which is why it is difficult to be completely replaced by AI at this point in time.

Therefore, it is safe to assume that demand for this type of work will not disappear anytime soon.

Will data scientist jobs become more fragmented?

Although the demand for data scientists is increasing year by year, it has been pointed out that the current jobs may disappear in the future due to the increasing fragmentation of the work.

The use of big data has only begun to attract attention in recent years, and because the data scientist position itself has a short history, there have been many cases of people working in situations where the scope of their work has not been defined.

Recently, however, the number of people working in the data science field has been increasing, and with the rise in specialization and the accumulation of experience, work has been subdivided to make it more efficient. If new job titles are created as a result of this segmentation of work, it is possible that the work may differ from the traditional image of a data scientist.

Will people with low skills lose their jobs?

As the number of data scientists who have acquired specialized knowledge at educational institutions increases, the market position of low-skilled data scientists will be in jeopardy.

Although there is currently a shortage of human resources in the data science field, if enough people with sufficient knowledge and skills are developed in the future, the hiring market will change and companies will begin to select people with more advanced skills.

Therefore, even though the occupations are in high demand, the future is not necessarily guaranteed, depending on individual skill levels. In order to remain valuable and sought after in a changing market, it is important to keep updating your skills to keep up with technological advances and market needs.

The knowledge and skills required of a data scientist include the following six
 

• Knowledge of big data
• Knowledge of machine learning
• Knowledge of statistics
• Knowledge of data analysis
• Consulting skills
• Skills in handling tools using R language

We will explain one by one.

Knowledge of Big Data

Data scientists generally work with huge amounts of data, known as "big data," rather than ordinary data. As the name suggests, big data has a very large volume compared to ordinary data, and collecting, storing, and processing this data requires specialized skills.

In particular, knowledge of Hadoop and related open source software such as HBase, Hive, and Pig is often required. Big data technology is evolving quickly and new developments are expected in the future, so it is important to actively gather information to stay up-to-date.

Knowledge of Machine Learning

Machine learning is a technology that allows computers to learn from large amounts of data and build algorithms and models that automatically perform tasks such as classification and prediction. This technology enables the analysis of large amounts of data in a short period of time, and is therefore an indispensable knowledge for data scientists.

In order to apply machine learning to data analysis, knowledge of how to implement it is also necessary. Specifically, it is necessary to understand the characteristics of each method of machine learning, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning, and to be able to use them appropriately depending on the situation.

Knowledge of Statistics

Data scientists must also have knowledge of statistics. Data is always subject to uncertainty, variation, and error, and simply looking at it does not provide useful insights. In order to analyze data with such variability and correctly interpret the results, statistical methods must be used to derive regularities and irregularities.

Specifically, the nature of the data can be clarified by identifying trends and distributions in the data and calculating indices such as mean and variance. It is also possible to find hidden patterns in the data by detecting the presence or absence of singularities and categorizing the data.

Thus, statistics is an essential discipline for all phases of data analysis.

Knowledge of Data Analysis

When conducting data analysis, one generally selects an appropriate analytical model to work with. Therefore, data scientists must also be knowledgeable about statistical processing techniques and data mining methods appropriate for analysis.

Data mining is a technique for finding hidden patterns and trends in large amounts of data to discover new and valuable knowledge.

While there are many different methods for analyzing data, one must accurately determine which method is most appropriate to derive the best solution to a problem. The greater the range of analytical techniques you understand and can apply, the more likely you will be able to select the more appropriate method.

Consulting Skills

The role of a data scientist is to identify and seek solutions to business problems and to find information that can lead to new business opportunities.

Therefore, consulting skills are essential in pursuing the root causes of problems and making appropriate recommendations based on insights gained through data analysis. Consulting skills mainly include logical thinking, problem-solving skills, listening skills, presentation skills, and business understanding.

These skills are difficult to acquire in a short period of time, so it is necessary to learn them through daily work.

Skills in handling tools using the R language

Data scientists frequently use specialized analytical tools in their work to efficiently handle vast amounts of data. During analysis, complex calculations are often required, and these tools can streamline the calculation process.

For example, there are basic analysis methods such as regression analysis that can be done in Excel, but for specialized analysis, the open source software R is often used. In some cases, larger companies have advanced analytical environments such as SPSS.

Data analysis requires a thorough knowledge of these tools and the skills to be able to use them. This skill is essential for smooth operations, as data analysis can be difficult if the tools are not used efficiently.

Since the tools used by each company are different, it is not necessary to master all tools, but it is recommended that you become familiar with the major tools.

There are three main characteristics that make a good data scientist
 

• Good at collecting information and analyzing data
• Not averse to mathematics and statistics
• Good problem-solving skills

Let's look at them one by one.

Good at collecting information and analyzing data

The job of a data scientist is to create value by collecting and analyzing information that is useful for problem solving and decision making. Therefore, being good at collecting and analyzing information is an important aptitude for success in this position.

Based on the results of analysis, the job involves examining how to contribute to the company's profits from a wide range of perspectives, and often spending long hours facing data in order to come up with the most appropriate measures.

Therefore, if you are good at gathering information and analyzing data, you will be able to continue working as a data scientist without difficulty.

Not averse to math and statistics

Knowledge of mathematics and statistics is essential to becoming a data scientist. Knowledge of mathematics such as probability theory, linear algebra, matrices, and differential and integral calculus, as well as skills in applying statistical analysis methods to data, are necessary.

Therefore, individuals who are not averse to mathematics and statistics, or rather have an interest in these fields, are highly qualified to become a data scientist.

Because data scientists are required to deal with data and numbers on a daily basis, this is a demanding position for those who are not good at math and statistics.

Strong problem-solving skills.

The role of a data scientist is to extract valuable information from large volumes of complex data and use it to help companies solve problems.

Therefore, those who have strong problem-solving skills to carry out the entire process of "identifying issues, devising solutions, and actually taking action" will be successful as data scientists.

In this article, we have explained why the future is bright for data scientists, the essential skills for data scientists, and the characteristics of people who are suited for this position.

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