Becoming a data scientist has become one of the most sought-after careers of the 21st century, but many in the field wonder will data science be automated at some point and no longer require human involvement. This begs the question of whether those working in the field will cause their own demise due to increasing automation. The answer is, and it is a complicated one, yes, but not immediately. To understand how this may happen, one must first understand what tasks are involved in the field and which of those tasks currently performed by data scientists are most likely to become automated in the coming years. You may also like: Top 10 Best Online Data Science Ph.D. Programs
Tasks That Will Become AutomatedCloud Tech News cites a report by Gartner that approximately 40 percent of data science tasks will become automated by 2020. The key here are tasks that can be simplified. Automated and mundane tasks that are frequently repeated will be the first to be automated, which will allow data scientists to concentrate on more complex algorithms. Among the tasks that can be expected to be subject to automation sooner rather than later are data integration and model building. With model building, there are also tools accomplishing part of the process. The advantage that machines have in this area is they do not have the same error risk as humans, making automation a step above current levels. Many pipeline tasks for data engineering, such as normalization, skewness removal, and cleansing and modeling tasks like selections for champion models, algorithms, champion models, and fitness metrics, are also becoming quickly automated. The goals of automation in data science are to increase the amount of data that may be used in a given task, as well as to increase the speed of the process. Many routine or tedious tasks that will probably be automated within the next five years will allow data science to become a more important and far-reaching tool for businesses than ever before. Regarding tasks that will likely become fully automated, data cleansing is likely one of the first. Data cleansing is also known as data cleaning. Inaccurate and corrupt records must be updated or fixed within a data set, and fixing them may include replacing, updating, or deleting the corrupted data. Another task that might become automated is data integration, which is where data from different sources is consolidated. Once consolidated, the data may be used and analyzed. As far as model building in data science is concerned, that facet of the industry may also eventually reach a state of total automation. Model building is sometimes referred to as modeling, and it involves the process of creating a descriptive diagram that explains the relationships between different types of information. Modeling is a task that data scientists may do when designing a new data store or researching a new design. Many experts also expect that the process of data ingestion will become automated in the next few years. Data ingestion is where data from various sources is placed within a storage medium where an organization may use, access, and analyze it. Data ingestion typically occurs inside a database or data warehouse. A data mart or document store may also be used. A final aspect of data science that may eventually become partially or somewhat automated is data visualization. Data visualization involves taking information and presenting it in a visual manner like a graph so that humans can interpret and understand the data. Through data visualization, humans can identify trends and patterns within the data.
Tasks That Won’t Be Automated
Artificial intelligence (AI) still has limitations, so don’t expect more complex tasks to become automated anytime soon. Data wrangling requires human judgment, which is a concept that isn’t available yet in AI. Other tasks where humans are needed are data interpretation and visualization because someone is always needed to interpret and explain results. Even processes such as machine learning, which is rapidly becoming automated, still need human input and interpretation in order to operate properly. Dataversity notes that data scientists will still be needed to maintain and oversee quality standards as automation advances. Data scientists will be needed to review automated output to ensure the validity of results. They may also be required to perform manual reviews of a task before setting automation into motion. Limitations in automation involve primarily qualitative measures and may make complete automation of data science impractical. There is also the likelihood that some tasks may see some advancement through partial automation, which may allow data scientists to wrangle ever-larger data sets. For example, any repeatable or simple task is a candidate for automation, but challenging tasks that may require multiple decisions based on creative input may need to remain in human hands. Another aspect of data science that probably won’t become automated is the very advancement of the profession and its processes. A human will be the one to create the process that allows a task to be automated, and a human may also be the one to invent new tasks that will help the profession reach a more advanced state.
What Professionals Believe About the Data Science FieldA recent study by KD Nuggets indicates that 51 percent of the individuals polled believe that most expert-level tasks in data science and predictive analytics that are currently done by humans will become automated by 2025. Another 25 percent believe that these jobs will be automated within 50 years. Data scientists in Asia were the most convinced that automation will take place soon, as 60 percent believed most of the industry would be automated by 2025.
The Expected Growth of Data ScienceOne of the factors that influences many computer science graduates and others to pursue data science jobs is the expected growth of the profession for the next several years. An article published by the Bureau of Labor Statistics (BLS) suggests that the expected growth of employment for mathematical science occupations is projected to hit 27.9 percent through 2026, which is much faster than the expected growth of all occupations. The BLS believes more than 50,000 new jobs will be created in mathematical science, and some of the fastest-growing occupations in the country will include statisticians, operations research analysts, actuaries, and mathematicians. Each of those professions utilizes big data to perform tasks like building computer models, streamlining company costs, and identifying trends and relationships in purchasing. College graduates who pursue work as statisticians or mathematicians that deal with data science may see earnings that exceed $92,000 per year, according to occupational outlook numbers published by the BLS. The only caveat to those high earnings is that statisticians must generally earn a graduate degree before they may enter the profession. Operations research analysts, meanwhile, may offer future data scientists a less expensive route to high-wage employment with expected incomes of more than $84,000 each year with a requirement of no more than a bachelor’s degree for entry-level employment. A graduate degree can help data scientists with a few years of experience advance their careers or enter executive positions at their companies.
The Overall Impact of Automation in the FutureOver the last century, there have been some notable cases of jobs becoming automated, but there has never been a consensus on the overall likelihood of automation in most industries. Research into the advancement of machines and technology and their impacts on the average working person’s job hasn’t yet shown that every single job will eventually be performed by a machine. An article from the Brookings Institution indicates that one study in 2013 revealed that employees in areas like transportation, logistics, administrative environments, and offices were the most likely jobs to be lost to automation. The study estimated that 47 percent of middle-class jobs in those areas were at high risk of becoming automated. However, a competing study published in 2017 suggested that some tasks would be automated but that it would be unlikely that entire jobs would be eliminated through automation for many middle-class workers. The one area where those studies and others seemed to coalesce was in the assumption that low-skill and low-wage jobs would be eliminated in the future due to automation. The idea that all jobs will eventually experience some type of automation isn’t too far-fetched. However, experts will likely continue to suggest that many jobs will see significant change due to automation. Data science will be one of them, but machines and technology won’t completely eliminate the human component of the field.
Automation May Bring Some BenefitsThe immediate concern with automation is that workers will lose their jobs completely because of artificial intelligence and machines. There is the fear that the loss of entire industries will destabilize the economy because workers will be unable to find other work without engaging in costly retraining. There is also the fear that entry-level jobs will disappear and will make it difficult for low-skill workers to find jobs. However, there are some groups that suggest that automation is a good thing in many circumstances and that it allows companies to produce with higher efficiency and productivity from employees. According to an article from Forbes, new technology is improving company profits by allowing employees to focus on judgment-based work rather than repetitive tasks. As far as data science is concerned, automation has come in the form of artificial intelligence and machine learning, which has allowed data scientists to compile significant amounts of data. The increase in the amount of information gathered has helped data scientists, as well as individuals without data science training, use valuable information. Another positive facet is that greater automation will allow more companies to utilize the results from data science work and research. Automation may help small companies take advantage of data science research without having to hire multiple employees to perform the work. When the company eventually grows into a larger enterprise, there may actually be an increase in overall jobs available for data scientists.
Some Reasons AI Won’t Completely Replace Data ScientistsThere is no doubt that automation will have a dramatic impact on the day-to-day responsibilities of the average data scientist in the next decade, but it’s unlikely that robots and artificial intelligence will completely change the profession. It’s also unlikely that today’s data scientists will be unable to sustain their employment because of drastic technological changes. Updates to the profession will come slowly and over time. Additionally, those changes won’t completely erase the profession from the planet in favor of robot employees. One of the biggest reasons automation won’t eliminate data science jobs is the fact that machine learning systems can only learn what they’re told to learn. Using the incredible amount of data collected in the modern era requires a human component. It’s unlikely that most business insights will come from human insight made with the raw data. It’s likely that for the foreseeable future that artificial intelligence and automation will only be able to collect and structure data with no more than a limited amount of analysis. Automation may uncover trends in data, but putting that information to work in the real world will likely always require human interpretation. Furthermore, applying the data collected to a specific industry, a facet of business, or a marketing idea will probably not be 100 percent possible with only the data offered by a machine. The best that employees can hope for as far as automation of their jobs is that the process allows them to enjoy greater productivity and that their jobs become even more valuable as a result due to the increase in data gathered and used.
Data Science Is a Complex Industry
To the uninitiated, the idea of data science might evoke an image of a lone computer scientist sitting in front of a computer screen, but there are many types of scientists within the overall industry. Data scientists may have one of three backgrounds in mathematics and statistics, software engineering, or data communications, and they may use their knowledge in different ways. For employees who have a background in mathematics and statistics or software engineering, their focus may be on data engineering. For those trained in data communications or who have a background in mathematics and algorithms, the focus may be on data analysis. The data sciences are a diverse area of research, and there is more than one educational path available to become a data scientist. College graduates who eventually become data scientists often earn degrees in areas like mathematics, statistics, and computer science, but there are also opportunities for jobs in data science with degrees in various social sciences and physical sciences. An engineering degree is a common pursuit for data scientists, too. Data science is such a diverse field that candidates for jobs can often come from virtually any scientific background.
Data Scientists Work With an Extraordinary Amount of InformationOne reason automation is such a hot topic in the world of data science is because of the ability of computers to take huge amounts of data and work with it efficiently and accurately. An incredible amount of data is gathered from the internet, and the 4.6 billion people who have access to the internet around the world create over 4.4 million GB of data every single minute of the day, according to numbers published in another article from Forbes. In addition to those 4.4 million GB of data, the world’s citizens also send more than 188 million emails each minute, send more than 18 million texts, and perform almost 4.5 million Google searches. Hundreds of thousands of tweets on Twitter are published every minute, hundreds of thousands of calls are made with Skype every minute, and more than 390,000 apps are downloaded every minute. All that activity represents a goldmine for data scientists. The amount of data produced by the average human on the planet is expected to continue to grow, and the overall number of people with access to the internet is also expected to continue to surge in the coming years. Those incredible increases in data production will soon mean that the only way of harnessing all that information is with the assistance of machines. Interestingly, supercomputers were once the only machines capable of collecting the amount of data produced from some sources, but data scientists and engineers have made some incredible advances in the strength of their algorithms and software programs built for the sole purpose of collecting data. Big data was invented with the help of the supercomputer and has been made even more advanced through programming discoveries.
Today and Future Training for Data ScientistsShould the prospect of data science automation impact the college and career aspirations of someone interested in data science? Since the profession will continue to grow with additional jobs added to the economy over the next decade, there’s no reason to put training on hold or go in a different direction when a career aspiration involves data science. The fact that data science is one of the fastest-growing professions in the country hasn’t changed despite advances in automation. Entering the profession does require a college degree, so it’s not a career choice to be taken lightly, but the healthy earning potential often makes it worth the time spent in college. At a minimum, future data scientists should be prepared to earn a master’s degree, which means earning a bachelor’s degree in four years and adding another one to two years of study in a graduate degree program. Majoring in computer science may be the best route for a future data scientist’s undergraduate program. One of the most common coding languages used by data scientists is Python, and this is a language that will virtually always be included in the curriculum of the average computer science degree program. Other languages used include Java, C/C++, and Perl. It may benefit a future data scientist to find an educational program that features instruction on machine learning and artificial intelligence. Graduate degree programs are available in machine learning and artificial intelligence, but college students can begin learning about these essential topics when they’re still in undergraduate school and can choose electives in AI and machine learning.
Preparing for Data Science AutomationLike many scientific fields, the training to become a data scientist is never fully complete. Degree programs can only offer training that encompasses current practices, theories, and protocols in data science. Anyone who chooses to enter the field as a data scientist will need to update their knowledge with continuous educational pursuits. Not only should data scientists continue to learn through continuing professional education (CPE), but they should also remain aware of trends and information published on the internet. The digital nature of data science and its importance to the internet means it’s not surprising that a great deal of information and leading-edge trends are published online. Further training and information may be gained from resources of the Association for Computing Machinery and the Institute of Electrical and Electronics Engineers Computer Society, as well as the Association of Information Technology Professionals and the Association for Information Science & Technology. To contend with future developments in data science, current and future professionals will need to remain aware of the automated tools that are introduced each year to the profession. There will never be an overnight change in data science automation levels, but scientists who ignore the trends may eventually feel they are at a disadvantage.
Will Machine Learning Eliminate Data Scientists?The idea that machines will eventually replace data scientists and many other science professionals isn’t a new one, and it’s even been a popular topic in major Hollywood films for the past several decades. However, the fear that machine learning will take over a profession like data science is unfounded since machine learning already plays an integral part in the industry. Machine learning refers to the algorithms that process large amounts of data and find patterns. The data may take the form of many things like images, numbers, and words. Anything that can be stored in a digital format can be used by machine learning algorithms and examined for patterns. Just about every major company on the planet that operates on the internet uses machine learning and data science. An example of what data scientists use machine learning for is in the recommendations customers receive when they shop online at a retailer. Machine learning algorithms will take details like a user’s clicks, their purchases, and other online browsing behavior to create lists of recommended products that may interest the user. Although machine learning is a type of artificial intelligence, it’s not something that should bring to mind fictional stories like the 1980s blockbuster “The Terminator” or the early 200s film “AI: Artificial Intelligence.” Machines and their ability to “learn” will never replace humans. The process will only improve the ability of humans to do their work.
Automation Will Result in Some Lost JobsThe price of greater efficiency gained through automation is the loss of some jobs, and data science jobs won’t be immune to this process. However, there are some indications that the number of data science jobs available will actually grow because of automation rather than shrink. In some industries, automation actually leads to greater innovation and improvements that make the profession more valuable over time. For industries like manufacturing, the process of automation, and the replacement of human workers with robots is an ongoing topic of discussion. While the loss of manufacturing jobs in the United States has often been blamed on increased manufacturing capabilities in other countries, automation has actually been one of the main drivers of manufacturing job losses in the United States. And, according to an article from U.S. News & World Report, the number of jobs in manufacturing that will be lost to robots in the next ten years will reach 20 million. Overall, that represents more than 8 percent of the worldwide manufacturing workforce. Many of those manufacturing jobs will be lost in China because that country is currently focusing a significant amount of its efforts on building an automated workforce. People who have careers like insurance underwriters, data entry, and customer service may also see their jobs replaced by automated versions within the next five or ten years, but data science isn’t one that can be included on that list. Data science will continue to become partially automated, but it will never become fully automated, like some jobs in manufacturing. The hard answer about whether data science will become automated is yes but only in regard to certain low-level tasks. As yet, AI tools do not have the wherewithal to replace human judgment, which is needed to advance the field. Automation will help with the workload that data scientists experience, but probably won’t completely take over the field. Answering the question of will data science be automated can certainly help a future data scientist with his or her career goals, but the specifics of what will be automated and what won’t be automated are only the estimates of employment and economic researchers. Related Resources:
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