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Becoming a data scientist has become one of the most sought-after jobs of the 21st century, yet many in the field wonder if data science will become automated. 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.

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Tasks That Will Become Automated

Cloud 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 advantages that machines have in this area is they do not have the same error risk as human, 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, algorithm, champion models and fitness metrics, are also becoming quickly automated.

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.

What Professionals Believe About the Data Science Field

A 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 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.

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