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With Machine Learning becoming the in thing in businesses today, it has become a concern to many who are a part of the workforce. But can Machine Learning really replace data scientists? According to a claim, more than 40% of the tasks related to the data science will be automated by the end of 2020. But this isn't bad news for the data scientists who are worried if they will be replaced. In fact, with automation becoming the thing for most techniques and analytics, the processes are only going to get better and more advanced, and data scientists will be in a better position to yield more desirable results by automating the processes.
The data science tasks that are likely to go under Machine Learning include:
So if we go back to the question that if data scientists really need to be worried about data processing automation taking over completely, the simple answer would be no. In fact, data processing is a method that requires automation, and human resource go hand in hand in various ways. But before we jump to that, here are some great benefits for enterprises utilizing machine learning:
Naturally, an automated system means a reduction of manual work -, especially for data collection and entry. The data scientists can use that time for performing tasks that add more value to the data processing system, resulting in better productivity. However, to make sure the data processing system is working at its best, data scientists prefer getting data science training to learn the better handling of automated tasks. This does not only further improve productivity but also brings job satisfaction. The employees are now more skilled and capable of bringing meaningful benefits to the company - a win-win situation.
Automating repetitive and lengthy tasks like data collection, data entry and analytics can improve the overall accuracy of the data in the long run.
Handling massive amounts of data in a manual system is not only time consuming but also has includes a strong chance of human error. Automation saves up time from the manual processing and also eliminates the risk of errors to give you more accurate data to work with.
Businesses - despite their size and industry - are constantly striving to reduce cost where they can. Automating data processing tasks that are both monotonous and time-consuming can save the organization more money in the long run. Also, they would require lesser staff to manage the process, which will ultimately translate to bigger savings too.
Data processing is an integral part of business operations as it helps to make informed decisions. The automation process and big data training for data scientists go hand in hand. With the right training, the team is more capable of handling automated tasks and achieve better and bigger benefits.
Data integration is an ideal task to automate. It's a lengthy process of combining data from a variety of resources into a cohesive, unified view. Implementing an automated Machine Learning system for data integration can help a business achieve all the benefits mentioned earlier. However, the automated coding system will require the data scientists to utilize the tool and supervise the coding. Keeping the massive amounts of data that organizations collect, manual interpretation and analysis will further make the process valuable - especially keeping the risk-free environment in mind associated with the machines.
Model building is another detailed process involving data collection, analyzing for patterns, and making calculated predictions based on the study and observations. Trained data scientists can utilize tools for automating the major elements of the model building. Keeping the enhanced sophistication and intelligence in mind, it is best to use ample resources for automating the model building process. This is particularly more important because of the load of data that needs to be analyzed. This gives the data scientist some time to focus on other important initiatives – such as the bandwidth.
While machines can play an important role in carrying out the detailed process of data science analysis, we cannot ignore the importance of people who can break up, understand, interpret, and implement solutions based on the observations and results of the data analysis. By implementing Machine Learning for data analysis, the organization can guarantee more accurate and efficient results, but the machines may not be able to interpret the results of the analysis like a trained data scientist can. There are three most important qualities that the human-factor brings to the table. These include:
Creativity: The data processing task does not end with gathering data, implementing algorithms and review findings. That's, in fact, where the real work begins. Data scientists can understand the implications of business with the data collected. They can collaborate with sales, marketing, and engineering solutions to yield desirable results.
Discernment: Machines can carry out data experiment, but informed business decisions require critical judgment and thinking to factorize parameters and monitoring for a customized solution that meets the specific requirements of the business. A machine may not be able to do that.
Curiosity: That's one attribute personal to human-factor. Only a human can experience curiosity and cross-check multiple solutions to find out the best and the most suitable answer. Only a human has the power to articulate.
In short, every organization needs data scientists to use their judgmental, curious minds to view a certain solution with all the possible outcomes before they work alongside machines. While there's no doubt about the machine's capabilities, but there's no way they can completely replace the human resource in any sector. In fact, the best way to put Machine Learning to best use is to incorporate the benefits of how the data science processing takes place and transform the workflow around the area to make it more efficient and less time-consuming.
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