Table of Contents
As we have learned about data science and its various applications in our previous articles(Click here if you have not read any of them). Now let us see how data science can be automated, We will briefly discuss the impacts of data science automation on salary and the number of jobs also we will discuss various tools used by a data scientist for automation. As we have seen data science is one of the sexiest jobs in the 21st-century let’s see will it be able to cope up with the rising industrial demand for data science automation?
What Is Data Science Automation?
As we have discussed in previous articles that data science is the process of extracting useful insights from the data provided, This process includes many steps like a collection of data filtering of data, etc.
In data science automation any or all of the steps will be automated, hence reducing the workload of the data science engineer. But in the future, if Data science could be fully automated will it affect the data engineers’ job? This is a highly debated topic and there is a lot of conflict on whether or not Data Science can be automated.
5 Data Science Automation Tools To Boost Your Data Science Projects
There are a large number of companies coming up with tools and products for the data science domain. Here, we shall look at some of these automation tools that one data science professional can use.
Auto weka was initially released in 2013, from its inception it primarily focused on helping nonexperienced people having the less technical knowledge to select the best suitable learning model and hyperparameters for their application.
Darwin is an automated model building tool that allows its users to go from data to the model in significantly less time than traditional methods. Also, it enables rapid prototyping of scenarios and productive extraction of insights.
This amazing tool is developed by a company called SparkCognition.Talking about how this tool works, the tool uses a patented approach based on neuroevolution that custom builds model architectures to ensure the best fit for the problem at hand.
3. DataRobot Automated Machine Learning:
DataRobot is an advanced Enterprise AI-enabled data science automation tool. The platform incorporates knowledge, experience, and best practices of some of the world’s leading data scientists.
DataRobot’s Automated Machine Learning platform is equipped with different types of regression techniques, ranging from the simplest to complicated statistical classic regression models. This tool can be used to solve both complex and simple problems, which makes it appealing to many data scientists.
When it comes to Data science automation, H2O has emerged as a leader. H20.ai can be used both as a data science automation tool and a machine learning automation tool. It is highly scalable and almost supports all the widely used statistical & machine learning algorithms after all it is an open-source platform.
One of the best things about this platform is that it has an industry-leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models.
dotData primarily focuses on feature engineering which is considered to be one of the most important, most time-consuming, and challenging for data science professionals. Simply put, the company is solely focused on democratizing and automating the entire data science workflow.
Data Science Automation: A Boon Or Bane To Your Data Science Career
As we have already discussed that data science and other related fields are highly rewarding some people may have some serious thoughts on how data science automation affects it? With even knowledge-based jobs like lawyers and accountants being automated, will Data Scientists prove to be an exception? 51% of people who voted for a poll conducted by kdnuggets.com says that within 10 years the data science works will be fully automated.
As of now all we can say is maybe data cleaning will be automated in the near future but coming up with the questions that need to be asked, identifying the particular data needed to answer those questions, analyzing that data and creating models, and making the report explaining everything done isn’t going to be automated for a long time. By that time when it gets automated petty much, every industry could be automated.
Some aspects of data science are obviously more difficult to automate than others which is why “data science” can no more be fully automated than can be the scientific method applied to any other domain. Human involvement, for the foreseeable future, is paramount, not only for overseeing and correcting courses for any level of automation but also to kick off searches for insight.
We may be able to automate exploratory investigations of what questions we should be looking to potentially apply the data science process to in the hopes of answering and even have this phase augmented by facts and figures, but the human element will need to make nuanced decisions on which courses of action are worthy of pursuit.
What Are The Benefits Of Data Science Automation?
We have discussed that your data science job is less vulnerable to automation, there is very little chance that your job will be taken up by a robot in the near future. But we have not discussed the application of data science automation.
One of the major sectors that benefit from data science automation will be the manufacturing industry. In order to understand its benefits, we need to know the applications of data science in manufacturing industries. Next, let’s discuss the application of data science in industrial automation, and later we will discuss the impact of data science automation in manufacturing industries.
Interesting Application Of Data Science In Industrial Automation
The manufacturing industry is undergoing a huge transformation supported by today’s digital age that requires the greater ability of the customers, business partners, and suppliers. The increasing scale and speed can be challenging for manufacturers, and this is where data science comes in. At least one-fifth of the largest manufacturers will rely on embedded intelligence built on cognitive data applications like Machine Learning and AI.
Now let us discuss some of the important applications of data science in industrial automation.
Predictive Analytics or Real-time Data of Performance and Quality
Data science can be applied to industries to closely and continuously monitor machine performances, downtimes, and products. The ability to generate a quicker response to issues has a direct impact on productivity. This will, in turn, help manufacturers discover new methods and ways to approach quality improvement and cost management.
Preventive Maintenance and Fault Prediction
The data used for real-time monitoring can be further analyzed to prevent machine failure and improve asset management. Data scientists make use of the knowledge of the machine and take note of the reasons why it may fail in order to make these predictions. After the analysis, they will come up with the deductions and reasons for the failure and will try to avoid them in the future.
Determining the cost of the product is the most vital part of any manufacturing process. It should be affordable for the targeted customers at the same time it has to cope up with the costs of manufacturing and other expenses like shipping, research, and advertising. So providing the product at a reasonable price for the customer is very important for any products.
Data science uses tools for aggregation and analysis of data, including both pricing and cost from internal sources and market competitors to extract optimized price variants. The market competition, in combination with the change and fluctuations in customer needs and preferences around the world, makes data science a valuable tool in manufacturing.
Automation and Robotization in the Smart Factory
Data scientists employ predictive and analytical tools to determine the best cost-saving opportunities yielding optimum benefits. The insights are then used by the engineers in their mode of operation and allowing the manufacturers to make the best decision while investing their money in robotics and automation technology.
This is how data science provides a new way of approaching design and optimization in some of the best production facilities operating today.
Supply Chain Optimization
Supply chain management is a nightmare for manufacturing industries as the risks associated are very high, complex, and unpredictable. These characteristics of this job made it more feasible for a data scientist to be handled.
Using the right data science model, market changes can be anticipated to minimize risk, avoid unnecessary expenses and result in savings. Another term used for supply chains is value chains and it is not without a reason.
Product Design and Development
For a company to maintain its market presence it has to keep on updating its product portfolio in order to keep on with the competition. Even if they don’t have to increase the number of products they should keep on updating their old products to improve their ergonomics and to provide a better user experience for the customers.
Data science can also be used in the production of a new item or improve an existing item to analyze consumer preferences and market trends. The actionable insights from customer feedback can be used by product marketers to improve products to fulfill customer requirements and profit the manufacturers. Thus a company can make sure that it is maintaining its market share.
Inventory Management and Demand Forecasting
As a manufacturing company, it is necessary to have a fundamental understanding of the number of raw materials available and finished products available. When the capacity increases it will become more and more difficult for traditional methods to cope up. So a better and smart method has to be involved. We have discussed in detail inventory management databases here.
Impact Of Data Science Automation In Manufacturing industries:
As we have seen data science is having direct effects on the quality and cost of production in manufacturing industries, Automation in this field will definitely be good for these industries. Automation will bring down the cost of production and this money can be productively used to collect more feedback from the customers and research and development of new products. As we all know Data science automation will make work easier for data scientists better insights can be deduced at less cost.
Here in this article, we focus primarily on the advantages and problems associated with data science automation by taking an example of how it will affect the manufacturing industries. Similarly, we have proven that information science works are not at risk of automation in the near future, since human interpretation plays an important role in data.
We have also seen that the introduction of automation in data science will only increase the productivity of data scientists by reducing workload.
It is necessary to note that as workers become easier and easier more people will be available to do the work and the cost of doing the work will be reduced, but when the cost is reduced need will increase exponentially and there will always be a constant or increasing payout for data science engineers and need for data science will never exhaust.
Will Data Science Automation Eliminate Data Scientists?
The quick answer is NO. Automation will not be able to eliminate data scientists since this field will always be requiring some kind of human interpretation. Data science automation, as we have seen in the article, will only make the work easier for data scientists.
How can Data Science Be Used In Lab Automation?
In laboratories, there are many scopes for the application of data science. As in laboratories we are conducting tests, maybe thousands or millions of them, and there will be repetitive results.
we can introduce data science to have some better insights into the results of the tests. Data science could even be used to predict the illness of a patient from previous similar results, and even could prescribe medicines if given adequate training.
Would A Data Scientist Use an Automated Data Science Platform?
As we have seen in the first part of the article that there are many data science automation tools available on the internet that will make a data scientist’s job easy. All data scientists should use automated platforms like h20.ai it will not only save time but will help a data science engineer to have better insights about the data and thus provide a more curated solution for the problem.