Data science impacts a lot of businesses in different industries. It is regarded as the ‘sexiest job of the 21st century.’ It is no surprise that it has managed to come this far as its immense potential and capability was always in its applications. Apart from data science, there is one more technology that is gaining prominence, and that is automation.
Today, automation is not only used in sectors like robotics, but it also collaborates with other domains to simplify processes for techies and data science is no exception. There are many companies that are now coming up with new products and tool for accelerating data science. In this article, we shall discuss the automation tools that can be used by data science professionals. But before doing that, we will first understand the term ‘Data Science.’
Understanding Data Science
Data science is a blend of various algorithms, tools, and machine learning principles that operate with the goal of discovering hidden patterns from raw data. It is used to make decisions and predictions by using prescriptive analysis, predictive causal analysis, and machine learning. It is used to scope out the right questions from the dataset. It is a multidisciplinary field that works at the raw level of data (structured, unstructured, or both) to make predictions, identify patterns and trends, build data models, and create more efficient machine learning algorithms. Data science experts work in the realm of the unknown. Some of the data science techniques are regression analysis, classification analysis, clustering analysis, association analysis, and anomaly detection. It solves the problem using Bayesian optimization.
Automation Tools for Data Scientists
Here is a comprehensive list of some of the popular automation tools that data scientists can use.
There are many machine learning algorithms available in the market that can be used and many are also implemented in the Weka package. These machine learning algorithms have their own hyperparameters that can drastically alter their performance and there are many possible alternatives available. This is where Auto-Weka comes into the picture. Released initially in 2013, it solves this problem by selecting a learning algorithm simultaneously and setting its hyperparameters. It solves the problem through Bayesian optimization. It also helps non-expert users to effectively identify machine learning algorithms and hyperparameter settings that are appropriate to their applications. In 2016, a new version of this tool was released after adding new features and stability fixes.
2. DataRobot Automated Machine Learning
This is an advanced Enterprise AI platform. It incorporates experience, knowledge, and best practices of the world’s leading data scientists. The automated machine learning platform of DataRobot allows ML developers to automate the creation of machine learning models to help understand and trust the predictions they make. It does this with unprecedented transparency. It offers different types of regression techniques that range from the simplest to most complicated statistical classic regression models. One more best thing about this platform is that it can also solve simple problems of up to a hundred different categories. This has become a sought-after platform for data science professionals.
It is the next go-tool for solving data science problems, developed by Sparkcognition. It is a company that builds AI systems for advancing the most important interests. It is an automated model building tool that allows users to switch from data to model in less time when compared to traditional methods. It allows productive extraction of insights and rapid prototyping of scenarios. This tool uses a patented approach that is based on neuroevolution. Neuroevolution custom builds model architectures based on the problem at hand.
Feature engineering is very time-consuming, important, and challenging for data science professionals. By packing the best-in-class AI capabilities, dotData works towards automating data science. The company solely focuses on automating and democratising the entire data science dataflow. In traditional processes, it can take months to identify the use case and get pipelines into production. This project will help execute complex data science projects with speed.
H2O has emerged as a leader in machine learning automation. It is a distributed, open-source in-memory machine learning platform that offers linear scalability. This platform supports some of the widely used machine learning and statistical algorithms. One of the best features of this platform is that it has an industry-leading AutoML functionality that runs through all hyperparameters and algorithms to produce a leaderboard of the best models.
The automation of data science is still in the nascent stage. It is already impacting the business world to a large extent. A considerable improvement in these technologies has already begun. Large corporations have now started investing in data science and machine learning technologies. The advancement in automation technologies will modify and simplify the tasks that need to be performed by data scientists.
I hope you found this article useful. If you are interested to learn data science and get instant updates about data science certifications and become a data science expert, check out Global Tech Council.