While many other buzzwords like Artificial Intelligence, Machine Learning, and Cloud are thriving, let’s see how data science is trending –
Data science is no more a buzzword as Google Trends clearly shows that the interest in data science has increased over the last 5 years. Even for professionals entering the data science industry- and there are a lot of them- you just need to do a quick search for data science jobs on any of the popular job portals and you will be surprised by the number of jobs.
According to an analysis of Data Science and Analytics Market in India, the Big Data, Data Science and Analytics industry in India was worth 17,625 crores annually in revenue for the financial year 2018. Furthermore, the analysis also predicts the data science and analytics industry to grow seven times in the next seven years at a CAGR of 33.5% becoming a 1,30,000-crore industry by the end of 2025.
Most of us might be thinking “What’s next for data science in 2019 leading up to 2020?”
As the statistics above show, 2018 was a stellar year for data science with substantial progress and rise in data science platforms, tools, technologies, and applications based on machine learning and AI. We will continue to see the advancement of data science-related technologies in 2019 and beyond. So, in order to give you a taste of what’s coming up in the data science space, we have put together the top data science trends to watch out for in 2019 and beyond–
AutoML – Automated Machine Learning to gain prominence
The phrase AutoML is being extensively used in data science conferences, publications, discussions, applications, and systems as an aid to develop better machine learning models. According to Gartner, more than 40% of data science tasks will be automated by 2020. This automation will boost the productivity of data scientists. Most of the machine learning and data science tasks require expert data engineers, data scientists, and researchers, and there is a huge talent supply now. The ability to automate repetitive data science tasks like choosing data sources, feature selection, and data preparation will compensate for the dearth of skilled data science experts. This will also help data scientists build more machine learning models in less time, improve prediction accuracy and model quality, and fine tune more new ML algorithms. Data scientists can focus on the solution instead of spending time on the process of creating data science workflows.
Few companies like Facebook and Google have already started using AutoML for internal processes. Facebook trains and tests approximately 300,000 ML models every month. It has created its own AutoML engineer referred to as Asimo that automatically produces improved versions of existing machine learning models. Even Google is developing AutoML techniques to automate the design of various machine learning models. AutoML is an exciting trend in the spotlight of the data science space with big strides of progress anticipated in the near future.
Enter the Era of Quantum Computing and Data Science
Quantum Computing as a concept is mind-bending and feels fantastic though it is still in its infancy. Quantum computers can perform complex calculations in just a couple of seconds which would otherwise take today’s computers hundreds of years to solve. This is because of the qubits or quantum bits in a quantum system that store much more information and are capable of running complex computations in seconds. In data science, quantum computing will help organizations sample huge treasure troves of data and optimize it for diverse business use cases. Quantum computers can quickly detect, analyze, integrate, and diagnose from large scattered datasets to uncover patterns. Companies like Google, Intel, and IBM are leading the way by researching into quantum computing but it is still in the beginnings. It might take up to 4 or 5 years to become a feasible option for most of the enterprise to explore its possibilities. We can expect quantum computing to become more mainstream by 2022.
Digital Twin Technology to Model the Data Science World
The Digital Twin market is anticipated to grow at a CAGR of 37.87% touching 15.66 billion by the end of 2023.
A digital twin is the creation of virtual replicas of physical device or elements that data scientists work on before setting up the actual devices. Digital Twin tech trend is based on three important concepts –
- The physical device or object that exists in the real world.
- The virtual object that exists in the digital world.
- A flyover that connects the above two pillars to send and receive data between them.
The bond a digital twin creates between the physical and digital world makes it easy for data scientists to test and enhance the systems and prevent problems even before they occur by using simulations. For instance, if there is an autonomous car in the middle of rush-hour traffic in Bangalore. The application of digital twin technology is to show it as a living model in 3D with complex technology ecosystem of navigation, entertainment, communication, electronics, collision avoidance, and more. The only missing element from this model is the human element and this is what holds good for the future of digital twin technology.
The reality is that digital twins can add value without artificial intelligence and machine learning if the application system is simple. If for example, the system has limited variables and there is an easy method to identify a linear relationship between the inputs and outputs, then no data science may be required. Nevertheless, most of the target systems have multiple data streams and multiple variables requiring data science expertise to understand what’s going on. For the years to come, the concept of digital twins will be extended with artificial intelligence enabled capabilities for advanced operation, analysis, and simulation.
Interoperability among Deep Learning Frameworks to become Strategic
Developing neural network models require data scientists to choose the right framework from a diverse set of choices like PyTorch, Microsoft Cognitive Toolkit, Caffe2, Apache MXNet, and TensorFlow. This is a critical challenge for most of the data scientists because once a model is trained and evaluated using a particular framework, it is difficult to port the trained model to another framework. This leads to a lack of interoperability among emerging deep learning frameworks. As the need for portability becomes more important than ever, Open Neural Network Exchange format (ONNX) – a new standard for exchanging deep learning models will become an essential tech trend in the data science industry. ONNX standard makes it possible to reuse existing trained neural network models across multiple frameworks, preventing vendor lock-in. Deep learning involves the concept of hyperparameter tuning which is the most expensive part of training a deep learning model. ONNX will help data scientists preserve the parameters and hyperparameter values that have been created during the training process of the model and make them instantly available in any target deep learning framework into which the model is being imported. ONNX standard is a great step forward for data scientists to put their developments in production faster and create a positive impact.
So, there you have it; the most important and emerging data science trends. 2019 is going to be an amazing year with plenty of novel discoveries and developments in the data science field. The demand for data scientists is not going to see a decline anytime soon. As such, learning data science continues to remain an essential investment. There’s a lot to be done in the world of data science, and it is not just about being an intellectualist or a techie. Real and challenging data science problems to be solved are waiting. As a closing thought, always keep in mind that time is the biggest asset. Even a single second spent doing nothing is a second lost not doing something worthwhile. Pick up the data science skills you want to learn and make the best out of it.