Data augmentation as an extension of data analytics: Working, operations and methodologies

Posted by Vidhi Yadav on September 8th, 2022

Introduction 

The overall functioning and accuracy of data analytics depends upon both the quality as well as quantity of the training data that is available. It has often been noticed that missing values in data sets or insufficient data leads to inadequate training of the model. A lot of inaccuracies may be encountered due to data insufficiency. This is where the role of data augmentation comes into play. Data augmentation can be understood as an extension of data analytics. It supplements the process of data collection as well as data preparation so that the analytics that is derived out of these data sets is precise, accurate and trustworthy.

Data augmentation helps organizations in a number of ways. It is one of the most important processes that is associated with data analytics. Although data augmentation is not a separate domain that needs to be studied individually, its importance cannot be ignored at the same time. One of the unique ways to get knowledge about data augmentation is to enroll in a Data Analytics course. For instance, data analytics courses in Bangalore provide full fledged training related to data science, data analytics and data management in addition to data augmentation. Let us look at the process of data augmentation in much more detail.

Data augmentation and its importance 

Data augmentation is the process in which we supplement the existing data with new data so that training of the model is appropriate and complete in all aspects. The process involves making a number of changes with the aim of generating novel data sets. The novel data set is usually generated using the deep learning technique. 

With the help of a data centric approach, it becomes possible to augment the existing data with new data and improve the performance, efficiency, outcome and accuracy of the machine learning model. Needless to mention, the sufficiency and qualitative supremacy of data not only reduces the operational costs but also improves the outcome of the overall model.

Working and operations

One of the most unique ways for data augmentation is to make simple alterations on visual data. This can simply be done by creating synthetic data sets that are derived from the existing ones. Generative adversarial networks are one of the simplest mechanisms for creating synthetic data sets. 

Let us know some of the important techniques that can be followed for the process of data augmentation. These techniques are highly relevant for the purpose of image classification and segmentation. For instance, one of the techniques that we can use is random rotating and resizing. We can also use vertical or horizontal scaling to align the image along a particular axis. Cropping and zooming are and also techniques that are most common for the purpose of augmentation. In addition to this, other techniques include grey scaling and random erasing.

Complex methods 

The above mentioned methods are usually simple and can be easily employed for the process of data augmentation. However, there are also some complex methods that are used for this process at an advanced level. 

• The first important method is reinforcement learning. Reinforcement learning is a process in which a model is allowed to take inputs from its immediate environment. In the process of reinforcement learning, a model is able to make decisions and achieve its goals by learning from a virtual environment. 

• The second important method is neural styles transfer. One of the most common uses of neural style transfer is to aggregate content image, style image and to separate the former from the latter. 

• In addition to this, we can also use generative adversarial networks as mentioned before. 

• Other important techniques of data augmentation include synonym replacement and word substitution. 

• Back translation is also one of the techniques that can be included under the domain of data augmentation. This technique has been inspired from the domain of natural language processing and is used in the context of translation of text from one form to another or one language to another.

The way ahead 

Data augmentation would be highly beneficial in future as it would improve the performance, accuracy and efficiency of different types of data models. It would also bring down the cost of operations that are incurred during the process of data collection. It would also be a prime technique that would allow us to predict rare events in advance. The prospects of data augmentation in the domain of data security and data privacy are also being considered in the present time.