In the last few decades, we have witnessed rapid advances in information technology. The amount of data has undergone voluminous expansion in the last few years as these years have been dominated by big data and its applications. Consequently, we have been confronted with challenges with respect to storage as well as the processing of large data sets.
It is in this context that our data analytics capabilities need to be further strengthened. One prosperous step in this regard is the setting up of training institutions that impart new-age skills related to business analytics, data science, artificial intelligence, and machine learning. The data analytics course in Bangalore is proving to be a game-changer for the analytics industry as well as aspiring data analysts. Amid this progress of the analytics industry, some questions related to economic domains remain unanswered. We strive to answer them in this article.
How can analytics boost the performance of economic forecasting models?
Data analytics can help in the analysis of new data sets that are obtained by mining unstructured data sets. The useful data from these data sets can be integrated into economic forecasting models so that they are trained with the help of wider data sets. This can reduce the chances of overfitting and predict the outcomes in a precise way. Data analytics is also useful as it helps in the assessment of historical financial curves as well as the present economic conditions. The useful inputs that are obtained from mapping these data sets can help in improving the performance of economic forecasting models. Analytics is also important as it helps in widening the applications of economic forecasting models in domains like stock analysis and algorithm trading. We cannot ignore the applications of data analytics in those sectors that are completely data-driven. In addition to this, data analytics is also pivotal in decision science models, the output of which forms a critical component of economic forecasting models.
What are the technical challenges involved in analytics?
The number of devices that gather and generate information in the economic sector are numerous and these include different types of iot devices as well as sensors. These data devices help in the processing of information that can be used as a raw material for training models related to various economic applications. However, the analysis of data that has been collected from diverse sources is a challenge in itself. This is not only because of the size and variety of this data but also due to its reliability and validity factors. If the data is obtained from unreliable sources, it may invariably reduce the predictive power of computational systems. For instance, data obtained from cloud computing systems is easy to use as well as process. However, before subjecting this data to processing and analytical solutions, it is important to establish its validity and reliability.
How can deep learning aid in analytics?
There is no doubt in the fact that machine learning methodologies like support vector machines, decision trees, random forest models as well as algorithms like classification and clustering have greatly enhanced our analytical capabilities. However, the rapid explosion in data has forced us to explore deep learning techniques that can help us in the analysis of data in various formats like image, video, text, and the like. Deep learning models make use of artificial neural networks, deep belief networks, deep convolutional neural networks as well as deep restricted Boltzmann machines to process both structured and unstructured data sets. For instance, the stock market forecasting was earlier performed by machine learning techniques by treating it as a regression problem. The challenge in using machine learning techniques was a huge dependence on structured data sets that may or may not be available. Deep learning techniques resolved the problem by performing time series forecasting of data and reducing the dependence on structured data sets. Consequently, the problem of overfitting was also resolved over a period of time. Thus, we came up with an accurate and precise analysis of the stock market with the help of deep learning techniques.
The way ahead
Both analytics and deep learning techniques can go a long way in improving the performance of our economic forecasting models. While deep learning techniques can prove helpful in mining unstructured data sets, analytics can help in deriving timely insights. That said, the need of the hour is to prioritize deep learning methodologies so that our analytics capabilities can be boosted which then pave the way for conceiving state of art economic forecasting models.