What are the new research challenges faced by data scientists in the 21st century?

Data science is one of the biggest raw materials of the 21st century for scientists to work with. Various disciplines like reasoning, extreme machine learning, computational systems, data analytics, deep learning, ethics, and the like are directly or indirectly related to data science. It is believed that data science would not only change the shape of technology but pave the way for an era of innovation and progress.

All this suggests that the research in the field of data science is touching a new high. Consequently, data science institutions are proliferating in various cities of India and providing training in data science. The best data science institute in Bangalore provides customized courses featuring various skillsets for training students so that they can contribute to the industrial ecosystem in metropolitan cities.

As the research in data science is growing by leaps and bounds, new challenges are emerging for the research community to counter. Let us take a look at some of these in deeper detail.

Deep learning pedagogical framework

Deep learning systems help in the functioning of various kinds of smart models as well as systems in the present times. However, we have still not understood the pedagogical framework of deep learning in detail. This is especially true about the complex artificial neural networks that operate behind the planning systems. It is in this context that the architectural properties and scope of deep learning are formidable challenges for amateurs. This makes researchers in their early stage accustomed to machine learning models due to the simplicity of tools and techniques.

Causal reasoning techniques

One of the most important tools to define relationships between various variables and establish a correlation between them is undoubtedly machine learning. The causal reasoning techniques that are a direct consequence of machine learning have found applications in sectors like economics, social science, and medicine. Economists are particularly benefiting from the machine learning techniques and the entire process of establishing the relationship between multiple financial variables has become much easier than before. Economists are now exploring various causal inferences and establishing strong relationships between two independent events.

Heterogeneity of Data

The heterogeneity of data also presents a formidable challenge in data science. This is because the handling of heterogeneous data sets decreases the accuracy with which the results can be predicted. For instance, consider a data set that is derived from the DNA sequence and contains thousands of combinations. The data is mined from different sources and there are differences in spatial and temporal representation. By virtue of various preprocessing and clustering tools,  the heterogeneity of data can be handled with a lot of ease. The wide range of data sets that we obtain after preprocessing can be used to build state-of-the-art machine learning models.

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Trustworthiness of data

The data that we collect from various sources lacks reliability as well as validity. To ensure the trustworthiness of data before the pre-processing stage, it is usually recommended to check the reliability of data sources. After reliable, robust, and secure processing is carried out, data becomes a finished raw material that is ready to use and exploit. It can then be used for trustworthy computing analytics methods and decision-making. The trustworthiness of data means that it can now be channelized into various pipelines that lead to model development, research as well as policymaking.

Data computation

At an advanced stage, data can be used for the improvement of various models and applications. The high computational power of a machine allows it to process large data sets. This acts as a precursor for building accurate predictive models and updating them as and when new data sets become available.

The computation of large data sets is not only critical for the success of any machine learning model but is also inevitable for the entire life cycle of data science.

One of the most strenuous challenges before data scientists is the automation of three important stages of the data life cycle. These include data collection, data cleansing, and data wrangling. The concern that arises if these three important stages are automated is related to accuracy and precision levels. Hence, there is always a trade-off between achieving high levels of automation as well as obtaining a high degree of accuracy.

Concluding remarks

The research challenges that are presented here are becoming food for thought for scientists worldwide. Once these challenges are overcome, we would be able to usher into newer realms of data science research.

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