5 Most Challenging Research Issues in Data Science

Data science is dynamic and draws strategies from statistics, programming skills, algorithms, computer science, and mathematics.

Data science experts use artificial intelligence and machine learning algorithms to perform tasks that require human intelligence.

These characteristics present different challenging research issues that spread over society and innovation.

A lot of questions are emerging regarding the challenging research issues in data science.

This article identifies five research areas that data scientists and researchers can address to improve research efficiency. 

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Addressing big data processing vulnerability

Various methodologies are applied in dealing with data processing vulnerability.

Some of the data and information may be unlabeled, especially when the volume is high, making it difficult to process.

These issues can be resolved by the use of distributed learning, dynamic learning, deep learning, and logic hypothesis. 

The rate of big data generation is outperforming the development of available storage systems.

Managing unstructured data is getting too complex, as reported by data analysts.

There is also an increase in complex data formats such as video, documents, audio, smart devices, and social media.

 The researchers are not able to identify easily what is relevant and what is not.

Online business transactions are expected to continue rising and so will be the connected devices. This is likely to generate large volumes of data.

 Data scientists are now supplementing relational databases with dynamic NoSQL databases.

Organizations are using distributed computing systems to analyze data and generate valuable insights. 

Scientific learning understanding of deep learning algorithms

Many data scientists and researchers appreciate the significance of deep learning.

However, they may fail to analyze the important properties of the deep learning models and how they produce outcomes. 

It is difficult for them to understand how delicate and vigorous the models are to include data deviations. The data analysts and scientists don’t understand how deep learning will perform tasks and the input requirements. 

Deep learning algorithms are also complex for students pursuing courses in data science.

They are faced with a lot of challenges when writing essays and other assignments.

If you are assigned data science essays and assignments and you are not able to handle them, you can engage writing professionals at Edubirdie to help you in the research.

Since essays and assignments are time-consuming, you can create more time to study and engage in other learning activities.

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Distributed cloud for real-time video analytics

The increased internet access has led to the popularity of video games as a medium of data exchange.

The role of operators and telecom infrastructure and the application of CCTVs and the Internet of Things (IoT) are also being emphasized. 

When the real data becomes available, it becomes difficult to transfer it to the cloud and process it efficiently.

Cloud computing has also raised security issues. Data transmitted in distributed networks are more prone to security threats such as hacking.

Additionally, you may not see the exact location of your data and how it is being processed. 

Many enterprises and companies lack the expertise and resources to handle distributed data.

They are increasing the cloud workloads and are having a difficult time coping with the tools while the need for expertise is growing. Organizations may need to deploy additional IT staff or train the existing ones.

Small organizations that may find it costly to add cloud specialists can take advantage of DevOps tools to perform repetitive tasks.

These tools may enable them to save costs and enhance the security and governance of cloud technology. 

Building relevant and productive chatbot systems

The Q&A and chatbot systems are quickly picking up the pace in many organizations today. There are a variety of chatbot systems available in the market.

One of the key challenging issues is how to make the systems productive and enable them to support summarized real-time discussions. 

This challenge becomes paramount with an increase in the scale of businesses. There is a lot of research going on to find the reasons behind this trend.

The solution to this problem requires an understanding of machine learning and natural language processing (NLP)

If you are a data science student or you are pursuing a course in artificial intelligence, you may have an uphill task when researching this area.

The good news is that you can use apps for research to enhance the efficiency of your academic writing process. With the help of the apps, you can save time and effort while writing any academic paper, no matter its complexity.

Big data processing uncertainty

Uncertainty in big data processing can be handled in various ways. The problem comes in if the data is unlabeled and voluminous.

Many organizations use various data management tools with designs that are aimed at supporting analytical and operational processing. 

Apart from the SQL, the NoSQL framework can differentiate big data from the traditional database management system.

The framework fulfills the big data demand applications including managing large volumes of data on a real-time basis.

The diversity of NoSQL tools, market status, and the developers is what increases data management uncertainty. 

Data analysts and scientists try to use fuzzy logic theory, distributed learning, active learning, and deep learning to fix this problem.

You can address the big data complexity by prioritizing important data relating to products, customers, suppliers, and sites.

Also, focus on updating and refining data concerning customer profiles to develop a master profile. 


Research issues in data science continue overwhelming experts as technology advances.

Data analysts and other experts are on the frontline trying to research what can be done to resolve the issues of data complexity.

Some of the methodologies deployed seem to be efficient but there are still gaps.

Fixing the challenges of data science should not be the responsibility of experts only, but all people in organizations in their capacity. 

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