These problem areas cover a wide range of difficulties spanning research, innovation, and society, with solutions derived from computer science, statistics, and various algorithms, with applications appearing in a variety of fields. Despite the fact that big data will be the focus of operations in 2020, analysts will still have to deal with a number of obstacles and challenges. A handful of these difficulties are related to data science.


“The world is one big data problem.” – by Andrew McAfee


Learning algorithms, particularly deep learning algorithms, have a scientific understanding- Despite our admiration for deep learning's incredible achievements, we still don't have a rational explanation of why it works so well. The numerical properties of deep learning models are not examined. We have no idea how to explain why a deep learning model gives one result but not the other.
In a dispersed cloud, managing synchronised video analytics- Videos have become a common medium of information exchange, thanks to increased access to the internet, even in developing countries. The communication system, administrators, Internet of Things (IoT) deployment, and CCTVs all play a part in promoting this.

Dealing with vulnerability in big data processing- The vulnerability in large data processing can be addressed in a variety of ways. This includes sub-topics such as how to benefit from low veracity and insufficient/uncertain training data. When there's a lot of unlabelled data, how do you deal with vulnerability? To tackle these problems, we can use dynamic learning, distributed learning, deep learning, and the indefinite logic hypothesis.


“Big data is at the foundation of all the megatrends that are happening.” – By Chris Lynch


Many questions have been raised about the difficult research difficulties surrounding data science. To address these concerns, we must first identify research challenge areas on which researchers and data scientists might focus in order to increase research efficiency.

Factors that may be readily avoided have a significant impact on data scientists' and the data team's productivity. Collecting relevant data, organising data assets, documenting data tables, and explicitly defining business terminology and KPIs: these excellent practises are simple to implement and will have a significant impact on the data team's productivity while reducing annoyance levels.