10 Prominent Research Areas In Data Science
We are living in the age of data. Ground-breaking advancements in technology have ushered in this new age, enabling us to acquire brilliant insights from various data. The emergent field of data science is transforming the world by employing potent statistical techniques and using robust computational systems.
Data science is ever-evolving. It is one of the most in-demand professional skills today and is the subject of widespread research & development. The field has an active research community driving progress and bringing forth path-breaking advancements.
Here, in this article, we explore 10 promising avenues of research in the domain.
10 Exciting Research Topics In Data Science
Data science is a multidisciplinary field. To dissect & interpret data, data science derives concepts and techniques from mathematics, statistics, computer science, software engineering, and associated fields.
Theses and dissertations remain the primary media for scientific research & academic investigations. Grounded in academic rigour, they engage some brilliant minds across top universities in exploration and development.
The following ten research areas can serve as prominent topics for data science dissertations.
[Note that the following are extensive research areas and can harbour several questions & topics for research. Dig deep to come up with appropriate topic ideas. ]
The Scientific Understanding of Learning
Deep learning has seen successful implementations across different domains. Yet, there is a significant lack of scientific understanding behind its exceptional performance and success.
The critical gaps in theoretical knowledge and research are:
- Lack of scientific understanding of deep learning’s operations;
- Poor knowledge about the mathematical properties of deep learning algorithms
- Inadequate ideas about the robustness or fragility of models to data perturbations
- Poor understanding of fundamental computational limits, measurement of uncertainty in outcomes, etc.
The above theoretical aspects are hot research topics in data science and AI today. However, if you are tackling such a topic and need some help, seek support from professional dissertation editors & writers.
You must have come across the term machine learning while browsing books & articles online or in libraries. Machine learning is currently the most promising avenue for developing artificial intelligence. However, machine learning has gone beyond such analyses and can now tackle causal questions.
- During extensive data analysis, the current study’s exciting, innovative, and growing area is causal inference.
- Widespread research is looking into multiple causal inferences in processes and observations.
- Natural experiments in economics & social sciences serve as inspirations for novel machine learning research in public health, retail, sports, etc.
The term precious data is used to denote data that contains
- information about rare events
- challenging and expensive to acquire
- specific and has a limited focus
Proper mining and analysis of such data are essentialities and a significant research aim in data science. Research into finding efficient, robust, and agile data analysis algorithms & models is picking up pace fast.
Multiple, Heterogeneous Data Structures
Specific problems require us to collate different kinds of data from varied sources. For example, cancer detection using AI and climate prediction models are two such applications that require diverse data.
Accumulating and analysing large scale data of varying formats, qualities, and numerous sources is a significant challenge. State-of-the-art data science methods cannot yet handle multiple, heterogeneous data to build a single, perfect model. As a result, focused research on combining data from various sources and of multiple types is growing and will have an extraordinary impact on the field.
This research area is substantially challenging. Consult with your instructors, talk with your peers, and look for professional dissertation editors.
Inferring From Noisy & Flawed Data
Data acquired from the real world suffers from numerous flaws. Noise, redundancy, unwanted Information, and unpredictability are significant limitations.
Making accurate inferences from noisy data using data science and machine learning techniques is a prominent field of research. Researchers are investigating better ways to filter noise and improve the accuracy & efficiency of inference models.
We have witnessed rapid implementations of AI systems across various domains in recent times. Autonomous vehicles, content management systems, chatbots, health care, law enforcement, public safety, entertainment – the list goes on. Given the highly probabilistic nature of AI models, questions on ethics, privacy, and fairness arise automatically.
A trustworthy AI model provides ample justifications for the outcomes. This allows end-users to understand the reasons behind predictions and develop a trust in the models.
Research and development of a trustworthy AI are gaining tremendous traction. However, it is a challenging research area as new trust properties come with new trade-offs; privacy vs accuracy, robustness vs efficiency, fairness vs robustness.
Systems For Data-Intensive Applications
Data is the primary focus of almost all major applicational fields, especially the sciences. Considerable advancements in computational systems have led to faster &special-purpose processors, GPUs, TPUs, domain-specific accelerators, etc. Many such systems form the primary hardware for machine & deep learning systems.
But even such fast & flexible computational systems have their limitations. Industries and research organisations want real-time predictions. The laws of physics, network latency & bandwidth and excessive energy requirements are significant bottlenecks for deep learning systems.
Research, rethinking and redesigning of computational designs are the need of the hour. Studies are now focusing on data rather than computation. Typical research topics include:
- Development of faster computational systems,
- Heterogeneous processing,
- More immediate network transfer & access,
- Better data handling & storage capabilities,
- Improved energy efficiency,
Classification Of Large-Sparse Datasets
Large but sparse datasets are pretty standard in big data. User ratings and comments over an extensive array of entities are prominent examples of such a dataset.
Research and investigations on developing accurate classifiers for high-dimensional and sparse data vectors. Studies in this research area focus on answering questions such as:
- How do different data set properties (sparsity rate, nature of discrepancies, etc.) affect classification?
- How to classify large sparse data efficiently using non-linear data structures?
Theses and dissertations need to address these questions with empirical and theoretical analyses.
Privacy is a crucial issue in AI and data science.
For most data science and ML applications, the more the data, the better the predictions. However, the manner of sourcing said data is an essential factor, primarily due to privacy or regularisation concerns. For example, sharing patient records from different hospitals can lead to better predictions but may raise issues of doctor-patient confidentiality.
Research on privacy issues involved exploring feasible, scalable, and effective data security techniques. Investigations on cryptographic and statistical methods, multiparty computation, differential privacy, secure enclaves, etc., are rising.
Big Data In Business
The advent of data science and big data analytics has redefined the marketing industry.
Yet several issues remain unexplored and demand substantial research. For example, domains such as surplus data, integration of complex data into customer data sets, etc., are still untrodden and need immediate attention.
Big data changed the way managers and business leaders make critical decisions. Further research on this topic will enable a better understanding of business conditions, key influencing & psychological factors and develop better solutions.
Understanding Consumer Behaviour
Big data and data science are path-breaking tools for visualising a consumer’s journey after buying a product. Research into this topic will help understand businesses’ problems during knowledge discovery, utilising insights and developing new strategies.
Well, that’s about it for this write-up. Hope it helps you come up with a revelatory & focused topic for your data science dissertations. If you need support beyond what your institute provides you with, always look for the best professional dissertation writers & editors.
All the best!