10 Best Ideas for Data Science Research and Dissertation Topics in 2022


by Sayantani Sanyal


January 3, 2022

These research and thesis topics for data science will ensure more knowledge and skills for students and scholars

As companies seek to use data to drive digital and industrial transformation, companies around the world are looking for skilled and talented data professionals who can leverage meaningful insights extracted from data to improve business productivity and help to successfully achieve the company’s objectives. Recently, data science has become a lucrative career option. Nowadays, universities and institutes are offering various data science and big data courses to prepare students for success in the technology industry. The best course of action to amplify the robustness of a CV is to participate or undertake different data science projects. In this article, we have listed 10 ideas for research and thesis topics to adopt as data science projects in 2022.

  • Convenient Video Analytics Management in a Distributed Cloud: With increased reliance on the internet, video sharing has become a mode of exchanging data and information. The role of Internet of Things (IoT) implementation, telecom infrastructure, and carriers is huge in generating insights from video analytics. In this perspective, several questions need to be answered, such as the effectiveness of the existing analysis systems, the changes to come if real-time analysis is integrated, etc.
  • Smart health systems using big data analytics:Big data analytics plays an important role in making health care more efficient, accessible and cost-effective. Big data analytics improves the operational efficiency of smart healthcare providers by providing real-time analytics. It improves the capabilities of intelligent systems using information based on short-term data, but there are still distinct challenges in this area.
  • Identify fake news using real-time analytics: The circulation of fake news has become a pressing problem in the modern age. Data collected on social networks may seem legitimate, but sometimes it is not. The sources that provide the data are unauthenticated most of the time, making this a critical issue to address.
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  • Secure federated learning with real-world applications: Federated learning is a technique that trains an algorithm on multiple devices and decentralized servers. This technique can be adopted to build models locally, but whether or not this technique can be deployed at scale or not, across multiple platforms with high-level security remains unclear.
  • Big data analysis and its impact on marketing strategy: The advent of data science and big data analytics has completely redefined the marketing industry. It has helped businesses by offering valuable information about their existing and future customers. But several issues such as existence of excess data, integration of complex data in customer journeys, and complete data privacy are some of the branches that are still unexplored and need immediate attention.
  • Impact of big data on business decision-making: Current studies mean that big data has transformed the way managers and business leaders make critical decisions about business growth and development. It allows them to access objective data and analyze market environments, enabling businesses to adapt quickly and make decisions faster. Working on this topic will help students understand current market and business conditions and help them analyze new solutions.
  • Implement big data to understand consumer behavior: To understand consumer behavior, big data is used to analyze data points describing a consumer’s journey after purchasing a product. The data gives a clearer picture of understanding specific scenarios. This subject will help to understand the problems that businesses face in using information and to develop new strategies in the future to generate more return on investment.
  • Big data applications for predicting future demand and forecasting: Predictive analytics in data science has become an integral part of decision making and demand forecasting. Working on this topic will allow students to determine the importance of analyzing high quality historical data and the factors that lead to higher consumer demand.
  • The importance of data mining versus data analysis: Exploration allows for a better understanding of the data set, making it easier to navigate and use the data later. Smart analysts must understand and explore the differences between data mining and data analysis and use them according to specific needs to meet organizational requirements.
  • Data Science and Software Engineering: Software engineering and development is an important part of data science. Skilled data professionals must learn and explore the possibilities of various technical and software skills to perform critical AI and big data tasks.

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Jessica C. Bell