80+ Data Science Dissertation Topics for PhD Students | Affordable Dissertation

After learning the fundamentals of data analytics, it’s time to apply your knowledge to projects. Employers prefer hiring students who have diverse experience that can be demonstrated in their projects. They also prefer employees who show good skills in data management and handling, manipulating data, and using statistics for predictive analysis and reporting.

You, as a student, should not stress about learning a computer language or software because there are user-friendly tools and packages to handle data and derive meaningful insights. Therefore, Affordable Dissertation UK recommends students undertake research projects that demonstrate their skills in data handling and reporting, even to non-technical audiences.

In this blog, we aim to help you, as PhD students, understand and choose topics for data analysis research projects as PhD students. You can learn how to choose a topic that will impress your research supervisor and be a good attraction for employers.

Why Data Analysis Research Projects are Important 

Research involving data analytics is crucial for professionals who want to compete and excel in a modern digital-data-centric business environment. 

Data analytics help you to demonstrate the following:

1. Application of Practical Skills: You can demonstrate practical experience and the ability to bring theory into practice in the real world.
2. Versatility: Data science has rapidly spread across various industries, and therefore, you can demonstrate that you have versatile skills, particularly adaptability to emerging technologies.
3. Critical Thinking: Data science research projects require critical thinking for completion and thus you demonstrate that you can provide critical information for important decisions using data analytics skills.
4. Technical Skills: Data Science provides you technical skills mainly in data handling and management however, these technical skills enable you to participate in diverse decision making of diverse industries.

Therefore, this blog will help you, in essence, to undertake a research project that helps you to gain not only academic success but also professional success while hunting for a job and during your employment.

50+ Data Science Dissertation Topics for PhD Students Categorised into Trending Sub-Areas

PhD Level Data Science Research Projects

At PhD Level, you must demonstrate advanced skills to impress your supervisor as well as potential funding organisations or employers; therefore, it is critical that you choose a topic that guides you to a successful project. Additionally, you will perform exploratory and predictive data analysis to understand data in detail.

• Probability and Statistics: Performs mean, median, standard deviation, probability distribution algorithms, and correlation on data. 
• Exploratory data analysis: Explore the distribution of data, understand different types of columns, and understand trends and patterns. 
• Predictive analytics: Performs regressions, classifications, clustering, and forecasting using machine learning algorithms.  

Probability and Statistics Projects

Real-time data from social media platforms are hot topics of interest for researchers. Consider the following topic: 

To analyse Twitter feeds on celebrities’ pages and explore the influence of celebrity on brand following

The following objectives cover the aim above;

• To identify trends in followers’ feedback towards celebrity endorsement on Twitter
• To explore the impact of celebrity endorsement on consumer behaviour 
• To explore the factors that shape the fan following of celebrities
• To make recommendations for businesses adopting celebrity endorsement 

The following hypotheses reflect the aim above:

H0: Celebrity endorsement on Twitter has a significant impact on consumer behaviour  

H1: Celebrity endorsement on Twitter has no impact on consumer behaviour  

There are also other relevant similar topics for PhD research projects:

1. Application of Big Data and social media intelligence in a technological educational institution
2. Data Science in the fitness industry: using social media to expand business 
3. Developing applications for personalised fitness and nutrition by using data analytics 
4. Using big data and blockchain as the new non-SQL database in the Fintech and banking industry 
5. Using social media voting data to assess brand popularity and equity 
6. Forecasting consumer behaviour towards change in sports product characteristics based on Instagram trends  

Data Science in Entertainment Sectors 

You can work on a portfolio project by applying skills similar to those of other datasets, such as Netflix Movie Data. Additionally, you can apply Exploratory Data Analysis in Python to show skill in data cleaning and validation, understanding relatedness and distribution, and exploring multivariate relationships.

Consider the following aims and objectives for this project:

To identify the impact of Guest stars on the box office 

• Measure the relationship between guest stars and movie success
•  Measure the relationship between guest stars and box office collection 
• To highlight factors for the selection of guest stars

Other Interesting Suggestions for Topics in the Entertainment Industry Are

7. Analysing factors that affect fans of music stars using big data analytics
8. Understanding the behaviour of music festival attendees by applying supervised learning 
9. Using natural language processing (NLP) and clustering to find similarities between movie plots in Netflix 
10. Using data mining techniques and algorithms to understand the preferences of consumers in the drama streaming industry.
11. Using data science to understand gamer behaviour in online gaming industry 
12. Is big data analytics helpful in enhancing sustainability in the gaming industry? Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data,
13. Optimising delivery networks of the content of streaming businesses using data science algorithms. 
14. Applying big data science in analysing the behaviour of utopian movie fans 
15. Identifying factors that affect the behaviour of movie star fans in the US fantasy genre 

Exploratory Data Analysis (EDA) Projects

16. Analysing Debt Statistics using SQL and big data analytics of developing countries 
17. Analysing classification of text in legal documents by applying machine learning models 
18. Understanding interest rate patterns using big data for developed countries 
19. Exploring the most significant debt indicators for underdeveloped countries 
20. Identifying the most common debt indicators of Asian countries in the past fifty years
21. Applying Big Data solutions in movement mobility applications such as Uber
22. Conducting data analytics to predict health burdens due to demographic changes 

Predictive analytics Topics 

23. Understanding the response of consumers to price change in the FMCG industry
24. Building predictive machine learning models to assess customer churn in the grocery retail sector 
25. Applying big data analytics to understand business reporting patterns about disclosure.
26. Applying big data and machine learning to understand consumer preferences and innovations in the automobile sector.
27. Identifying factors that affect the price elasticity of personal hygiene retail products 
28. Understanding trends in online reviews by customers about product design and Applying data science to optimise talent acquisition and human resource management in MNCs.
29. Predicting the occurrence of diabetes in relation to diet intake consumption in the European population 
30. Forecasting risks of motor accidents due to alcohol consumption using big data analytics 
31. Using data science to improve advertising strategy for ad placement.

Predict Credit Card Approval

Data science has also gained popularity in the banking industry. For example, consider the following research project. 

To predict approval of credit card request for a customer(s)

• To identify the factors that affect approval of credit card request 
• To predict the chances of approval of a credit card request

Applying simple machine learning algorithms is an essential part of a data analyst’s life. You can gain further experience by applying similar methods to a new dataset, such as Banking Marketing. 

Other possible topics in this research area are as follows:

32. Explore the factors that influence credit card default 

33. To identify the impact of credit history on the risk of default of small businesses

34. Explore the factors that affect financial inclusion of women in developing countries

35. Reducing traffic-related deaths by identifying risk factors of car accidents 

36. Exploring the factors that increase the risk of accidents in the summer season 

37. Exploring the determinants of non-fatal traffic violations 

38. Identifying the impact of safety procedures on accident prevention 

39. Using big data analytics to explore insurance amount in relation to accident risk factor 

Analysis of world population 

There is a range of data science projects focused on World Population Analysis. Some of the recent research topics and areas in this context are: 

40. Applying big data analytics to predict lifespan 

41. Maximising the accuracy of geological surveys by applying big data analytics in the exploration of natural resources.

42. The role of big data analytics in urban planning 

43. Using big data analytics to analyse crime patterns and prevention strategies.

44. Using big data to predict the economic burden of cancer patients

45. Optimising pharmaceutical drug delivery using machine learning in large pharmaceutical retail chains 

46. Disaster management using data science applications for weather forecasts.

47. Using big data analytics to control pandemics such as COVID-19

48. Using big data analytics to map hospital locations for equitable healthcare 

Data Analytics in Education Research 

49. Creating user-friendly applications for children for mathematics lessons 

50. Developing advanced data analysis applications for teachers to enhance student engagement 

51. Applying machine learning algorithms to enhance the personalisation of online learning students.

52. Exploring the role of big data in analysing student performance and suggesting educational policies 

53. Data Science and life chance: understanding the relationship between socio-economic background and learning attainment

54. Optimising human resources and budget allocation using big data analytics 

55. Predicting student outcomes using socioeconomic data and census 

Big Data in the Transportation Sector 

56. Using random forest to predict Taxi Fares in the United Kingdom

57. Understanding disruptions in supply chain networks using big data 

58. Building anomaly detection machine learning models for network traffic management.

59. Building anomaly detection machine learning models for network traffic management.

60. Application of big data analytics to predict the operational efficiency of airlines.

61. Applying big data analytics to optimise routes for the public transportation sector

62. Predicting driver behaviour towards penalties using data science

63. Understanding the impact of speed limits on the prevention of accidents 

64. Understanding passenger choice of taxi service and loyalty behaviour 

Time Series Analysis and Forecasting 

If you want to get an idea for other types of analysis and forecasting, try exploring the following dissertation topics.

65. Using data science to increase the efficiency of the recruitment process

66. Analysing rent and real estate pricing by big data analytics predictive models 

67. Understanding investment behaviour using financial time series data 

68. Big data analytics can be applied to monitor and prevent environmental pollution.

69. Understanding the safety behaviour of construction workers and accident prevention  

70. Understanding the relationship between gender diversity and profitability 

71. Predicting financial performance with sustainability investment 

72. Predicting share prices based on corporate social responsibility budgets 

Big Data in E-commerce 

Some of the interesting research areas in e-commerce are

73. Examination of the influence of big data analytics on recommendation systems by e-commerce businesses 

74. Identifying factors that affect traffic on e-commerce websites

75. Using big data science to improve warehousing function for large e-commerce businesses

76. Using big data analytics to develop a model for smart E-commerce logistics in the construction sector 

77. Using big data analytics to develop intelligent models for e-commerce metasearch using a next-generation ranking system 

78. Building logistic distribution models using big data analytics and algorithms 

79. Making improvements in e-commerce business processes using predictive algorithms for customer needs and seasonality in the hotel sector 

80. Predicting hotel turnout based on tourist data analytics

Conclusion

After learning the essential skills, you need to build a solid portfolio to showcase your knowledge. In addition, you will learn new tools, features, and concepts that will benefit your professional life. 

In this post, we have learned about beginner projects, advanced projects, senior projects, and end-to-end data analytics projects. In addition, we have covered projects on data ingestion and cleaning, probability and statistics, data manipulation and visualisation, and exploratory data and predictive analytics.