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1. A statistical analysis of the rise of crime against aboriginal women in Australia.

This dissertation topic mainly seeks to highlight the use of statistical tool in analysing the rise of crime against indigenous women. Moreover, while working on this dissertation topic, students need to focus on the veracity of data collected during this period. The research process must be very precise and the use of methods for data collection need to be value-neutral or unbiased. The statistical tools like SPSS or R-Studio are also utilised in order to come a desired conclusion. Further, recommendations are made regarding the manner in which data analysis of various parameters are done.

Suggested Readings:
Baldry, E., & Cunneen, C. (2014). Imprisoned Indigenous women and the shadow of colonial patriarchy. Australian & New Zealand Journal of Criminology, 47(2), 276-298.

https://www.indigenousjustice.gov.au/wp-content/uploads/mp/files/publications/files/brief019.pdf

2. Use of data mining techniques for business analytics: Improved decision-making through statistical analysis.

In this dissertation, the sole focus is kept on how the use of data crunching methods have benefitted various businesses. The decision-making process for a business firm has to take in numerous determinants in order to be competitive in the market. The data analysis, which can be a gigantic task, is usually done using various tools like R or MATLAB. The marketing decisions that are taken by organisations also need to be analysed. The use of data in measuring various determinants for coming to a desired conclusion requires in-depth analysis.

Suggested Readings:
A. Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.
B. Evans, J. R., & Lindner, C. H. (2012). Business analytics: the next frontier for decision sciences. Decision Line, 43(2), 4-6.

3. Estimation of wheat cropping in Australia along with their areas using machine learning based algorithm.

This dissertation topics revolves around the various factors of farming such as crop yield, cropping pattern, seasons for growing the crop of wheat. The research process includes exploration of data, understanding the complexities of Geographic Information System (GIS), discovering and learning software tools that are data compatible, thereby, forging an effective processing method by conducting research experimentation. Moreover, the use of machine learning is also considered in order to streamline the volume of data. The analysis of natural factors such as rainfall and temperature for these specific areas are also done.

Suggested Readings:
A. Vagh, Y. (2013). Mining climate data for shire level wheat yield predictions in Western Australia.
B. Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C., & Foley, J. A. (2012). Recent patterns of crop yield growth and stagnation. Nature communications, 3, 1293.

4. Analysing the use of statistics to tally top ten greats in the world of football.

The sports arena has been revolutionised with the use of data analytics, thereby, bringing out new dimensions of analysis. While using these spatio-temporal tools, students have to keep the authenticity of data as an important parameter. The numerous aspects of the sport such as goals, assists, tackles, saves, etc. are also thoroughly analysed. The analysis of the wins and losses are also an important part of this dissertation. The role of a sports analysts must be taken up by the students in order to discuss all outcomes of these investigation.

Suggested Readings:
A. Albert, J., & Koning, R. H. (Eds.). (2007). Statistical thinking in sports. CRC Press.
B. Wood, B. (2016). A comparative assessment of elite level football: highlighting final third entries, 50: 50 challenges and goalkeeper distributions (Doctoral dissertation, Cardiff Metropolitan University).

5. Anticipating depression levels among the teens of Australia through the data analysis of their respective social media posts.

In this dissertation, the digital analytics regarding social media usage is taken into consideration. It looks into every aspect of behaviour in the Australian teens. The data is collected and accordingly examined with the help of various statistical tools. The analysis of data is plotted specifically to find out how social media posts help in tracking their respective depression levels. The degree of frustration among the youth can be due to various factors, and social media analysis can guide us get a clear understanding about each of them. Recommendation regarding the same would also be provided while preparing this dissertation.

Suggested Readings:
A. Bazeley, P. (2013). Qualitative data analysis: Practical strategies. Sage.
B. Griffiths, M. D., Kuss, D. J., & Demetrovics, Z. (2014). Social networking addiction: An overview of preliminary findings. In Behavioral addictions (pp. 119-141).

6. A robust clinical decision support system that can diagnose a disease using classification techniques.

The clinical support system that is framed keeping every parameter in consideration can easily help in diagnosis. Such analysis is mainly expounding various aspects of data analytics. While examining numerous classification techniques, its parameters are also thoroughly studied making the system robust. Every detail is examined and plotted in a graph, thereby, framing an orderly and vigorous structure for diagnosis. The data collected during this dissertation research consists information of the disease as well as its timeline. Here the concern is not just limited to data mining, but also maintaining the privacy of such confidential data.

A. Siddiqui, M. F., Reza, A. W., & Kanesan, J. (2015). An automated and intelligent medical decision support system for brain MRI scans classification. PloS one, 10(8), e0135875.

7. Compare and contrast all applications for educational data mining regarding chemical engineering course in Australia.

Chemical engineering has been one of the most lucrative engineering courses. The data collected in this regard is also analysed and is compared with other educational courses. Such data mining is done using statistical and analytical tools. The various applications in this regard have to be thoroughly studied and accordingly utilised to arrive at a proper conclusion. While analysing these details every parameter must be taken into consideration. Further, the contribution made by the engineers from this field must also be examined. The recommendations made for enhancing the understanding of this topic is to be made sincerely.

Suggested Readings:
A. Gupta, G. K. (2014). Introduction to data mining with case studies. PHI Learning Pvt. Ltd.
B. Schofield, D. (2012). Mass effect: A chemical engineering education application of virtual reality simulator technology. Journal of Online Learning and Teaching, 8(1), 63.

8. Sentiment analysis among young women in Australia after Donald Trump got elected as President of USA.

While preparing this dissertation, the data collected from mostly young women needs to be analysed to find out their reaction after Donald Trump was elected as the President of America. The examination of various reactions that outpoured in the social media and the streets of USA as well as across the world. The protest is documented well and evaluated as per the age group of the women. All analytics of social media is used to conclude the results of this research. Further, recommendations are also made on how this sample size is selected in order to get a better and
reliable data.

Suggested Readings:
A. Alashri, S., Kandala, S. S., Bajaj, V., Ravi, R., Smith, K. L., & Desouza, K. C. (2016, August). An analysis of sentiments on facebook during the 2016 US presidential election. In Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on (pp. 795-802). IEEE.
B. Lilleker, D., Jackson, D., Thorsen, E., & Veneti, A. (2016). US Election Analysis 2016: Media, Voters and the Campaign.

9. Forecasting students’ performance in their terminal examination using multilayer perceptron and linear regression.

Here the students’ performance is tracked using various statistical methods. These methods help in finding out specifics about each and every student. These particulars are essential if one has to analyse and come out with a statistical model regarding the students’ performance in their final examination. The use of linear regression would aid in formulating an equation with the dependent variable in this study. In this case, there can be two binary values – pass and fail. Moreover, the degree of performance – grades, can also be analysed during this study.

Suggested Readings:
A. Bhardwaj, B. K., & Pal, S. (2012). Data Mining: A prediction for performance improvement using classification. arXiv preprint arXiv:1201.3418.
B Cheng, C. K., Paré, D. E., Collimore, L. M., & Joordens, S. (2011). Assessing the effectiveness of a voluntary online discussion forum on improving students’ course performance. Computers & Education, 56(1), 253-261.

10. Analysing how the use of data mining classification techniques can help to identify risk factors as well as diagnose ovarian cancer recurrence.

Here, the study is mainly centred around how the machine learning can help in enhancing the existing diagnosis of ovarian cancer. The data mining techniques used in bioinformatics and biomedicine would help in reducing the intensity of cancer as well as its treatment. The data collected in this regard is analysed in detail to come up with a concrete conclusion, thereby, providing better options for cancer treatment. The application of machine learning using artificial neutral networks and biomarker are also studied to find out reliable data. Some recommendations are also made in order to highlight future avenues of research in this field.

Suggested Readings:
A. Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8-17.
B. Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F., & Hua, L. (2012). Data mining in healthcare and biomedicine: a survey of the literature. Journal of medical systems, 36(4), 2431-2448.