Please read and study the entire online book, “Data Mining Tutorial- Introduction to Data Mining (Complete Guide).” This is a wonderful look at the major topics and tools of modern data mining studies. You will be doing much of what is discussed in the book in this class and we will continue much of the classic data science components of this book in the next class in this sequence (IS 5213 – Data Science and Big Data). You are to discuss your thoughts about this reading. Please discuss what experience you have with data mining and data visualization, what you would like to learn from these topics, and where you find your future in the field of Data Science.
Submit your answers in 1-2 page word document to this assignment.
Data mining involves finding patterns, correlations, and anomalies that exist within large data sets to end up predicting an outcome. Data visualization involves the practice of translation to incorporate visual context in terms of graphs and maps. This makes data easier for humans to understand and hence puts insights from them. The major goal of data visualization is ensuring ease of identification of patterns and trends present in large data sets. I can attest that I have several data visualization skills that can help with data analytics. This is such as asking tough questions, developing audience understanding, having basic visual design skills as well as the practice of rapid prototyping. Data visualization is important in reflecting its importance in data mining.
I have come across some data visualization tools which are very helpful. AWS removes the complexities that come with training, building, and deploying machine learning models that occur at any scale. Tableu exists but may be costly that helps in visualization tools of python, is open source, and corresponds in several libraries and visualizations. Another popular data mining tools that I find interesting are such as the RapidMiner which is a prototype used in discoveries that works in windows and Linux. Microsoft SQL server 2005, KNIME and clementine features are of great significance in data mining (Rizvee et al2021). I feel each data analyst should learn more than one tool to use in procedures and processes that would involve the same. However, I would like to learn more about data mining and visualization shortly to improve my analyst skills and boost my career as well. As I learn, I would wish to take part in data mining competitions, learn such software, check on available data resources and learn its suites.
After learning many of the concepts and putting them to use, I expect an improvement and better involvement in my future career in the field of data science. I would imply my knowledge in various fields that need data science to upgrade and function better to make the world a better place. This in turn is such as future healthcare, education, market basket analysis, and fraud detection. These are the fields in data science that I would focus most on since they carry more weight and affect many people as well. Data mining would play a major role in healthcare since it holds great potential to improve these systems (Leung et al,2021). This is used in improving care and in reducing costs and identifying best practices to use. Researchers make use of multi-dimensional databases such as soft computing, statistics, and data visualization. It would also impact education with educational data mining to develop methods to help in discovering knowledge and in predicting students’ future learning behaviors. Traditional methods of fraud detection have been failing over a long period which is why data mining would impact the process, hence my future involvement in it.
Leung, C. K., Kaufmann, T. N., Wen, Y., Zhao, C., & Zheng, H. (2021, September). Revealing COVID-19 data by data mining and visualization. In International Conference on Intelligent Networking and Collaborative Systems (pp. 70-83). Springer, Cham.
Rizvee, M., Amiruzzaman, M., & Islam, M. (2021). Data mining and visualization to understand accident-prone areas. In Proceedings of International Joint Conference on Advances in Computational Intelligence (pp. 143-154). Springer, Singapore.
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