Data Science In Practice

The Data Science in Practice module provides students with an opportunity to apply their knowledge through project work. They will select a project from a specific domain and appropriately apply exploratory data analysis, statistical methods and select appropriate advanced modelling techniques such as regression analysis, machine learning algorithms or . This module also develops your scientific communication skills through the preparation of project reports and presentations.



Reading List

(General):

• Gao, G., Mishra, B., & Ramazzotti, D. (2018). Causal data science for financial stress testing. J. Comput. Sci., 26, 294-304.
• Chen, H., Lundberg, S.M., & Lee, S. (2018). Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data. ArXiv, abs/1801.07384.

(Machine Learning):

• Miller, James D. and Rui Miguel Forte. Mastering Predictive Analytics with R: Machine Learning Techniques For Advanced Models. Second Ed. Birmingham: Packt, 2017.
• Fuentes, Alvaro. Mastering Predictive Analytics with Python. Birmingham: Packt, 2018.

(Data Analytics in Business):

• Hands-On Exploratory Data Analysis with R: Become an Expert in Exploratory Data Analysis Using R Packages, Radhika Datar and Harish Garg, 1st Edition. (Packt Publishing, 2019). 266 pages
• Hands-On Exploratory Data Analysis with Python: Perform EDA Techniques to Understand, Summarize, and Investigate Your Data, Suresh Kumar Mukhiya and Usman Ahmed, 1st Edition. (Packt Publishing, 2020). 352 pages.