Big Data
C7084 Big Data and Decision Making
Welcome to C7084. This module is a survey of tools to store, access and analyse “big data”. There is an emphasis on practical applications using SQL and lab exercises, along with lectures on selected topics.
The material is designed to be experienced in a one week format followed by an assessment meant to showcase practical skills. The work will be supported with live sessions during the main week of delivery.
Resources
Lectures and related material .zip file ::: lecture 05 movies db (100ish MB)
Labs and related material .zip file
Optional readings
Readings books :: Readings articles
Books Gupta, V., 2022. Business Intelligence with Databricks SQL: Concepts, tools, and techniques for scaling business intelligence on the data lakehouse. Packt Publishing.
Pettit, T., Cosentino, S., 2022. The MySQL Workshop: A practical guide to working with data and managing databases with MySQL. Packt Publishing.
Teate, R.M.P., 2021. SQL for Data Scientists: A Beginner’s Guide for Building Datasets for Analysis, 1st edition. ed. Wiley, Indianapolis.
Articles Amani, M., Ghorbanian, A., Ahmadi, S.A., Kakooei, M., Moghimi, A., Mirmazloumi, S.M., Moghaddam, S.H.A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., Brisco, B., 2020. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, 5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052
Cravero, A., Sepúlveda, S., 2021. Use and Adaptations of Machine Learning in Big Data—Applications in Real Cases in Agriculture. Electronics 10, 552. https://doi.org/10.3390/electronics10050552
Guo, H., 2017. Big Earth data: A new frontier in Earth and information sciences. Big Earth Data 1, 4–20. https://doi.org/10.1080/20964471.2017.1403062
Kamilaris, A., Kartakoullis, A., Prenafeta-Boldú, F.X., 2017. A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037
Northcott, R., 2020. Big data and prediction: Four case studies. Studies in History and Philosophy of Science Part A 81, 96–104. https://doi.org/10.1016/j.shpsa.2019.09.002
Pham, X., Stack, M., 2018. How data analytics is transforming agriculture. Business Horizons 61, 125–133. https://doi.org/10.1016/j.bushor.2017.09.011
Runting, R.K., Phinn, S., Xie, Z., Venter, O., Watson, J.E.M., 2020. Opportunities for big data in conservation and sustainability. Nature Communications 11, 2003. https://doi.org/10.1038/s41467-020-15870-0
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B., 2020. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing 164, 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001
Harper Adams Data Science
This module is a part of the MSc in Data Science for Global Agriculture, Food, and Environment at Harper Adams University, led by Ed Harris.