| CODE | EMP1004 | |||||||||
| TITLE | Data Management and Processing | |||||||||
| UM LEVEL | 01 - Year 1 in Modular Undergraduate Course | |||||||||
| MQF LEVEL | Not Applicable | |||||||||
| ECTS CREDITS | 6 | |||||||||
| DEPARTMENT | Environmental Management and Planning | |||||||||
| DESCRIPTION | This study-unit aims to bridge mathematical and statistical content with application to various fields related to Earth Systems. Moreover, it endeavors to enhance the students’ problem-solving capabilities and their ability to seek out and utilize available data. Amongst other things, the unit will introduce students to sampling design, sampling techniques, descriptive statistics, graphical displays, and inferential statistics. Students will be introduced to a range of statistical tests include t-tests, ANOVA, regression analysis and chi-squared tests, using Earth Systems data. Students will also be introduced to the main components of typical data management systems, as well as to the importance of meta-data and of data quality control. The unit will also introduce data visualization methods as well as methods for quality control of data. The unit will further introduce students to the global distribution of data repositories of Earth Systems science data that are widely dispersed geographically. Using theory and practical case studies, students will be given the opportunity to become familiar with the varied range and format of Earth system data formats, enabling them to be able to collate and import these using specific analytical software, to produce seamless data and derived information. Amongst other things, the unit will introduce students to various relevant software packages including SPSS, Ocean Data View (ODV4), HDF viewers (for large scene satellite data basic processing) and PANOPLY (for plotting of binary data). Study-unit Aims The study-unit aims: - to develop students’ knowledge of mathematical and statistical concepts, and their ability to use these tools to explore and solve real-world problems; - to enable students to assess data quality; - to enable students to be aware of various options for data visualization; - to provide students with an understanding of the scope and diversity of Earth Systems science data and its management; - to help students develop an awareness of sources of relevant data, including base maps, atmospheric soundings and meteorological/terrestrial/oceanic measurements; - to enable students to become familiar with global general circulation models (GCMs) that are now instrumental in understanding Earth systems in the long-term. Learning Outcomes 1. Knowledge & Understanding By the end of the study-unit the student will be able to: a. Explain the elements of a reliable sampling design and strategy; b. Describe the structure of data management systems; c. Explain how data management systems will increase access and use of scientific data and reduce the threat of data being 'lost'; d. Describe how computing and models can lead to the assimilation and forecasting of earth phenomena, including anthropogenic processes. 2. Skills By the end of the study-unit the student will be able to: a. Collect data in relation to an identified problem, propose a related appropriate mathematical/statistical technique/model, and use this technique/model to solve the problem; b. Select appropriate statistical techniques for data analysis; c. Apply mathematics and statistics to make predictions and inferences; d. Derive, analyze and assess relationships between variables; e. Upon being given an Earth Systems data set, construct a corresponding meta-data record in the appropriate formats that will adequately describe that data set, so that the data set can be made accessible within a data management system; f. Perform and adopt basic Earth systems data processing/management in relation to (i) geospatial data and remote sensing, (ii) climate change analysis, (iii) general circulation models, and (iv) data visualization; g. Develop algorithms to blend data coming from various sensor platforms to enhance temporal and spatial content. Main Text/s and any supplementary readings: Main Texts Freund, J.E. & Simon G.A., 2004. Statistics - A First Course. Prentice Hall Inc. 8th Edition. ISBN: 978-013-046-653-2. Chandrika Kamath, 2009. Scientific Data Mining - A Practical Perspective. Society for Industrial and Applied Mathematic. ISBN: 978-089-871-675-7. Haining, R., 2003. Spatial Data Analysis: Theory and Practice. Cambridge University Press. 1st edition. ISBN: 978-052-177-437-6. Henderson, P. & Henderson, G. M., 2009. The Cambridge Handbook of Earth Science Data. Cambridge University Press. 1st edition. ISBN: 978-052-169-317-2. Abiteboul, S., Manolescu, I., Rigaux, P., Rousset, M. & Senellart, P., 2011. Web Data Management. Cambridge University Press. 1st edition. ISBN: 978-110-701-243-1. Candan, S.K. & Sapiono, M.C., 2010. Data Management for Multimedia Retrieval. Cambridge University Press. 1st edition. ISBN: 978-052-188-739-7. Stopher, P., 2011. Collecting, Managing, and Assessing Data Using Sample Surveys. University of Sydney. 1st edition. ISBN: 978-052-168-187-2. Supplementary texts Emery, W.J. & Thomson, R.E., 2001. Data Analysis Methods in Physical Oceanography. Elsevier Science & Technology Books. 3rd Edition. ISBN: 978-0444507570. Starck, J., 1998. Image Processing and Data Analysis: The Multiscale Approach. Cambridge University Press. 1st edition. ISBN: 978-0521599146. Lightstone, S., Teorey, T. & Nadeau T., 2007. Physical Database Design: The Database Professional's Guide to Exploiting Indexes, Views, Storage and More. Morgan Kaufmann. 4th Edition. ISBN: 978-0123693891. Ross Sheldon, M., 2010. Introductory Statistics. Academic Press. 3rd Revised Edition. ISBN: 9780123743886. Wardlaw, A.C., 1999. Practical Statistics for Experimental Biologists. Wiley. 2nd Edition. ISBN: 0-471-98822-7. Bryman, A. & Cranmer, D., 1997. Quantitative Data Analysis with SPSS for Windows. A Guide for Social Scientists. Routledge. 1st Edition. ISBN: 0-415-14720-4. Elmasri, R., 2007. Fundamentals of Database Systems. Pearson. 5th Edition. ISBN: 0-321-41506-X. Seelye, M., 2004. An Introduction to Ocean Remote Sensing. Cambridge University Press. ISBN: 978-052-180-280-2. Electronic Sources: OceanColor webpage: https://oceancolor.gsfc.nasa.gov/ System for Automated Geoscientific Analysis: http://saga-gis.org/en/index.html The Principles of Good Data Management (NERC): http://www.bgs.ac.uk/oml/docs/Good_dataV1.pdf Proceedings of the International Conference on Marine Data and Information Systems (IMDIS 2010), Paris France, March 2010: http://www.seadatanet.org/imdis2010/presentations Proceedings of the International Conference on Marine Data and Information Systems (IMDIS 2008), Athens Greece, March - April 2008: http://hnodc.hcmr.gr/imdis-2008/presentations.htm Proceedings of the International Conference on Marine Data and Information Systems (IMDIS 2005), Brest, France, May-June 2005: http://www.ifremer.fr/imdis/ |
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| STUDY-UNIT TYPE | Lecture and Practicum | |||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Joel Azzopardi Liberato Camilleri Charles Galdies (Co-ord.) Adam Gauci |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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