CODE | IES5039 | ||||||||||||
TITLE | Analytical Methods and Techniques in Data Science | ||||||||||||
UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||
MQF LEVEL | 7 | ||||||||||||
ECTS CREDITS | 6 | ||||||||||||
DEPARTMENT | Institute of Earth Systems | ||||||||||||
DESCRIPTION | This study-unit develops the skills and conceptual framework that enable students to become effective practitioners in the use of data to solve practical problems. The topics will include specialized analytical methods and techniques such as exploratory data analysis, data wrangling, descriptive statistics, time series analysis, modeling and simulation, machine learning, regression, classification, hypothesis testing, and data visualization. Students complete structured instructional modules focused on methods of collecting, analyzing and interpreting data relevant to fields in integrated science and technology. Skills for data exploration, analysis and project management will be developed through analysis of real-world datasets using Python and Jupyter Notebooks (both locally and in the cloud) and GitHub. Study-Unit Aims: - Develop students’ ability to build conceptual, technical and communication skills to investigate and answer environmental questions using data; - Develop students’ ability to create, construct, or acquire data (quantitative, qualitative, spatial, and/or visual) and organize it for appropriate analysis; - Develop students’ ability to create machine learning models that enable predictions from big, complex data into actionable insights; - Develop students’ ability to analyze data and draw conclusions from it; - Enable students to develop in-depth knowledge of specialized analytical techniques relating to specific sub-topics in applied data science; - Develop students’ ability to work independently, under guidance. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Identify and characterize analytical techniques that are used within the full range subject areas associated with the integrated science and technologies; - Explain how these contribute to advancing the goals of policy and technical management associated with integrated science and technology; - Explain when, where and how such analytical techniques can be appropriately used - Discuss the limitations of these techniques. 2. Skills: By the end of the study-unit the student will be able to: - Identify data requirements for use with specific analytical techniques; - Follow established procedures in gathering and organizing data and applying analytical methods; - Work through examples using specified analytical methods; - Apply methods to specific problem-solving scenarios; - Draw conclusions on the basis of the analysis conducted using these techniques. Main Text/s and any supplementary readings: - http://neuralnetworksanddeeplearning.com/index.html ( by Michael Nielsen. Online and Free -We will use this as a resource - Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition by Sebastian Raschka (Author), Vahid Mirjalili (Author) Packt Publishing (December 12, 2019) ISBN-10 :1789955750 ISBN-13 : 978-1789955750 - Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing 1st Edition by Ni-Bin Chang (Author), Kaixu Bai (Author) CRC Press; 1st edition (June 30, 2020 ISBN-10:0367571978 ISBN-13:978-0367571979 - Introduction to Remote Sensing, Fifth Edition 5th Edition by James B. Campbell (Author), Randolph H. Wynne (Author) The Guilford Press; 5th edition (June 21, 2011) ISBN-10 : 160918176X ISBN-13 : 978-1609181765 - Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels 1st Edition P Cambridge University Press; 1st edition (August 31, 2009) ISBN-10 : 0521791928 ISBN-13 : 978-0521791922 - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition O'Reilly Media; 2nd edition (October 15, 2019) ISBN-10 : 1492032646 ISBN-13 : 978-1492032649 |
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ADDITIONAL NOTES | ** Resits will be held during the period indicated by the partner institution responsible for the study-unit in accordance with the regulations applicable at the respective institution. | ||||||||||||
STUDY-UNIT TYPE | Lecture and Laboratory Session | ||||||||||||
METHOD OF ASSESSMENT |
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LECTURER/S | Stefano Colafranceschi Adam Gauci (Co-ord.) |
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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 2025/6. It may be subject to change in subsequent years. |