Date: Jan 23-27, 2023
Keywords: Data Science, Big Data, Trends, Analysis, Cleaning, Packaging data, and Cloud Computing (Amazon AWS).
In this part, students will understand the overview of Data Science, covering a broad selection of key challenges in and methodologies for working with big data. Topics to be covered include data collection, integration, management, modeling, and visualization. Student will get understanding of status of NOAA and other agencies, their need of cloud computing related to weather and climate modelling. Student will learn basic understanding of data cleaning, identifying data trends, and data analysis. Students will be introduced to Python and Jupyter Notebook with sample examples of cloud computing.
Keywords: Algorithm Development, Computer programming, Model Building, Introduction to Models (Physical, Empirical, Look-up Tables, Statistical Models, etc.)
In this part, students will develop ability to articulate processes for solving problems and to implement those processes efficiently within program development. This part introduces mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. This part emphasizes the relationship between algorithms and programming and introduces basic performance measures and analysis techniques for these problems. Students will be also familiarized with type of models, Look-up tables, black-box modeling, statistical models, etc.
Keywords: Earth Science Modelling, Climate and Hydrological System, Calibration and Validation for Modeling and Product Development.
In this part, students will examine the interactions among the components of the Earth System, including the atmosphere, oceans, biosphere, solid Earth, and humans. The focus will be on viewing the Earth as a system, interactions between the various components, and the forcing’s and feedbacks that produce the environment in which we live. Emphasis will be placed on the global scale, physical climate, and hydrological system. Students will get familiar calibration and validation methodologies for physical modeling and product development.
Keywords: Remote Sensing Data Access, Processing, Statistical Analysis and Visualization, Creating Maps and Graphs. Geographical Information System (GIS).
In this part, students will learn remote sensing and in-situ data acquisition, processing, and analysis. Students will learn about basic concepts of Geographical Information System (GIS) and how the GIS is being used in the real world to support problem-solving and decision-making. Students will be able to use ArcMap, ArcCatalog, and Arc Toolbox and learn how to use GIS for analysis, and effectively present data in map layouts. Students will be able to create and manage spatial databases and produce well-designed maps for presentation. Student will learn about vector and raster formats used in GIS. Student will also learn about data visualization process to use communicate information clearly in graphical form and gain an overview of the concepts used to visualize data.
Topic 5: Introduction to Data Science Programing (Python and Machine Learning)
Keywords: Python, Jupyter Notebook, Anaconda Navigator, Python Libraries, Machine Learning.
In this part, students will learn basic python programing. Students will receive help in installation of Anaconda Navigator, Jupyter Notebook, and some need Python Libraries. The Machine Learning will be introduced to students with some sample codes of Random Forest, Regression Analysis, etc.
Geographic Information Systems (GIS): GIS is a computer-based tool that uses spatial data to analyze and solve real-world problems. This course is designed to introduce the student to the basic principles and techniques of GIS with as objective to develop an understanding of geographic space and how maps represent geographic space. A student will be able to read maps, as well as write about and discuss information gleamed from maps. By the end of the GIS module, students will be creating maps from sources both graphical and tabular data using ArcGIS. Students will be able to use GIS to build maps using data they collected during program. Students will present and discuss their mapping work based on data collected from different sources using PowerPoint presentation. You will be given assignments based on activities performed during the class. The examples of assignments includes: (1) importing data with latitude and longitude coordinates in GIS and creating maps, (2) Import excel or other data formats data in GIS and joining it Global or Local maps to create new maps using imported datasets.
Group GIS Project: Students are divided in several groups with 3-4 students in each group. In the GIS project, students are free to choose Earth System Science related topic, in consultation with the instructor. The project should have rich GIS elements. Students will be downloading data from various government website sources including: Census bureau, NOAA, NASA, USGS, DOL, World Bank, UN, and educational institutes, etc. The assessment in GIS project is based quality of GIS maps produced, presentation skills, effort in data processing and preparation, and analysis of data. Students will be creating ArcGIS StoryMaps to present their project. GIS data Resources
Link to upload your work: Google Link
|QGIS Installation Manual – Windows
|QGIS Installation Manual – MacOS
Data Science: Python
Download and Install Anaconda Navigator
Basic Programing and walkthrough Python