This course is designed to equip graduate students with essential programming skills using Python. Students will learn to write efficient and reproducible code for data processing, analysis, and visualization. Topics covered include programming fundamentals, python data structures, file handling, and using NumPy and Pandas for working with tabular and time series data. Students will engage directly with real-world datasets through guided tutorials, comprehensive Jupyter notebook examples, and interactive classroom discussions.

Topics covered:

1. Conda, Jupyter Notebook

2. Variables, String, Data Structures

3. Code blocks, Control statement

4. List comprehension, Functions

5. Lambda function, Class

6. Review and Exam #1

7. Numpy ndarrays, File I/O

8. Basic plotting

9. Pandas data structures

10. Data wrangling with Pandas

11. Datetime, Time series

12. Review and Exam #2

13. Scipy, Statistical distributions

14. Seaborn, Cartopy

This course is designed to equip students with essential skills and techniques to perform geospatial data analysis using Python. This course emphasizes both theoretical concepts and hands-on practice, offering comprehensive insights into spatial data handling, visualization, statistical analysis, and application to geospatial problems. The course combines lectures, hands-on exercises, and project-based assignments to facilitate active learning. Students will engage directly with real-world datasets through guided tutorials, comprehensive Jupyter notebook examples, and interactive classroom discussions.

Topics covered:

1. Conda, Installing pythongis environment

2. Pandas

3. Shapely, Geometry objects

4. Data models, Geopandas

5. Coordinate reference systems

6. Geocoding

7. Spatial query, Spatial join

8. Raster analysis

9. Overlay analysis

10. Terrain analysis

11. Spatiotemporal analysis

12. Aggregating raster with vector data

13. Spatial interpolation

14. Class project

This course provides a comprehensive introduction to Geographic Information Systems (GIS) and their applications in analyzing, visualizing, and interpreting spatial data. Students will gain both conceptual knowledge and practical skills essential for working with spatial data across a range of disciplines. Through a combination of interactive lectures and hands-on lab sessions using ArcGIS Pro, students will explore key GIS concepts such as map design, coordinate systems, spatial data models, georeferencing, spatial analysis, and data visualization.

Topics covered:

1. Course overview

2. Datum, Map projection, Coordinate system

3. Data models

4. Maps, Data entry and editing

5. Aerial and satellite images

6. Data input and digitization

7. Attribute data and tables

8. Basic spatial analysis

9. Raster analysis

10. Terrain analysis

11. Special topic on watershed delineation

12. Spatial interpolation

13. Cartographic modeling

14. Class project

This course provides a foundation for understanding the essential elements of atmospheric physics, including thermodynamics (e.g. adiabatic processes, phase transformations, stratification), aerosol and cloud microphysics (e.g. nucleation, Kohler theory, growth by condensation and collection), atmospheric electricity, and atmospheric radiation transfer (e.g. Beer's law, radiative transfer equations with and without scattering).

Topics covered:

1. Course overview

2. Atmospheric composition and structure

3. Hydrostatic balance and vertical structure

4. Thermodynamics of dry and moist air

5. Atmospheric stability

6. Aerosol sources and microphysics 

7. Cloud and precipitation processes

8. Review and Exam #1

9. Atmospheric radiation and energy balance

10. Surface energy balance and boundary layer

11. Atmospheric dynamics: equation of motion

12. General circulation and jet streams

13. Greenhouse effect and climate forcing

14. Review and Exam #2