Courses
Introduction to Earth Data Analysis
- Get Started
- Learn Git/Github for team collaboration
- Install Python via Anaconda
- Install Python IDE (Jupyter Notebook, Visual Studio Code)
- Python variables and data types
- Install and use Python packages
- Get familiar with Numpy for data manipulations
- Get familiar with Matplotlib for plotting arrays
- Get familiar with Pandas to analyze tabular data
- Get familiar with text file formats
- Use basic Markdown syntax to format text in Jupyter Notebook files
- Get familiar with text file formats - CSV, .txt, YAML
- Read/Write data from text files using Python
- Read/Write data from text files using Pandas
- Deal with missing data in Pandas
- Spatial Data Analysis
- Read multi-layered raster data (.tif / .hdf / .nc) in Python
- Read vector data (shapefiles) using geopandas
- GIS in Python
- Time-series Analysis using Pandas
- Read time-series data
- Work with
Datetimeformats - Resample time series
- Filtering/smoothing time-series with Pandas
- Plot time series
- How to plot topographic high-resolution geospatial maps
Numerical methods for scientific computation
- Monte Carlo methods and earthquake location problem
- Monte Carlo Simulation to test for the correlation between two dataset
- Hypothesis test for the significance of linear trend using the Monte Carlo simulations
- Numerical tests for seismic resolution
- Least-Squares Method in Geosciences
- Genetic Algorithm: a highly robust inversion scheme for geophysical applications
- Iterative Newton–Raphson method to find roots of a function
- Exploratory Factor Analysis
- Principal Component Analysis To Decompose Signals and Reduce Dimensionality
- Empirical Orthogonal Function analysis to inspect the spatial coherency in the geospatial data
