Introduction to Earth Data Analysis

  1. Get Started
    1. Learn Git/Github for team collaboration
    2. Install Python via Anaconda
    3. Install Python IDE (Jupyter Notebook, Visual Studio Code)
    4. Python variables and data types
    5. Install and use Python packages
    6. Get familiar with Numpy for data manipulations
    7. Get familiar with Matplotlib for plotting arrays
    8. Get familiar with Pandas to analyze tabular data
  2. Get familiar with text file formats
    1. Use basic Markdown syntax to format text in Jupyter Notebook files
    2. Get familiar with text file formats - CSV, .txt, YAML
    3. Read/Write data from text files using Python
    4. Read/Write data from text files using Pandas
    5. Deal with missing data in Pandas
  3. Spatial Data Analysis
    1. Read multi-layered raster data (.tif / .hdf / .nc) in Python
    2. Read vector data (shapefiles) using geopandas
    3. GIS in Python
  4. Time-series Analysis using Pandas
    1. Read time-series data
    2. Work with Datetime formats
    3. Resample time series
    4. Filtering/smoothing time-series with Pandas
    5. Plot time series
  5. How to plot topographic high-resolution geospatial maps
    1. Generic Mapping Tools (GMT-6) for high-quality topographic maps
    2. PyGMT: Easily plot GMT maps using Python
    3. Plot location data from the text file on a map, and clipping along coastlines
    4. Three-dimensional perspective map using GMT/PyGMT

Numerical methods for scientific computation

  1. Monte Carlo methods and earthquake location problem
  2. Monte Carlo Simulation to test for the correlation between two dataset
  3. Hypothesis test for the significance of linear trend using the Monte Carlo simulations
  4. Numerical tests for seismic resolution
  5. Least-Squares Method in Geosciences
  6. Genetic Algorithm: a highly robust inversion scheme for geophysical applications
  7. Iterative Newton–Raphson method to find roots of a function
  8. Exploratory Factor Analysis
  9. Principal Component Analysis To Decompose Signals and Reduce Dimensionality
  10. Empirical Orthogonal Function analysis to inspect the spatial coherency in the geospatial data