Computational Environments and Computational Skills Expected
The workshop will offer a half day of training in your choice of either the Python or R languages. For those who are new to computing with atmospheric science datasets, we recommend the
Anaconda Distribution (choose Python 3.7)
The distribution includes both Python and R languages and there is extensive documentation of how to get started with either Python or R.
We also have mentors who are skilled in Matlab, Mathematica, Fortran who can offer assistance in these languages if one of these is your preferred environment.
Please note, though, that regardless of which of these environments you plan to use, you will need to come to the workshop with some level of computational skills and there will only be an organized review provided in Python and R during the workshop. For those working in different computational environments, one-on-one review instruction will be available for Matlab, Mathematica and Fortran.
Computational skills expected prior to the start of the workshop:
The workshop will offer a half day of training in your choice of either the Python or R languages. For those who are new to computing with atmospheric science datasets, we recommend the
Anaconda Distribution (choose Python 3.7)
The distribution includes both Python and R languages and there is extensive documentation of how to get started with either Python or R.
We also have mentors who are skilled in Matlab, Mathematica, Fortran who can offer assistance in these languages if one of these is your preferred environment.
Please note, though, that regardless of which of these environments you plan to use, you will need to come to the workshop with some level of computational skills and there will only be an organized review provided in Python and R during the workshop. For those working in different computational environments, one-on-one review instruction will be available for Matlab, Mathematica and Fortran.
Computational skills expected prior to the start of the workshop:
- read in ASCII, hdf, netcdf, etc. data formats
- manage time fields in ASCII data sets
- perform simple statistical analysis on the data (mean, standard deviation, weighted and unweighted best fit regressions, etc.)
- plot analyzed data versus time
- correlate data sets from different instruments adjusting for different averaging times, etc.