Re-work tutorial to make it more accessible for self-study

Author: flrs

README.md

@@ -1,27 +1,26 @@
 # Predicting the Wind: Data Science in Wind Resource Assessment
 
-This repository contains a presentation and additional content about data science in wind resource assessment. The
-presentation was held on 2020-03-11 at the [PyData San Diego Meetup](https://www.meetup.com/PyData-San-Diego/).
+This repository contains material for a hands-on tutorial and additional content about data science in wind resource 
+assessment. The tutorial was held on 2020-03-11 at the [PyData San Diego Meetup](https://www.meetup.com/PyData-San-Diego/).
 
-### Presentation
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/flrs/predicting_the_wind/master?filepath=presentation.ipynb) *(recommended start)* – [presentation.ipynb](./presentation.ipynb) 
+### Tutorial
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/flrs/predicting_the_wind/master?filepath=tutorial_for_self_study.ipynb) *(recommended start)* – [tutorial_for_self_study.ipynb](./tutorial_for_self_study.ipynb) 
 
 <img src="./images/presentation_thumb.gif" width="400px" alt="Thumbnail of presentation"></img>
 
-*Update 2020-03-15: Added information about how to estimate turbine power output with long-term wind estimates.*
-
-Presentation about data science in wind resource assessment. The presentation is about the fictitious scenario of 
-building a wind farm on the hills around the AI incubator [The Sandbox San Diego](https://www.thesandbox.ai/). It 
-explains how the wind can be measured and how the measurement can be used together with climate models and ground 
-station data to generate a long-term estimate of the wind. Subsequently, the presentation shows how to use that estimate
- to predict wind turbine power output. Finally, the presentation puts the output of the fictitious wind farm into the
- broader context of the California power grid.
+Hands-on tutorial about data science in wind resource assessment. The tutorial is about the fictitious scenario 
+of building a wind farm on the hills around the AI incubator [The Sandbox San Diego](https://www.thesandbox.ai/). Using
+Python code, it explains how the wind can be measured and how the measurement can be used together with climate models 
+and ground station data to generate a long-term estimate of the wind. Subsequently, the tutorial explores how to use 
+that estimate to predict wind turbine power output. Finally, the tutorial puts the output of the fictitious wind 
+farm into the broader context of the California power grid.
 
-From a data science perspective, the presentation touches on data exploration, modeling and validation, and clarifies
+From a data science perspective, the tutorial touches on data exploration, modeling and validation, and clarifies
  where domain knowledge of wind and fluid dynamics can help improve the wind estimate.
 
-The presentation uses the following tool stack:
- - [Jupyter Notebook](https://jupyter.org/) as a basis 
+The tutorial uses the following tool stack:
+ - [Python](https://www.python.org/) for performing calculations
+ - [Jupyter Notebook](https://jupyter.org/) as an IDE 
  - [RISE Jupyter extension](https://rise.readthedocs.io/) for presenting the notebook 
  - [hide_code extension](https://github.com/kirbs-/hide_code) for hiding some (long-ish) code to build maps 
  - [Folium](https://python-visualization.github.io/folium/) to display maps
@@ -35,19 +34,26 @@ The presentation uses the following tool stack:
 <img src="./images/map_and_12x24.png" width="400px" alt="Map of San Diego and 12x24 wind speed matrix"></img>
 
 A notebook about how to download and preprocess meteorological data from ASOS measurement stations with Python 
-in a Jupyter notebook. In addition to showing how to download the data, the notebook also shows how to quickly produce a 12x24 plot with matplotlib and pandas.
+in a Jupyter notebook. In addition to showing how to download the data, the notebook also shows how to quickly produce 
+a 12x24 plot with matplotlib and pandas.
 
 ### Additional Content: Downloading ERA5 Data in Python
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/flrs/predicting_the_wind/master?filepath=data_acquisition%2Fdownloading_ERA5_data_in_python.ipynb) – [downloading_ERA5_data_in_python.ipynb](./data_acquisition/downloading_ERA5_data_in_python.ipynb)
 
 <img src="./images/temporal_wind_pattern.png" width="400px" alt="Temporal Wind Pattern"></img>
 
-A notebook that shows how to download and transform ERA5 reanalysis data from the [Copernicus Climate Change Service](https://cds.climate.copernicus.eu/cdsapp#!/home) in Python. The notebook is a classical example of an ETL process and includes some interesting wind data exploration snippets.
+A notebook that shows how to download and transform ERA5 reanalysis data from the 
+[Copernicus Climate Change Service](https://cds.climate.copernicus.eu/cdsapp#!/home) in Python. The notebook is a 
+classical example of an ETL process and includes some interesting wind data exploration snippets.
 
 ### Additional Content: Synthesizing a (Mock) Wind Speed Time Series
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/flrs/predicting_the_wind/master?filepath=data_acquisition%2Fsynthesizing_a_wind_speed_time_series.ipynb) – [synthesizing_a_wind_speed_time_series.ipynb](./data_acquisition/synthesizing_a_wind_speed_time_series.ipynb)
 
 <img src="./images/met_series.png" width="400px" alt="Wind speed and direction time series plot"></img>
 
 A Jupyter notebook that deals with how to come up with an artificial met mast time series when you only have some 
-reference data and not a lot of time.
+reference data and not a lot of time.
+
+### Changelog
+- 2020-11-21: Re-worked tutorial to make it more accessible for self-study.
+- 2020-03-15: Added information about how to estimate turbine power output with long-term wind estimates.*