Unverified Commit 30f9179a authored by Simon Bowly's avatar Simon Bowly
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Add course maps to readme.

Static site build commands.
parent 30dd8484
...@@ -2,28 +2,61 @@ ...@@ -2,28 +2,61 @@
## By Topic ## By Topic
* [Project-Tools](./Project-Tools) * [Project-Tools](Project-Tools)
* [Git](./Project-Tools/Git) * [Git](Project-Tools/Git)
* [Python-Jupyter](./Python-Jupyter) * [Python-Jupyter](Python-Jupyter)
* [Pandas-DataFrames](./Pandas-DataFrames) * [Pandas-DataFrames](Pandas-DataFrames)
* [Merging](./Pandas-DataFrames/Merging) * [Merging](Pandas-DataFrames/Merging)
* [Time-Series](./Pandas-DataFrames/Time-Series) * [Time-Series](Pandas-DataFrames/Time-Series)
* [Incoming-Survey](./Pandas-DataFrames/Incoming-Survey) * [Incoming-Survey](Pandas-DataFrames/Incoming-Survey)
* [Visualisation](./Visualisation) * [Visualisation](Visualisation)
* [Machine-Learning](./Machine-Learning) * [Machine-Learning](Machine-Learning)
* [Unsupervised-Methods](./Machine-Learning/Unsupervised-Methods) * [Unsupervised-Methods](Machine-Learning/Unsupervised-Methods)
* [Principal-Component-Analysis](./Machine-Learning/Unsupervised-Methods/Principal-Component-Analysis) * [Principal-Component-Analysis](Machine-Learning/Unsupervised-Methods/Principal-Component-Analysis)
* [K-Means-Clustering](./Machine-Learning/Unsupervised-Methods/K-Means-Clustering) * [K-Means-Clustering](Machine-Learning/Unsupervised-Methods/K-Means-Clustering)
* [Supervised-Methods](./Machine-Learning/Supervised-Methods) * [Supervised-Methods](Machine-Learning/Supervised-Methods)
* [Regression](./Machine-Learning/Supervised-Methods/Regression) * [Regression](Machine-Learning/Supervised-Methods/Regression)
* [Decision-Trees](./Machine-Learning/Supervised-Methods/Decision-Trees) * [Decision-Trees](Machine-Learning/Supervised-Methods/Decision-Trees)
* [Support-Vector-Machines](./Machine-Learning/Supervised-Methods/Support-Vector-Machines) * [Support-Vector-Machines](Machine-Learning/Supervised-Methods/Support-Vector-Machines)
* [K-Nearest-Neighbours](./Machine-Learning/Supervised-Methods/K-Nearest-Neighbours) * [K-Nearest-Neighbours](Machine-Learning/Supervised-Methods/K-Nearest-Neighbours)
* [Ensemble-Methods](./Machine-Learning/Ensemble-Methods) * [Ensemble-Methods](Machine-Learning/Ensemble-Methods)
* [Boosting](./Machine-Learning/Ensemble-Methods/Boosting) * [Boosting](Machine-Learning/Ensemble-Methods/Boosting)
* [Topic-Hints](./Topic-Hints) * [Topic-Hints](Topic-Hints)
* [Optimization](./Topic-Hints/Optimization) * [Optimization](Topic-Hints/Optimization)
* [Brain-Scans](./Topic-Hints/Brain-Scans) * [Brain-Scans](Topic-Hints/Brain-Scans)
* [Time-Lag-Features](./Topic-Hints/Time-Lag-Features) * [Time-Lag-Features](Topic-Hints/Time-Lag-Features)
* [Image-Features](./Topic-Hints/Image-Features) * [Image-Features](Topic-Hints/Image-Features)
* [Stock-Portfolios](./Topic-Hints/Stock-Portfolios) * [Stock-Portfolios](Topic-Hints/Stock-Portfolios)
## By Unit
### ADS1001
* Week 1 [Intro to Python 1](Python-Jupyter/01-PythonIntroduction.ipynb)
* Week 2 [Intro to Python 2](Python-Jupyter/02-PythonIntroduction.ipynb)
* Week 3 [Python Modules](Python-Jupyter/03-PythonModules.ipynb)
* Week 4 [Plotting with Matplotlib](Visualisation/04-PlottingMPL_Intro.ipynb)
* Week 5 [Intro to Pandas](Pandas-DataFrames/05-PandasWeather.ipynb)
* Week 6 [Plotting with Seaborn](Visualisation/06_Seaborn.ipynb)
* Week 7 [Aggregating and Grouping Data](Pandas-DataFrames/07-AggregationGrouping.ipynb)
* Week 8 [Merging and Joining Data](Pandas-DataFrames/Merging/04-MergeJoinIntro.ipynb)
* Week 9 [Intro to Linear Regression](Machine-Learning/Supervised-Methods/Regression/03-IntroLinearRegression.ipynb)
* Week 10 [Intro to Linear Regression (Dupe?)](Machine-Learning/Supervised-Methods/Regression/03-IntroLinearRegression.ipynb)
* Week 11 [K Nearest Neighbours (kNN) Classification](Machine-Learning/Supervised-Methods/K-Nearest-Neighbours/12-kNN.ipynb)
### ADS1002
### ADS2001
* Week 2 [Regression Workflow](Machine-Learning/Supervised-Methods/Regression/01-ML_Workflow_Diabetes.ipynb)
* Week 3 [Logistic Regression](Machine-Learning/Supervised-Methods/Regression/02-LogisticRegression_Iris.ipynb)
* Week 4 [Support Vector Machines (SVM)](Machine-Learning/Supervised-Methods/Support-Vector-Machines/03-SVM.ipynb)
* Week 5 [Decision Trees](Machine-Learning/Supervised-Methods/Decision-Trees/04-DecisionTrees.ipynb)
* Week 6 [Random Forests](Machine-Learning/Supervised-Methods/Decision-Trees/05-RandomForest.ipynb)
* Week 7 [Principal Component Analysis (PCA)](Machine-Learning/Unsupervised-Methods/Principal-Component-Analysis/08-PCA.ipynb)
* Week 8 [K-Means Clustering](Machine-Learning/Unsupervised-Methods/K-Means-Clustering/KMeans.ipynb)
* Week 9 [Git](Project-Tools/Git/GIT-CLI-CORE.pdf)
* Week 10 [Advanced Plotting](Visualisation/11-AdvancedPlotting.ipynb)
* Week 11 [Boosting](Machine-Learning/Ensemble-Methods/Boosting/Boosting.ipynb)
### ADS2002
# Data Challenges: Course Maps
## ADS1001 (2021)
* Week 1: [03-BeginnerToAdvanced/01-PythonIntroduction](03-BeginnerToAdvanced/01-PythonIntroduction.html)
* Week 2: [03-BeginnerToAdvanced/02-PythonIntroduction](03-BeginnerToAdvanced/02-PythonIntroduction.html)
* Week 3: [03-BeginnerToAdvanced/03-PythonModules](03-BeginnerToAdvanced/03-PythonModules.html)
* Week 4: [03-BeginnerToAdvanced/04-PlottingMPL_Intro](03-BeginnerToAdvanced/04-PlottingMPL_Intro.html)
* Week 5: [03-BeginnerToAdvanced/05-PandasWeather](03-BeginnerToAdvanced/05-PandasWeather.html)
* Week 6: [03-BeginnerToAdvanced/06_Seaborn](03-BeginnerToAdvanced/06_Seaborn.html)
* Week 7: [03-BeginnerToAdvanced/07-AggregationGrouping](03-BeginnerToAdvanced/07-AggregationGrouping.html)
* Week 8: [04-Merging/04-MergeJoinIntro](04-Merging/04-MergeJoinIntro.html)
* Week 9: [05-LinearRegression/03-IntroLinearRegression](05-LinearRegression/03-IntroLinearRegression.html)
* Week 10: [05-LinearRegression/03-IntroLinearRegression](05-LinearRegression/03-IntroLinearRegression.html)
* Week 11: [12-kNN/12-kNN](12-kNN/12-kNN.html)
## ADS2001 (2021)
* Week 2: [05-LinearRegression/01-ML_Workflow_Diabetes](05-LinearRegression/01-ML_Workflow_Diabetes.html)
* Week 3: [05-LinearRegression/02-LogisticRegression_Iris](05-LinearRegression/02-LogisticRegression_Iris.html)
* Week 4: [06-SVM/03-SVM](06-SVM/03-SVM.html)
* Week 5: [07-DecisionTrees/04-DecisionTrees](07-DecisionTrees/04-DecisionTrees.html)
* Week 6: [07-DecisionTrees/05-RandomForest](07-DecisionTrees/05-RandomForest.html)
* Week 7: [08-PCA/08-PCA](08-PCA/08-PCA.html)
* Week 8: [09-KMeans/KMeans](09-KMeans/KMeans.html)
* Week 9: 10-Git/GIT%20CLI%20CORE.pdf
* Week 10: [11-Plotting/11-AdvancedPlotting](11-Plotting/11-AdvancedPlotting.html)
* Week 11: [13-Boosting/Boosting](13-Boosting/Boosting.html)
### Create web version
```
sed -nE "s|.*\ [(.*)\].*|\1.ipynb|p" course-map.md | xargs -I {} jupyter nbconvert --to html {}
pandoc --from markdown --to html course-map.md -o course-map.html
```
# Some useful commands for managing the repository
Build (approximately) the topic index in the main readme:
```
find . -type d -not -path "*/.ipynb_checkpoints" -not -path "*/__pycache__" | sed -En "s|(.*)\/([a-zA-Z0-9'-]*)$|* [\2](\1/\2)|p"
```
Build a static html site from all notebooks with their current outputs, using readme as an index page:
```
pandoc --from markdown --to html README.md -o static/index.html
find . -type f -name "*.ipynb" | xargs -I {} bash -c 'f="{}" && mkdir -p -- static/"${f%/*}" && jupyter nbconvert --to html $f && mv "${f%.*}.html" static/"${f%/*}"'
```
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