# Bachelor of Applied Data Science: Data Challenges Unit Resources

## Sections by Topic

* [Project Tools](Project-Tools)
* [Python & Jupyter](Python-Jupyter)
* [Pandas & DataFrames](Pandas-DataFrames)
* [Visualisation](Visualisation)
* [Machine Learning](Machine-Learning)
    * [Unsupervised Methods](Machine-Learning/Unsupervised-Methods)
    * [Supervised Methods](Machine-Learning/Supervised-Methods)
        * [Regression](Machine-Learning/Supervised-Methods/Regression)
        * [Decision Trees](Machine-Learning/Supervised-Methods/Decision-Trees)
    * [Ensemble Methods](Machine-Learning/Ensemble-Methods)
* [Topic Hints](Topic-Hints)
    * [Optimization](Topic-Hints/Optimization)
    * [Brain Scans](Topic-Hints/Brain-Scans)
    * [Time Lag Features](Topic-Hints/Time-Lag-Features)
    * [Image Features](Topic-Hints/Image-Features)
    * [Stock Portfolios](Topic-Hints/Stock-Portfolios)

## Weekly Notebooks 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/Matplotlib-Intro.ipynb)
* Week 5 [Intro to Pandas](Pandas-DataFrames/05-PandasWeather.ipynb)
* Week 6 [Plotting with Seaborn](Visualisation/Seaborn.ipynb)
* Week 7 [Aggregating and Grouping Data](Pandas-DataFrames/07-AggregationGrouping.ipynb)
* Week 8 [Merging and Joining Data](Pandas-DataFrames/Merging/MergeJoinIntro.ipynb)
* Week 9 [Intro to Linear Regression](Machine-Learning/Supervised-Methods/Regression/03-IntroLinearRegression.ipynb)
* Week 10 [Intro to Linear Regression](Machine-Learning/Supervised-Methods/Regression/03-IntroLinearRegression.ipynb)
* Week 11 [K Nearest Neighbours (kNN) Classification](Machine-Learning/Supervised-Methods/K-Nearest-Neighbours.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.ipynb)
* Week 5 [Decision Trees](Machine-Learning/Supervised-Methods/Decision-Trees/DecisionTrees.ipynb)
* Week 6 [Random Forests](Machine-Learning/Supervised-Methods/Decision-Trees/RandomForests.ipynb)
* Week 7 [Principal Component Analysis (PCA)](Machine-Learning/Unsupervised-Methods/Principal-Component-Analysis.ipynb)
* Week 8 [K-Means Clustering](Machine-Learning/Unsupervised-Methods/K-Means-Clustering.ipynb)
* Week 9 [Git](Project-Tools/GIT-CLI-CORE.pdf)
* Week 10 [Advanced Plotting](Visualisation/AdvancedPlotting.ipynb)
* Week 11 [Boosting](Machine-Learning/Ensemble-Methods/Boosting.ipynb)

### ADS2002