Unverified Commit 800c7726 authored by Simon Bowly's avatar Simon Bowly
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Add topic index and consolidate resource links.

parent 0b1069c9
# ADS1001/2 PyData Resources
# Bachelor of Applied Data Science: Data Challenges Unit Resources
## Course Materials: Jupyter Notebooks
## By Topic
* [Basic Python Concepts](03-BeginnerToAdvanced/BeginnerToAdvanced.ipynb)
* Pandas
* [Reading, Cleaning and Exploring Data](01-Incoming-Survey/Reading-Cleaning-Exploring.ipynb)
* [Exploring and Plotting Data](02-Pandas-Timeseries/Weather.ipynb)
* [Handling Timeseries Data](02-Pandas-Timeseries/Melbourne-Pedestrians.ipynb)
* [Merging and Joining](04-Merging/MergeJoinIntro.ipynb)
* Regression Models
* [Linear Regression](05-LinearRegression/LinearRegress.ipynb)
* [Polynomial Regression](05-LinearRegression/PolyFitt.ipynb)
* [Multilinear Regression](05-LinearRegression/Diabetes.ipynb)
* [Logistic Regression](05-LinearRegression/LogisticRegressionIris.ipynb)
* [Multiclass Logistic Regression](05-LinearRegression/MulitLogisticRegression.ipynb)
## Online Tutorials: Python/Pandas Basics
* [Jupyter Notebook for Beginners: A Tutorial](https://www.dataquest.io/blog/jupyter-notebook-tutorial) - This tutorial covers some basic essentials of Python, Jupyter and Pandas library.
* [Dealing with Rows and Columns in Pandas DataFrame](https://www.geeksforgeeks.org/dealing-with-rows-and-columns-in-pandas-dataframe) - This tutorial covers how to access and manipulate rows and columns of DataFrames in Pandas (selecting, indexing, slicing, dropping, and so on).
For more resources and links, see the [Wiki](https://gitlab.erc.monash.edu.au/ads1001/python-data-science-resources/-/wikis/home).
* [Project-Tools](./Project-Tools)
* [Git](./Project-Tools/Git)
* [Python-Jupyter](./Python-Jupyter)
* [Pandas-DataFrames](./Pandas-DataFrames)
* [Merging](./Pandas-DataFrames/Merging)
* [Time-Series](./Pandas-DataFrames/Time-Series)
* [Incoming-Survey](./Pandas-DataFrames/Incoming-Survey)
* [Visualisation](./Visualisation)
* [Machine-Learning](./Machine-Learning)
* [Unsupervised-Methods](./Machine-Learning/Unsupervised-Methods)
* [Principal-Component-Analysis](./Machine-Learning/Unsupervised-Methods/Principal-Component-Analysis)
* [K-Means-Clustering](./Machine-Learning/Unsupervised-Methods/K-Means-Clustering)
* [Supervised-Methods](./Machine-Learning/Supervised-Methods)
* [Regression](./Machine-Learning/Supervised-Methods/Regression)
* [Decision-Trees](./Machine-Learning/Supervised-Methods/Decision-Trees)
* [Support-Vector-Machines](./Machine-Learning/Supervised-Methods/Support-Vector-Machines)
* [K-Nearest-Neighbours](./Machine-Learning/Supervised-Methods/K-Nearest-Neighbours)
* [Ensemble-Methods](./Machine-Learning/Ensemble-Methods)
* [Boosting](./Machine-Learning/Ensemble-Methods/Boosting)
* [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)
......@@ -46,6 +46,8 @@ Here are a few tutorial options to get started:
* [10 minutes to pandas](https://pandas.pydata.org/docs/getting_started/10min.html): crash course in playing with DataFrames.
* [Pandas cheat sheet](https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf): great visual guide to the most common operations you'll need for transforming/grouping/plotting/exploring data sets.
* [Pandas cookbook](https://github.com/jvns/pandas-cookbook): a set of jupyter notebooks to follow through with real data to play with. This is set up using an online service called Binder which allows you to click through the exercises while running the code examples (see the 'how to use this cookbook' section).
* [Jupyter Notebook for Beginners: A Tutorial](https://www.dataquest.io/blog/jupyter-notebook-tutorial) - This tutorial covers some basic essentials of Python, Jupyter and Pandas library.
* [Dealing with Rows and Columns in Pandas DataFrame](https://www.geeksforgeeks.org/dealing-with-rows-and-columns-in-pandas-dataframe) - This tutorial covers how to access and manipulate rows and columns of DataFrames in Pandas (selecting, indexing, slicing, dropping, and so on).
* Other useful resources
* [PyData TV](https://www.youtube.com/user/PyDataTV): this YouTube channel has a lot of useful videos from the PyData workshop series.
* [.head() to .tail()](https://www.youtube.com/watch?v=7vuO9QXDN50) is a good one to start with.
......@@ -73,4 +75,4 @@ Also, it's the industry standard for working on code collaboratively so is a val
**Please note** that by default, when you create a GitHub repository, everything you contribute is public.
You can set a repository to private, but this only allows you to give access to up to three other people.
Ensure you have permission from everyone involved in the creation of any work before you store it on a public site.
If you do want to keep work private and still use the Git workflow, you can create projects on Monash's [GitLab server](https://gitlab.erc.monash.edu.au/), which allows you to keep a private repository for group work.
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If you do want to keep work private and still use the Git workflow, you can create projects on Monash's [GitLab server](https://gitlab.erc.monash.edu.au/), which allows you to keep a private repository for group work.
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