AWS LTA Workshop 2020

Instructors

• Daniel Kurniadi @iqDF - Singapore, Nanyang Technological University

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This repository will contain the teaching material and other info associated with our AWS LTA data science workshop at AWS LTA 2020 held January 2020 in NTU, Singapore.

Schedule:

The 2-part tutorial will be held every week during the event:

• Parts 1: Wednesday, January 21, 2020 (time 18:00 SGT)
• Parts 2: Wednesday, January 28, 2020 (time 18:00 SGT)

Obtaining the Tutorial Material

If you have a GitHub account, it is probably most convenient if you clone or fork the GitHub repository. You can clone the repository by running:

git clone --recursive https://github.com/MLDA-NTU/AWS-Workshop-2020.git

If you are not familiar with git or don’t have an GitHub account, you can download the repository as a .zip file by heading over to the GitHub repository (https://github.com/MLDA-NTU/AWS-Workshop-2020) in your browser and click the green “Download” button in the upper right.

Please note that we may add and improve the material until shortly before the tutorial session, and we recommend you to update your copy of the materials one day before the tutorials. If you have an GitHub account and cloned the repository via GitHub, you can sync your existing local repository with:

    $ git pull origin master

If you don’t have a GitHub account, you may have to re-download the .zip archive from GitHub.

Running in Google Colab

The recommended way to follow our workshop and run our notebooks is using Google Colab here. Follow the instruction as follows:

Step 1:

Open the menu File on the top left bar in Google Colab notebooks and choose menu Open Notebook ....

Step 2:

An orange menu box will pop out for you to choose a notebook to open from. Click the menu Github in the orange menu bar. Here, we can open the workshop notebook straight from our Github repository. Enter the github link: https://github.com/MLDA-NTU/AWS-Workshop-2020 in the search bar to see all notebooks for this workshop that we have hosted in github.

Step 3:

The workshop notebook will be loaded to your google colab environment. Click Runtime setting from the menu bar and change runtime configuration to the following:

Step 4:

Finally, hit the connect button on the top right corner in your google colab notebook and enjoy the workshop.

Installation Notes in Local Machine

If you decided to run the workshop notebooks on your local machine, you need to first install Python3.5+ from the official site here.

This tutorial will require recent installations of

• NumPy
• SciPy
• matplotlib
• pandas
• scikit-learn
• Jupyter Notebook
• boto3

The last one is important, you should be able to type:

    $ jupyter notebook

in your terminal window and see the notebook panel load in your web browser. Try opening and running a notebook from the material to see check that it works.

For users who do not yet have these packages installed, a relatively painless way to install all the requirements is to use a Python distribution such as Anaconda CE, which includes the most relevant Python packages for science, math, engineering, and data analysis; Anaconda can be downloaded and installed for free including commercial use and redistribution. The code examples in this tutorial should be compatible to Python 2.7, Python 3.5-3.7.

After obtaining the material, we strongly recommend you to open and execute the Jupyter Notebook jupter notebook check_envs.ipynb that is located at the top level of this repository. Inside the repository, you can open the notebook by executing

jupyter notebook check_envs.ipynb

inside this repository. Inside the Notebook, you can run the code cell by clicking on the "Run Cells" button as illustrated in the figure below:

Finally, if your environment satisfies the requirements for the tutorials, the executed code cell will produce an output message as shown below:

Although not required, we also recommend you to update the required Python packages to their latest versions to ensure best compatibility with the teaching material. Please upgrade already installed packages by executing

• pip install [package-name] --upgrade
• or conda update [package-name]

to download all necessary data beforehand.

Contributing

When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change.

Please note we have a code of conduct, please follow it in all your interactions with the project as written here