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iap-class

Welcome to the "Programming with Data" IAP class!

Getting Started

You can clone this repository to your local machine by executing the following at a terminal

$ git clone git@github.com:mitdbg/iap-class.git
$ cd iap-class

To finish your installation, please look at the "Setup" section below.

Logistics

We will have a total of 8 lectures on the following topics:

  1. Processing: The relational data model and SQL.
  2. Pandas and Data Wrangling: Using the relational model in Python; working with text data.
  3. Preparation: Data preparation and cleaning.
  4. Presentation: Data visualizations and plotting.
  5. Prediction: Introduction to some Machine Learning techniques
  6. PyTorch: Introduction to Neural Networks.
  7. Performance: Improving data processing performance.
  8. Parallelism: Further improving performance through parallelism.

We will also have optional assignments each day: a shorter one over lunch and a longer one overnight. You are free to do these at your own pace, or not at all; they are just meant to give you some practiec with the techniques.

The schedule for each day will be as folllows:

  • 10:00-10:15: Homework questions from day before
  • 10:15-11:45: Morning lecture
  • 11:45-12:30: Lunch and lunch assignment
  • 12:30-14:00: Afternoon lecture

Setup

Cloning this repository

You can use the following command to clone this repository locally:

git clone git@github.com:mitdbg/iap-class.git

If this is your first time cloning a repository from GitHub using SSH, you may want to read the documentation about how to set this up.

Installing prerequisites

In order to complete the assignments included under assignments/, you will need to install some prerequisites and create a Python virtual environment. You can do this by running the following commands:

chmod +x setup.sh
sudo ./setup.sh

Managing the virtual python environment

To avoid interfering with the versions of python packages you may have already installed on your machine, the setup script has installed all dependencies in a virtual environment called iap-data-venv. Whenever you use python in one of the assignments, make sure that you have activated this virtual environment by running:

source iap-data-venv/bin/activate

Once you are done, you can deactivate the virtual environment by running:

deactivate