Set up your computing environment#

You will need a working computing environment to run the examples and to complete the labs in this course. While you can use any programming language, I strongly recommend Python for a few reasons:

  • Python is a widely used language for data science, and resources for Python are easy to find.

  • Many common algorithms, including ones used in this course, are readily available as Python packages.

  • In this course, examples and some partial code of the labs will be provided in Python only.

If you already have a working Python environment, it may already be sufficient for this course. However, you might still want to create a new environment in case you need to install new packages for this course and you don’t want them to interfere with your original environment. The instructions below will guide you to install conda and create a new Python environment.

If you wish to use a different programming language, please read Use other programming languages below.

Use Python on your machine#

First, check if you already have conda available on your machine. If you have been using Python on your machine, there is a good chance that you already have conda too. You can check whether the conda command is available from a terminal. If it is, skip Install conda and jump to Create a new environment directly.

Install conda#

If conda is not available on your machine, you can install one very easily.

I recommend installing Miniforge, but you can choose Miniconda or even Anaconda if you like.

Simply download Miniforge3 for your OS from this page. Once it’s downloaded, just run the installer and follow the instruction therein.

Near the end of the installation process, you will be prompted to choose whether you want the base conda environment be activated by default. If you choose no, make sure you copy the command that activates the base conda environment and keep it somewhere handy. You will need to activate the base conda environment for the conda command to be available on your machine.

Create a new environment#

Once conda is available on your machine, you can go ahead and create a new environment for this course.

First, download this environment definition file.

Once it’s downloaded, you can run:

conda env create -f /path/to/environment.yml

Once the package installation is done, you can run the following to activate this new environment:

conda activate 7730

Start JupyterLab#

After the 7730 environment is activated, you can start the JupyterLab interface by running

jupyter lab

If you are not familiar with using JupyterLab, you can find extensive documentation here.

Use Python on Google Colab#

If you don’t want to install Python on your own machine, or if you don’t have access to a machine that you can install new software, you can use Google Colab as an alternative.

Google Colab provides free Jupyter Notebook service. To use Google Colab, you will need a Google account, a web browser, and a stable Internet connection. Simply visit to start using the service.

Use other programming languages#

While I do recommend using Python for this course, learning Python is not one of the learning objectives of this course. Hence, if using another programming language makes it easier for you to learn the materials in this course, you are welcome to do so. You can submit the assignments using the programming language you choose.

Please note that I will not be able to translate any partial code in the assignments into other programming languages. I will also not be able to help you identify packages in other programming languages that offer the functionalities of Python packages that we use.