For additional information about creating and managing Anaconda environments, see the Anaconda documentation. Open an Anaconda command prompt and run conda create -n myenv python=3.7 pandas jupyter seaborn scikit-learn keras tensorflow to create an environment named myenv. In this section, you will create a workspace for the tutorial, create an Anaconda environment with the data science modules needed for the tutorial, and create a Jupyter notebook that you'll use for creating a machine learning model.īegin by creating an Anaconda environment for the data science tutorial. With native support for Jupyter notebooks combined with Anaconda, it's easy to get started. Visual Studio Code and the Python extension provide a great editor for data science scenarios. If you go this route, you will need to install the following packages: pandas, jupyter, seaborn, scikit-learn, keras, and tensorflow. Alternatively, if you'd prefer not to use Anaconda or Miniconda, you can create a Python virtual environment and install the packages needed for the tutorial using pip. Note: If you already have the full Anaconda distribution installed, you don't need to install Miniconda. Both extensions are published by Microsoft. For additional details on installing extensions, see Extension Marketplace. Note that by default, the Python extension installs the Jupyter extension for you. The Python extension for VS Code and Jupyter extension for VS Code from the Visual Studio Marketplace. If you do not have them already, install them prior to beginning. The following installations are required for the completion of the tutorial. Specifically, using passenger data from the Titanic, you will learn how to set up a data science environment, import and clean data, create a machine learning model for predicting survival on the Titanic, and evaluate the accuracy of the generated model. This tutorial demonstrates using Visual Studio Code and the Microsoft Python extension with common data science libraries to explore a basic data science scenario. Configure IntelliSense for cross-compiling.
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