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Ben Bolstad has completed

Creating Robust Workflows in Python

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4 hours
3,900 XP
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Course Description

The decisions we make in life are guided by our principles. No one is born with a life philosophy, instead everyone creates their own over time. In this course, you will develop a set of principles for your data science and software development projects. These principles will save time, prevent frustration, and build your confidence as a data scientist and software developer. In addition to best practices in the Python programming language, You will learn to leverage hidden gems in the Python standard library and well-known tools from Python's excellent ecosystem, such as pandas and scikit-learn. The time you invest in this course will yield dividends for you and others throughout your career. Your colleagues, community members, and future self will thank you.
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  1. 1

    Python Programming Principles

    Free

    In this chapter, we will discuss three principles that guide decisions made by Python programmers. You will put these principles into practice in the coding exercises and throughout the rest of the course!

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    Don't repeat yourself
    50 xp
    Functions and iteration
    100 xp
    Find matches
    100 xp
    Dataset dimensions
    100 xp
    Modularity
    50 xp
    Extract words
    100 xp
    Most frequent words
    100 xp
    Abstraction
    50 xp
    Instance method
    100 xp
    Class method
    100 xp
  2. 2

    Documentation and Tests

    Documentation and tests are often overlooked, despite being essential to the success of all projects. In this chapter, you will learn how to include documentation in our code and practice Test-Driven Development (TDD), a process that puts tests first!

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    Type hints
    50 xp
    TextFile hints
    100 xp
    MatchFinder hints
    100 xp
    Docstrings
    50 xp
    Get matches docstring
    100 xp
    Obtain words docstring
    100 xp
    Reports
    50 xp
    Build notebooks
    100 xp
    Convert notebooks
    100 xp
    Pytest
    50 xp
    Parametrize
    100 xp
    Raises
    100 xp
  3. 3

    Shell superpowers

    Shell scripting is an essential part of any Python workflow. In this chapter, you will learn how to build command-line interfaces (CLIs) for Python programs and to automate common tasks related to version control, virtual environments, and Python packaging.

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    Command-line interfaces
    50 xp
    Argparse nbuild()
    100 xp
    Docopt nbuild()
    100 xp
    Git version control
    50 xp
    Commit added files
    100 xp
    Commit modified files
    100 xp
    Virtual environments
    50 xp
    List installed packages
    100 xp
    Show package information
    100 xp
    Persistence and packaging
    50 xp
    Pickle dataframes
    100 xp
    Pickle models
    100 xp
  4. 4

    Projects, pipelines, and parallelism

    In the final chapter of this course, you will learn how to facilitate and standardize project setup using project templates. You will also consider the benefits of zipped executable projects, Jupyter notebooks parameterization, and parallel computing.

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    Project templates
    50 xp
    Set up template
    100 xp
    Create project
    100 xp
    Executable projects
    50 xp
    Zipapp
    100 xp
    Argparse main()
    100 xp
    Notebook workflows
    50 xp
    Parametrize notebooks
    100 xp
    Summarize notebooks
    100 xp
    Parallel computing
    50 xp
    Dask dataframe
    100 xp
    Joblib
    100 xp
    Wrap-up
    50 xp
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GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

Collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Sara Billen

Prerequisites

Python Data Science Toolbox (Part 2)Machine Learning with scikit-learn
Martin Skarzynski HeadshotMartin Skarzynski

Co-Chair, Foundation for Advanced Education in the Sciences (FAES)

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