Advanced Python for Data Science
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Advanced Python for Data Science

About the Book

Today, most people enter the world of Data Science through the buzz and allure of “AI.” We tackle Kaggle challenges, voraciously consume Stack Overflow, and eat, live, and breathe through the Jupyter Notebook. Python, along with its “killer app” of Machine Learning, has done nothing short of revolutionize the way we “do data science,” and the world is a more interesting place because of it!

The Big Cloud providers, and many open source tools, have done wonders to democratize this technology. But, ‘easy access’ to high technology comes with a cost - we can easily go too far, rely too much on the tools we have today, and forget how to build the tools we need to truly transform our individual projects.

Most of the time, your impact as a Data Scientist is limited by your ability to enact your ideas - not by the ideas themselves. You can train a model on ‘clean’ data using Scikit Learn or FastAI, or run an ANOVA, in a notebook. Enacting that idea means getting to the data in the first place. It means knowing how to store it. It means processing your data at scale. It means running your processing script, reliably, every day on fresh data. It means testing that script. It means collaborating on that script with a coworker - or 10 - as the project scales. It means curating a library and building tools to solve the same problem for 5 new projects. It means packaging a model up for distribution - sharing with another data scientist, or deploying it as a service.

It means changing the way you think about problems by adopting new paradigms that accelerate you - and your work - across your organization. It means building an approach to data science within the broader python ecosystem.

This book is about python, and how to be an effective python programmer, as a Data Scientist. We learn the advanced python skills we need to accelerate you, and solve the real, daily problems you face in your DS role.

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  • Categories

    • Python
    • Machine Learning
    • Artificial Intelligence
    • Data Science
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About the Author

Scott Gorlin
Scott Gorlin

Scott Gorlin, Ph.D., is an executive and leader of applied science and machine learning, with a particular emphasis on enabling data scientists to enhance - and reproduce - their work through code.  A programmer by hobby since elementary school, Dr. Gorlin quickly realized the potential of applying foundational and advanced programming concepts to research and development, and has worked to enhance his and his teams’ coding standards and practices since the beginnings of his professional career.

This aspect of his work evolved into teaching a formal graduate course, Advanced Python for Data Science, which is offered through Harvard Extension School’s degree and certificate programs in machine learning.

Dr. Gorlin received his doctoral degree in 2011 from the Massachusetts Institute of Technology in Computational and Systems Neuroscience, and went on to lead R&D for Choicestream, an ad-tech firm.  He currently is Senior Director of Enterprise Science and Trusted AI at Liberty Mutual, and continues to teach Advanced Python at Harvard Extension School in his free time.

Table of Contents

  • Preface
    • Introduction
      • What is this book?
      • Who is it for?
      • What will you learn?
      • The state of the book
    • An introduction to Advanced Python
      • What is Advanced Python?
      • Tech Requirements
      • Helpful Themes
      • Readings
  • Workflows
    • Continuous science
      • Debugging
      • “Bug Report” Rules
      • Primers
      • Higher Levels
      • Testing
      • Readings
    • Scientific workflows
      • What is Python?
      • Config
      • Decorators
      • Bootstraping
      • Readings
    • Packages and iteration
      • Packages
      • Versioning
      • Functional Programming
    • Avoiding the for loop
      • Primitives
      • Vectorizing
      • Einstein Summation
      • Iteration Primitives
      • Vectorization: A Case Study
      • Iterators
      • Readings
  • Skeletons
    • Classes, composition, and graphs
      • Inheritance
      • Composition
    • The DAG
      • Graphical Programs
      • What does Data Science look like?
      • The Revelation
    • Luigi
      • Project scaffolding
      • The Task
      • The Pieces
      • The Big Picture
      • Atomicity
      • Atomicity
      • Readings
    • Graphs
      • Luigi
      • The Big Picture
      • Salted Graphs
      • The Sorry State of Stateful Data
      • Advanced Luigi
  • Data
    • Dask and Parquet
      • Micro Sciences
      • Dask - Basics
      • Rookie Mistakes
      • Executive Summary
      • Split, Apply, Combine
      • Data Containers
      • Parquet
      • Dask - Partitioning
      • Case Study - Fancy Indexing
      • Dask and Luigi
      • Readings
    • Django and SQL
      • Mutability
      • Living Data
      • Django
      • ORM
      • Django Code
      • ORM Breakdowns
      • The Competition
      • Readings
    • API’s and Data
      • Metaprogramming
      • DB Design
      • Atomic Targets
      • Migrations
      • The Web
      • APIs
      • Reading
    • More Meta
      • Api’s and Clients
      • Factories
      • Optimization
      • Readings
  • Algorithms
    • Smart & Lazy Coding
      • Parallel Code
      • Memory Views
      • Memoization
      • Sketching
      • Readings
    • Visualization
      • Data Viz
      • Declarative Grammars
      • Javascript and HTML5
      • Colormaps
      • Data Shading
      • Readings
    • Where We Are
      • “Python”
      • Testing
      • Workflows
      • Higher Levels
      • Deployment
      • Looping
      • Functional Coding
      • Composition
      • Graphical Programs
      • Data Scaling
      • The Web
      • DB’s
      • API’s
      • Meta
      • Optimization
      • Visualization
  • Appendix
    • Changelog

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