Software Engineering for Data Scientists
Asynchronous
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Registration Deadlines
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Category
Launch, Supplemental, Self-Directed, Mini-Course
Overview
The Software Engineering for Data Scientists course is meant to help data scientists write production ready code as well as gain familiarity with the tools used to make models available to their users. The core idea we will be exploring is making code robust and re-usable across a team. This course can also serve as an introduction toward ideas used in ML Ops and Data Engineering.
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Organizers, Instructors, and Advisors
Steven Gubkin
Head of Training and Assessment
Office Hours:
By appointment only
Email:
Preferred Contact:
Slack
Your primary contact for GitHub access.
Kevin Nowland
Lead instructor, ML Ops Engineer
Office Hours:
Intermittent Thursday Afternoons
Email:
Preferred Contact:
Slack
Please reach out on slack if you have any questions about the content in this course!
Objectives
After completing this course, you will be able to the following:
- Understand common tools used to deploy models for real-time inference
- Improve your code's robustness through unit testing
- Improve your code's readability through using linters and type checking
- Use basic command line commands
- Be able to implement a simple continuous integration pipeline using GitHub Actions
First Steps/Prerequisites
- Figure out how to access a terminal emulator, e.g., the Terminal program on Mac OS / Ubuntu
- If using Windows, enable the Windows Subsystem for Linux and access a terminal emulator
- Download pyenv and use it to install python 3.10.x
Program Content
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Textbook/Notes
Intro to the CLI - part 1
Getting ready to code
We’ll be talking about the different shells that allow you to interact with your computer, navigating the filesystem, and basic ways to manipulate files.
Project/Homework Instructions
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Schedule
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Please check your registration email for program schedule and zoom links.
Project/Homework Deadlines