Software Engineering for Data Scientists

Course

Software engineering skills are increasingly important for data scientists. In this course, you'll learn best practices for writing software, working with Python packages, and building web data dashboards. You'll take a look "under the hood" of Python packages like scikit-learn and then practice creating and sharing virtual environments, packages, and web data dashboards. You will also become familiar with code quality and reproducibility tools including unit tests, linting, and GitHub actions. In your final project, you will develop a FastHTML dashboard that allows users to interact with a machine learning model through a web interface.

Advanced
3 weeks
Last Updated January 24, 2025

Skills you'll learn:

Magic methodsUnit testing

Prerequisites:

matplotlibGithubPandasIntermediate SQLscikit-learn

Advanced

3 weeks

Last Updated January 24, 2025

Skills you'll learn:

Magic methods • Unit testing • Python virtual environments • Model deployment

Prerequisites:

matplotlib • Github • Pandas

Course Lessons

Lesson 1

Object-Oriented Programming

Explore object-oriented programming (OOP), including classes, instances, magic methods, inheritance, and polymorphism. Utilize professional coding patterns for efficient Python software development.

Lesson 2

Code Reproducibility

Use and create virtual environments, write Python packages, manage file paths in a way that enables cross-platform compatibility, and ensure code quality through testing and linting.

Lesson 3

Data Science Dashboards

Dig into deployment options for ML models using web servers, web forms, dynamic endpoints, and interactive dashboards with Python and FastHTML.

Lesson 4 • Project

Project: Data Science Dashboard

Build a dashboard web application that allows managers to monitor an employee's performance and their predicted risk of recruitment.

Taught By The Best

Jo-L Collins

Senior Lead Data Scientist

Jo-L Collins is a Data Scientist and former Data Science instructor at Flatiron School, where they use a variety of inferential and descriptive analysis techniques, paired with airflow development, to answer complex data questions.

The Udacity Difference

Combine technology training for employees with industry experts, mentors, and projects, for critical thinking that pushes innovation. Our proven upskilling system goes after success—relentlessly.

Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

Gain proven experience
Retain knowledge longer
Apply new skills immediately

Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.