Production-Level Code for Data Science

Production-Level Code for Data Science targets those that have a desire to immerse themselves in a single, long, and comprehensive project that covers several advanced Python concepts. By the end of the project you will have built a fully-functioning Python library that is able to complete many common data analysis tasks. The library will be titled Pandas Cub and have similar functionality to the popular pandas library.

This course focuses on developing software within the massive ecosystem of tools available in Python. There are 40 detailed steps that you must complete in order to finish the project. During each step, you will be tasked with writing some code that adds functionality to the library. In order to complete each step, you must pass the unit-tests that have already been written. Once you pass all the unit tests, the project is complete. The nearly 100 unit tests give you immediate feedback on whether or not your code completes the steps correctly.

Learn from a world-class industry expert

Teddy Petrou is founder of Dunder Data, a company that specializes in helping students become experts at data science using Python. Teddy is the author of multiple highly rated texts such as Pandas Cookbook, Master the Fundamentals of Python, and Master Data Analysis with Python. Teddy has taught hundreds of students Python and data science during in-person classroom settings. He sees first hand exactly where students struggle and continually upgrades his material to minimize these struggles by providing simple and direct paths forward. Teddy holds a master's degree in statistics from Rice University.

$ 700 $ 1,000

Enroll your team

Level :

Intermediate/Advanced

Date :

Starting on March 15

Live Sessions :

6 live courses (2 hours each) with weekly office hours and recordings

Lifetime Access

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Course teacher

Teddy Petrou

Prerequisites:

This is an intermediate/advanced course. Participants must feel comfortable with the fundamentals of Python.

This course is for:

Python software engineers, data scientists, those that are interested in building a complete Python library.

What you will be able to do after this course:

By the end of the course, you will have built a fully function data analysis library.

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Additional information about the course

Session 1: Project Genesis

We will download and install VS Code, an excellent free interactive development environment, then set up the environment and learn about test-driven development. We begin by inspecting the init file which will begin as the sole location for our library code. We’ll then learn how to import our library into a Jupyter Notebook. DataFrame construction is begun by checking input types.

Session 2: Properties and Subset Selection

We will implement several basic DataFrame properties such as access to the columns, values, and data types. A nice visual representation of the DataFrame will be displayed. We’ll then learn how to select subsets of data with the square brackets.

Session 3: Methods

We’ll create several methods, adding powerful features to our DataFrame. It will have the ability to aggregate, determine if a value is missing or not, and find unique values.

Session 4: Advanced Methods

Several more powerful methods will be added to our DataFrame. It will have the ability to rename and drop columns, sort column values, take random samples, create pivot tables and more.

Session 5: Reading Data

We’ll add the ability to read text data from files. This can be quite a challenge as data can be formatted in a wide variety of ways.

Session 6: Conclusion

You’ll now have a complete data analysis library with many of the same capabilities as the pandas library. Your code will have passed at least 100 tests through this process. We’ll discuss possible additional features and next steps you can take to improve the library.