Testing Python Data Science Code

Focused View

Miki Tebeka

53:58

231 View

01 - Introduction

01 - Testing scientific applications.mp4

00:46

02 - What you should know.mp4

00:14

03 - Setting up.mp4

01:09

02 - 1. Testing Overview

01 - Why test.mp4

00:58

02 - Types of tests.mp4

00:46

03 - Challenges in testing scientific applications.mp4

00:54

04 - Continuous integration overview.mp4

00:55

03 - 2. pytest

01 - pytest overview.mp4

01:53

02 - Selecting tests.mp4

02:30

03 - Parametrized tests.mp4

01:16

04 - Fixtures.mp4

02:10

05 - Mocking.mp4

02:17

06 - Challenge Test with pytest.mp4

00:50

07 - Solution Test with pytest.mp4

01:38

04 - 3. hypothesis

01 - Overview of hypothesis.mp4

00:36

02 - Testing with hypothesis.mp4

01:37

03 - NumPy utilities.mp4

01:54

04 - pandas utilities.mp4

01:34

05 - Writing strategies.mp4

01:46

06 - Challenge Test with hypothesis.mp4

00:28

07 - Solution Test with hypothesis.mp4

00:59

05 - 4. Data Validation

01 - Using schemas.mp4

02:51

02 - Truth values.mp4

02:35

03 - Floating point wonders.mp4

01:46

04 - Approximate testing.mp4

01:18

05 - Dealing with randomness.mp4

01:45

06 - Comparing pandas DataFrames.mp4

01:31

07 - Challenge Testing numerical code.mp4

00:56

08 - Solution Testing numerical code.mp4

00:51

06 - 5. Regression Testing

01 - Regression testing overview.mp4

00:33

02 - Selecting regression data.mp4

01:33

03 - Choosing quality metrics and baseline.mp4

01:12

04 - Quality regression testing.mp4

01:04

05 - Choosing speed and memory metrics.mp4

01:00

06 - Performance regression testing.mp4

01:26

07 - 6. Testing Jupyter Notebooks

01 - Testing Notebooks overview.mp4

00:48

02 - Using nbconvert.mp4

01:59

03 - Refactoring code.mp4

01:39

04 - Other test libraries.mp4

01:29

08 - Conclusion

01 - Next steps.mp4

00:32

Description

The larger and more complex the world of data science becomes, the more data there is to collect, sort, clean, model on, and much more. An emerging pain point in this brave new world is that a lot can go wrong if your data engineering and development practices are shoddy. This advanced-level course shows data scientists, Python developers, and data analysts how to test scientific (data science) code written in Python. Veteran data science trainer and consultant Miki Tebeka covers testing techniques, with a focus on issues specific to data science code, such as floating point errors, statistical testing, working with large datasets, choosing a baseline, and more. After presenting a testing overview, Miki dives into testing with pytest and hypothesis. He explains how to use schemas, truth values, approximate testing, and more in data validation. Miki goes over regression testing, then demonstrates how to test Jupyter Notebooks.

More details

User Reviews

Rating

average 0

Total votes0

Focused display

Python

Data Science

Software Testing

Miki Tebeka

Instructor's Courses

Teaching effective hands-on workshops all of the world. Consultant solving hard problem with the right tools (Java and C++ are *not* the right tools ;). Book author, LinkedIn learning Author, open source contributor and convention organizer, meetup co host and coding for fun in my spare time. Specialties: Python & Scientific Python (Expert), Go (Expert), C/C++, Clojure, JavaScript, bash, ... Information retrieval - tokenization, summarization, clustering, search ... Concurrency - Multi process, multi threaded, Hadoop ... Web development - REST APIs, jQuery, JavaScript, CSS, (X)HTML Assemblers, Linkers, Debugger, Simulators SCM tools (git, Mercurial, Perforce, subversion, CVS, ClearCase) Linux, OS X and Windows Functional Programming, OOD, OOP Databases - SQL (BigQuery, PostgreSQL, MySQL, Oracle) and NoSQL (Redis, MongoDB, CouchDB)

Linkedin Learning

View courses Linkedin Learning

LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.