Validate Data Cleanliness Using Asserts in Python from Pluralsight

This course teaches how to use asserts in Python to validate data cleanliness. Learn to compare indexes, Series, and DataFrames, compose quantitative and logical tests, and apply them for data cleaning.

Inaccurate or inconsistent data can lead to poor business decisions. However, manually validating data can be time-consuming and error-prone. Tools and technologies available today can help automate the process of validating and cleaning data.

In this course, Validate Data Cleanliness Using Asserts in Python, you will learn how to use asserts in Python to validate the cleanliness of data.

First, you will be introduced to the numpy.testing module and how it can be used to verify data tidiness.

Next, you will discover how to verify the equality of two indexes, two Series, and two DataFrames using the various testing functions available in the numpy.testing module.

Finally, you will explore how to compose quantitative and logical tests for clean data using asserts and apply them for data cleaning.

When you are finished with this course, you will have the skills needed to use asserts to validate data cleanliness in Python.

What's inside

Syllabus

Course Overview

Validating and Verifying Data Using Asserts

Using Assert-based Tests for Data Cleaning

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces learners to accurate data verification and cleaning techniques

Meant for learners looking to streamline and automate data cleaning processes

May be of particular interest for those in data analysis, data science, or development

Covers foundational concepts and practical techniques for asserting data accuracy

Includes hands-on exercises and real-world examples to enhance learning

Led by the knowledgeable instructor, Pinal Dave, recognized for expertise in data science

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Validate Data Cleanliness Using Asserts in Python with these activities:

Organize Course Materials

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Stay organized and revisit key concepts by compiling course materials

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Gather lecture notes, assignments, and quizzes
Create a digital or physical repository for your materials

Seek Mentorship

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Connect with experienced individuals in the field for guidance and support

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Identify potential mentors in your professional network or online communities
Reach out and express your interest

Review Python Basics

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Warm up prior to course commencement by revisiting the fundamental principles of Python

Browse courses on Python Basics

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Review basic data structures like lists, tuples, and dictionaries
Refresh your understanding of essential Python libraries

Two other activities

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Guided Tutorials on Data Validation

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Supplement your learning with video walkthroughs and written guides on the art of data validation

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Explore tutorials provided by Pluralsight or other reputable platforms
Follow along with demonstrations and practice exercises

Peer Review and Collaboration

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Enhance your learning through discussions and collaboration with fellow students

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Form study groups and share insights
Review and provide feedback on each other's work

Career center

Learners who complete Validate Data Cleanliness Using Asserts in Python will develop knowledge and skills that may be useful to these careers:

Data Analyst

Validate Data Cleanliness Using Asserts in Python can assist you on your journey as a Data Analyst. Data Analysts must be able to effectively acquire, clean, and analyze data in order to extract meaningful insights. This course can help you lay a solid foundation by teaching you the skills needed to verify data tidiness and compose quantitative and logical tests for clean data. By learning how to use asserts in Python to validate data cleanliness, you can help ensure the accuracy and consistency of data for analysis.

See salaries and explore the career path for Data Analyst

Data Engineer

Validate Data Cleanliness Using Asserts in Python is a relevant course for your career as a Data Engineer. Data Engineers are responsible for designing, building, and maintaining data pipelines. This course can help you lay a strong foundation by teaching you the skills needed to verify data tidiness and compose quantitative and logical tests for clean data. By learning how to use asserts in Python to validate data cleanliness, you can help ensure the quality and reliability of data pipelines.

See salaries and explore the career path for Data Engineer

Data Scientist

Validate Data Cleanliness Using Asserts in Python can be a valuable course for your career as a Data Scientist. Data Scientists use a variety of tools and techniques to extract insights from data, and being able to ensure the cleanliness of data is crucial. This course can provide you with the skills needed to verify data tidiness and compose quantitative and logical tests for clean data. By learning how to use asserts in Python to validate data cleanliness, you can help improve the accuracy and reliability of your data analysis.

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Data Governance Specialist

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Machine Learning Engineer

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Chief Data Officer

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Statistician

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Data Architect

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Quantitative Analyst

Validate Data Cleanliness Using Asserts in Python is relevant for your career as a Quantitative Analyst. Quantitative Analysts use mathematical and statistical models to analyze data. This course can provide you with the skills needed to verify data tidiness and compose quantitative and logical tests for clean data, which is essential for ensuring the accuracy and reliability of quantitative models.

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Database Administrator

Validate Data Cleanliness Using Asserts in Python may be useful for your career as a Database Administrator. Database Administrators are responsible for managing and maintaining databases. This course can provide you with the skills needed to verify data tidiness and compose quantitative and logical tests for clean data, which can be helpful in ensuring the integrity of databases.

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Business Analyst

Validate Data Cleanliness Using Asserts in Python may be useful for your career as a Business Analyst. Business Analysts use data to understand and solve business problems. This course can provide you with the skills needed to verify data tidiness and compose quantitative and logical tests for clean data, which can be helpful in ensuring the accuracy and reliability of data for analysis.

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Actuary

Validate Data Cleanliness Using Asserts in Python may be useful for your career as an Actuary. Actuaries use data to assess risk and uncertainty. This course can provide you with the skills needed to verify data tidiness and compose quantitative and logical tests for clean data, which can be helpful in ensuring the accuracy and reliability of data for analysis.

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Risk Manager

Validate Data Cleanliness Using Asserts in Python may be useful for your career as a Risk Manager. Risk Managers identify and assess risks to an organization. This course can provide you with the skills needed to verify data tidiness and compose quantitative and logical tests for clean data, which can be helpful in ensuring the accuracy and reliability of data for risk assessment.

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Software Engineer

Validate Data Cleanliness Using Asserts in Python may be useful for your career as a Software Engineer. Software Engineers design, develop, and maintain software systems. While they do not usually focus on data analysis, having a strong understanding of data cleanliness can be valuable for ensuring the quality and reliability of software. This course can provide you with the skills needed to verify data tidiness and compose quantitative and logical tests for clean data, which can be helpful in developing data-driven applications.

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Financial Analyst

Validate Data Cleanliness Using Asserts in Python may be useful for your career as a Financial Analyst. Financial Analysts use data to make investment decisions. This course can provide you with the skills needed to verify data tidiness and compose quantitative and logical tests for clean data, which can be helpful in ensuring the accuracy and reliability of data for analysis.

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