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Validate Data Cleanliness Using Asserts in Python

Pinal Dave

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.

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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.

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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

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Activities

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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.
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.
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.
Data Governance Specialist
Validate Data Cleanliness Using Asserts in Python is highly relevant for your career as a Data Governance Specialist. Data Governance Specialists develop and implement data governance policies and procedures. 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 compliance with data governance policies and procedures.
Machine Learning Engineer
Validate Data Cleanliness Using Asserts in Python is highly relevant for your career as a Machine Learning Engineer. Machine Learning Engineers design, develop, and maintain machine learning models. This course can provide you with the skills needed to verify data tidiness and compose quantitative and logical tests for clean data, which is crucial for ensuring the accuracy and reliability of machine learning models.
Chief Data Officer
Validate Data Cleanliness Using Asserts in Python is relevant for your career as a Chief Data Officer. Chief Data Officers are responsible for overseeing all aspects of data management within 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 is critical for ensuring the quality and reliability of data across the organization.
Statistician
Validate Data Cleanliness Using Asserts in Python is relevant for your career as a Statistician. Statisticians use data to draw conclusions and make predictions. 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.
Data Architect
Validate Data Cleanliness Using Asserts in Python is relevant for your career as a Data Architect. Data Architects design and build data management systems. 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 quality and reliability of data management systems.
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.
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.
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.
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.
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.
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.
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.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Validate Data Cleanliness Using Asserts in Python.
The pandas library has become a standard tool for data manipulation and cleaning in Python. provides a comprehensive guide to the library and its uses.
Provides a comprehensive introduction to Python for data analysis. It good resource for learners who are new to the field.
Provides a comprehensive introduction to data science, including data cleaning and preparation. It good resource for learners who are new to the field.
Provides a comprehensive overview of deep learning with Python. It good resource for learners who are interested in learning more about the field.
Provides a comprehensive overview of natural language processing with Python. It good resource for learners who are interested in learning more about the field.
Provides a comprehensive overview of computer vision with Python. It good resource for learners who are interested in learning more about the field.
Provides a comprehensive overview of feature engineering for machine learning. It good resource for learners who are interested in learning more about the field.
Provides a comprehensive overview of data science for business. It good resource for learners who are interested in learning more about the field.

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