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Cleaning String Data in Python

Victoria Clark

Python has built-in capabilities that help you easily clean string data. This course will teach you what those capabilities are and how to use them.

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Python has built-in capabilities that help you easily clean string data. This course will teach you what those capabilities are and how to use them.

An essential part of creating critical-decision-making applications is the ability to process quality data to analyze expected outcomes in the best possible way. Python comes with great built-in capabilities to clean string data.

In this course, Cleaning String Data in Python, you will learn how to use various built-in Python capabilities to clean string data.

First, you’ll learn how to clear out unwanted characters from strings by using vectorized string operations in Python.

Next, you’ll learn how to use various Python capabilities to remove line breaks, remove stop words, remove numbers, and remove unwanted characters from a string using regular expressions.

Finally, you’ll learn how to work with unstructured string data to extract relevant information from it and make it useful.

When you are finished with this course, you’ll have the skills and knowledge necessary for cleaning string data using Python’s capabilities and features.

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What's inside

Syllabus

Course Overview
Getting Started with String Cleaning Ops
Advanced String Cleaning Ops

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores built-in Python capabilities for string data cleaning, which is crucial for data processing in applications
Builds a strong foundation for beginners in string data cleaning using Python
Develops core skills essential for critical-decision-making applications

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Activities

Coming soon We're preparing activities for Cleaning String Data in Python. These are activities you can do either before, during, or after a course.

Career center

Learners who complete Cleaning String Data in Python will develop knowledge and skills that may be useful to these careers:
Data Scientist
As a Data Scientist, you will use your knowledge of Python to clean and analyze data to help businesses make informed decisions. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Data Scientist.
Machine Learning Engineer
As a Machine Learning Engineer, you will use your knowledge of Python to develop machine learning models. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Machine Learning Engineer.
Data Engineer
As a Data Engineer, you will use your knowledge of Python to build and maintain data pipelines. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Data Engineer.
Software Engineer
As a Software Engineer, you will use your knowledge of Python to develop software applications. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Software Engineer.
Data Analyst
As a Data Analyst, you will use your knowledge of Python to analyze data to help businesses make informed decisions. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Data Analyst.
Web Developer
As a Web Developer, you will use your knowledge of Python to develop websites and web applications. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Web Developer.
Database Administrator
As a Database Administrator, you will use your knowledge of Python to manage and maintain databases. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Database Administrator.
Business Analyst
As a Business Analyst, you will use your knowledge of Python to analyze data to help businesses make informed decisions. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Business Analyst.
Network Engineer
As a Network Engineer, you will use your knowledge of Python to design and maintain computer networks. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Network Engineer.
IT Auditor
As an IT Auditor, you will use your knowledge of Python to audit computer systems for security vulnerabilities. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any IT Auditor.
Information Security Analyst
As an Information Security Analyst, you will use your knowledge of Python to protect computer systems from cyberattacks. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Information Security Analyst.
Technical Writer
As a Technical Writer, you will use your knowledge of Python to write technical documentation. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Technical Writer.
Project Manager
As a Project Manager, you will use your knowledge of Python to manage and track projects. This course will teach you how to use Python's built-in capabilities to clean string data, which is an essential skill for any Project Manager.

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 Cleaning String Data in Python.
Provides a comprehensive overview of natural language processing (NLP) techniques, with a focus on Python implementations. It covers topics such as text preprocessing, feature engineering, and machine learning for NLP tasks, and it includes numerous examples and exercises to help readers learn the material.
Provides a hands-on introduction to data science, using Python as the primary programming language. It covers topics such as data cleaning, data analysis, and machine learning, and it includes numerous examples and exercises to help readers learn the material.
Provides a comprehensive overview of the Python programming language, with a focus on data analysis applications. It covers topics such as data manipulation, data analysis, and data visualization, and it includes numerous examples and exercises to help readers learn the material.
Provides a comprehensive overview of machine learning techniques, with a focus on Python implementations. It covers topics such as supervised learning, unsupervised learning, and deep learning, and it includes numerous examples and exercises to help readers learn the material.
Provides a practical introduction to the use of Python for financial applications. It covers topics such as data analysis, financial modeling, and risk management, and it includes numerous examples and exercises to help readers learn the material.
Provides a comprehensive overview of the Python programming language, with a focus on data science applications. It covers topics such as data manipulation, data analysis, and machine learning, and it includes numerous examples and exercises to help readers learn the material.
Provides a practical introduction to deep learning techniques, with a focus on Python implementations. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, and it includes numerous examples and exercises to help readers learn the material.
Provides a comprehensive overview of reinforcement learning techniques, with a focus on Python implementations. It covers topics such as Markov decision processes, value iteration, and policy gradient methods, and it includes numerous examples and exercises to help readers learn the material.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It covers topics such as text preprocessing, feature engineering, and machine learning for NLP tasks, and it includes numerous examples and exercises to help readers learn the material.

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