Cleaning Data with Pandas from Pluralsight

Learn to clean and manipulate data using the Pandas library in Python. Cover common issues like missing values and irrelevant features, use correlation analysis, encode categorical features, and prepare data for machine learning models.

In the real world, rarely is data organized into neat tables that can be fed directly into a machine learning model or used for data analysis. Data you find is often messy, missing many values, and generally tends to have multiple other issues that you need to solve before gaining any sort of meaningful inference from it.

In this course, Cleaning Data with Pandas, you will learn how to use the Pandas library in Python to clean and manipulate data.

First, you will understand what data cleaning is and why it is so important in the context of data analysis. Then, you will solve the most common issues plaguing datasets - missing values, irrelevant features, and duplicate values.

Next, you will see what correlation analysis is and how it helps in data cleaning.

Finally, you will see how to encode categorical features and prepare your dataset to be fed into machine learning models.

When you’re finished with this course, you will have the skills and knowledge you need to effectively clean and manipulate data using Pandas.

What's inside

Syllabus

Course Overview

Introduction to Data Cleaning with Pandas

Correlation Analysis and Data Preparation

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Explores common issues plaguing datasets, including missing values, irrelevant features, and duplicate values, which is standard in data analysis

Develops skills and knowledge in data cleaning and manipulation using Pandas, which are core skills for data analysis and preparation

Career center

Learners who complete Cleaning Data with Pandas will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts apply their knowledge of data cleaning and manipulation techniques, such as the skills taught in Cleaning Data with Pandas, to prepare and analyze data for various business purposes. This course covers topics like handling missing values, irrelevant features, and duplicate values, which are common challenges Data Analysts encounter in their day-to-day work. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Data Scientist

Data Scientists leverage data cleaning and manipulation skills to prepare data for modeling and analysis. Cleaning Data with Pandas provides a comprehensive introduction to these skills, covering topics such as handling missing values, irrelevant features, and duplicate values. These techniques are essential for Data Scientists to ensure the accuracy and reliability of their models and analyses. This course can help you build a strong foundation for a successful career in Data Science.

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Business Analyst

Business Analysts use data to identify and solve business problems. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Business Analysts face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Machine Learning Engineer

Machine Learning Engineers prepare data for training and deploying machine learning models. Cleaning Data with Pandas provides a comprehensive introduction to data cleaning and manipulation techniques, covering topics like handling missing values, irrelevant features, and duplicate values. These techniques are essential for Machine Learning Engineers to ensure the accuracy and efficiency of their models. This course can help you build a strong foundation for a successful career in Machine Learning Engineering.

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Data Engineer

Data Engineers design and build data pipelines to support data analysis and machine learning applications. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Data Engineers face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Statistician

Statisticians use data to make inferences about the world around us. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Statisticians face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Financial Analyst

Financial Analysts use data to make investment decisions. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Financial Analysts face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Market Researcher

Market Researchers use data to understand consumer behavior. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Market Researchers face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Operations Research Analyst

Operations Research Analysts use data to solve complex business problems. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Operations Research Analysts face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Quantitative Analyst

Quantitative Analysts use data to make investment decisions. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Quantitative Analysts face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Actuary

Actuaries use data to assess risk. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Actuaries face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Software Engineer

Software Engineers design, develop, and maintain software applications. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Software Engineers face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Web Developer

Web Developers design and develop websites and web applications. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Web Developers face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Database Administrator

Database Administrators design, implement, and maintain databases. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Database Administrators face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Data Entry Clerk

Data Entry Clerks input data into computer systems. Cleaning Data with Pandas covers techniques for handling missing values, irrelevant features, and duplicate values, which are common challenges Data Entry Clerks face when working with data. By mastering these techniques, you'll gain a strong foundation for success in this role.

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Cleaning Data with Pandas

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