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

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

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

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 Cleaning Data with Pandas with these activities:

Connect with a Mentor

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Accelerate your progress by seeking guidance from an experienced mentor who can provide personalized advice, support, and encouragement throughout your learning journey in data cleaning.

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Identify potential mentors who have expertise in data cleaning
Reach out to mentors and express your interest
Establish regular communication and ask for guidance
Be open to feedback and suggestions

Review 'Python Data Science Handbook'

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Expand your understanding of data cleaning in Python by reading 'Python Data Science Handbook', which covers essential concepts, techniques, and best practices for working with data in Python.

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Read the chapters on data cleaning and data manipulation
Work through the code examples and exercises
Apply the techniques to your own data cleaning projects

Revisit Basic Python Skills

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Strengthen your foundational Python skills to enhance your ability to effectively apply Pandas for data cleaning.

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Review the basics of Python syntax, data types, and control flow
Practice writing simple Python scripts and functions
Work through exercises and challenges to test your understanding

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Attend a Study Group or Workshop

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Enhance your learning through collaboration by attending study groups or workshops where you can discuss data cleaning challenges, share knowledge, and get feedback from peers.

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Find a study group or workshop that aligns with your interests and learning goals
Attend the sessions regularly and participate actively
Prepare questions and share your insights
Connect with other participants and exchange resources

Follow Along with Pandas Data Cleaning Tutorials

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Enhance your hands-on skills by following step-by-step tutorials that guide you through the process of data cleaning using Pandas, including handling missing values, encoding categorical features, and preparing data for machine learning models.

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Locate relevant Pandas data cleaning tutorials online
Follow the instructions and work through the code examples
Experiment with different techniques and datasets
Troubleshoot any errors you encounter
Adapt the techniques to your own data cleaning projects

Complete Data Cleaning Worksheets

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Reinforce your understanding of data cleaning techniques by completing guided worksheets that cover common issues like missing values, duplicate values, and outlier detection.

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Review the provided worksheets
Work through the exercises, focusing on applying the data cleaning techniques
Check your answers against the provided solutions
Identify areas where you need additional practice

Develop a Data Cleaning Pipeline

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Apply your data cleaning skills to a real-world dataset by creating a comprehensive data cleaning pipeline that includes handling missing values, dealing with outliers, and preparing the data for use in machine learning models.

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Gather a suitable dataset for your project
Identify and address the different types of data issues present in the dataset
Write code to implement the data cleaning steps
Test and evaluate the effectiveness of your pipeline
Document your pipeline and share it with others

Create a Resource Pack for Pandas Data Cleaning

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Enhance your knowledge of Pandas data cleaning by compiling a comprehensive resource pack that includes tutorials, articles, code snippets, and datasets relevant to the course material.

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Search for and gather high-quality resources related to Pandas data cleaning
Organize the resources into a structured and accessible format
Include a brief description and analysis of each resource
Consider creating additional materials, such as cheat sheets or summaries
Share your resource pack with the community

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Financial Analyst

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.

See salaries and explore the career path for Market Researcher

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Database Administrator

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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