Data Management and Preparation Using R from Pluralsight

Data management and data preparation is a very important yet widely overlooked part of data analysis. Importing, selecting a proper class, cleaning, and filtering are all part of data preparation and will be taught in this course.

Have you ever encountered problems in data analysis just because the data was not clean, had a wrong format, or was simply messy? Data preparation is an immensely important yet overlooked field in data science. Most of the time of a data professional is not spent analyzing or visualizing, it is spent getting data ready as clean and well-structured as possible. R is a widely used open source tool with an active user community. This community created high quality add on packages for data preparation. In this course, Data Management and Preparation Using R, you will not only learn about data preparation in R Base, you will also learn about those add on packages that make R so powerful. First, you'll learn about data importing, cleaning, and structuring (selecting the right class). Next, you'll explore data querying. Finally, you will learn about dplyr, tidyr, reshape2 and data.table. At the end of this course, you will be able to select the right tools and efficiently perform data import, formatting, cleaning, and querying.

What's inside

Syllabus

Course Overview

Introduction

Selecting Suitable Classes and Importing Data

Cleaning Data with tidyr

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers foundational and intermediate skills in R, preparing learners for a deeper dive into data science

Imparts practical knowledge on leveraging R packages for efficient data preparation, addressing a critical aspect of data analysis often overlooked

Introduces dplyr, tidyr, reshape2, and data.table, extending learners' toolkit for data manipulation and analysis

Suitable for learners with basic experience in data analysis or programming, providing a solid foundation for further studies

Taught by Martin Burger, an experienced instructor in data science and machine learning

Part of a comprehensive series of courses on data science, providing a structured learning path for those seeking to advance their skills

Reviews summary

Practical r for data management & preparation

According to students, this course offers a solid and practical foundation in R for data management and preparation. Many learners commend the clear explanations, engaging instructor, and hands-on exercises, particularly for `dplyr` and `tidyr`, as being incredibly helpful for tackling real-world data cleaning challenges. While it is deemed excellent for beginners and those building core skills, some more experienced learners found certain sections lacking depth, especially regarding `data.table` or advanced optimization techniques. This suggests it might be better suited for foundational understanding rather than comprehensive advanced topics.

The hands-on exercises and practical focus are highlighted as highly valuable for skill development.

"I found the hands-on exercises incredibly helpful for solidifying my understanding."

"As a data analyst, data cleaning is 80% of my job. This course directly addressed that pain point."

"The hands-on coding and projects are the strongest part of the course for me."

"The hands-on labs were invaluable. I struggled with messy data before, but now I feel confident in handling various data types..."

Reviewers frequently praise the course for its highly clear and practical teaching approach.

"The instructor explained complex R concepts for data management, especially `dplyr` and `tidyr`, in a very clear and practical way."

"The instructor's pace was perfect, and the material was relevant to real-world data scenarios."

"I appreciated the clear examples. I'd say it's best for those with little to no prior R experience in data cleaning."

"The logical flow of topics and the practical exercises truly reinforced the concepts."

While `dplyr` and `tidyr` are well-covered, the `data.table` section is seen as less comprehensive.

"The `data.table` section felt a bit rushed compared to `dplyr`, and I wish there were more advanced examples or challenges."

"I found the coverage of `data.table` quite superficial. I had to look up external resources to fully grasp its capabilities."

"The `reshape2` part felt a bit dated since `tidyr` is more common now."

The course excels at basics but may not satisfy those seeking advanced or performance-oriented techniques.

"I found some parts a bit simplistic if you already have some R experience. More complex case studies would have been beneficial."

"My main critique is that while it covered the core functions, it didn't delve much into performance optimization for large datasets, which would have been useful for my work."

"I felt this course lacked depth. It touched on many topics but didn't go deep enough into any of them. I needed more advanced techniques for my daily work."

"It's great for absolute beginners, but intermediate users might find themselves skipping sections."

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 Data Management and Preparation Using R with these activities:

R Beginner Refresher

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Brush up your knowledge and skills in R to ensure you have a strong foundation before taking this course.

Browse courses on R Programming

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Revisit R syntax and data types
Review basic data manipulation functions

Read R for Data Science

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Gain a deeper understanding of R concepts through a comprehensive and beginner-friendly book, providing a solid foundation for this course.

View R for Data Science: Import, Tidy, Transform,... on Amazon

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Read chapters 1-3 to understand R basics, data types, and data manipulation
Work through the exercises to reinforce your learning

Complete R Data Manipulation Tutorial

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Follow a structured tutorial to learn essential data manipulation techniques in R, building your proficiency in data preparation.

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Find a comprehensive tutorial on R data manipulation
Work through the tutorial, completing all exercises and assignments

Four other activities

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Show all seven activities

Data Cleaning Exercises

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Enhance your data cleaning skills by working through guided exercises, improving your ability to handle messy data in this course.

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Complete at least 10 exercises on data cleaning and manipulation
Review solutions and identify areas for improvement

Create Data Preparation Cheat Sheet

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Reinforce your learning and improve recall by creating a cheat sheet that summarizes key data preparation concepts and techniques.

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Organize and summarize important concepts from the course
Include examples and code snippets for easy reference

Data Cleaning Project

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Apply your data preparation skills to a real-world dataset, enhancing your ability to handle complex and messy data in practice.

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Find a suitable dataset that requires significant cleaning and preparation
Apply the techniques learned in this course to clean and prepare the dataset
Analyze the cleaned dataset and draw insights

Connect with Experienced Data Scientists

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Seek guidance and support from experienced data scientists to enhance your learning and career development.

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Attend industry events and meetups
Connect with data scientists on LinkedIn

Career center

Learners who complete Data Management and Preparation Using R will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist needs to be able to manage and prepare data in order to analyze it and draw conclusions. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you a excellent foundation for a career as a Data Scientist.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst needs to be able to manage and prepare data in order to analyze it and draw conclusions. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you a strong foundation for a career as a Data Analyst.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer needs to be able to manage and prepare data in order to make it available for analysis. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for storage. You will also learn how to use R to perform data querying and filtering. This course will give you a strong foundation for a career as a Data Engineer.

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Statistician

A Statistician needs to be able to manage and prepare data in order to analyze it and draw conclusions. This course will teach you how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you the skills you need to succeed as a Statistician.

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Machine Learning Engineer

A Machine Learning Engineer needs to be able to manage and prepare data in order to build and train machine learning models. This course will teach you how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you the skills you need to succeed as a Machine Learning Engineer.

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Data Science Manager

A Data Science Manager needs to have a strong understanding of data management and preparation in order to effectively lead a team of data scientists. This course will teach you how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you the skills you need to succeed as a Data Science Manager.

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Business Analyst

A Business Analyst needs to be able to manage and prepare data in order to analyze it and draw conclusions. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you a strong foundation for a career as a Business Analyst.

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Financial Analyst

A Financial Analyst needs to be able to manage and prepare data in order to analyze it and draw conclusions. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you a strong foundation for a career as a Financial Analyst.

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Operations Research Analyst

An Operations Research Analyst needs to be able to manage and prepare data in order to analyze it and draw conclusions. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you a strong foundation for a career as an Operations Research Analyst.

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Software Engineer

A Software Engineer needs to be able to manage and prepare data in order to develop and test software applications. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you a strong foundation for a career as a Software Engineer.

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Quantitative Analyst

A Quantitative Analyst needs to be able to manage and prepare data in order to analyze it and draw conclusions. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you a strong foundation for a career as a Quantitative Analyst.

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Actuary

An Actuary needs to be able to manage and prepare data in order to analyze it and draw conclusions. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you a strong foundation for a career as an Actuary.

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Data Architect

A Data Architect needs to be able to manage and prepare data in order to design and implement data architectures. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you a strong foundation for a career as a Data Architect.

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Database Administrator

A Database Administrator needs to be able to manage and prepare data in order to maintain and optimize databases. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you a strong foundation for a career as a Database Administrator.

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Data Visualization Specialist

A Data Visualization Specialist needs to be able to manage and prepare data in order to create visualizations that communicate data insights. This course in Data Management and Preparation Using R will help you develop the skills you need to succeed. You will learn how to import data, clean it, and prepare it for analysis. You will also learn how to use R to perform data querying and filtering. This course will give you a strong foundation for a career as a Data Visualization Specialist.

See salaries and explore the career path for Data Visualization Specialist