“Getting a data science job would have been much harder without Dataquest. It’s a great product. I recommend it to anyone who asks me about how to get started.”

Data Science Manager @ Later

## Course overview

Data cleaning is a necessary skill for anyone who wants to work in a data-related field.

You’ll start this course by learning how to identify data cleaning needs prior to analysis, how to use functionals for data cleaning, how to practice string manipulation, how to work with relational data, and how to reshape data using tools from the tidyverse. You’ll create correlation matrices to identify trends in your data, and then you’ll then learn how to deal with missing values in your dataset.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll work on a guided project to analyze parents’, students’, and teachers’ perceptions of NYC schools. You’ll learn to work with survey data — specifically how to import, simplify, and reshape the data. You’ll also learn about R Notebooks and how you can use them to showcase your work.

## Key skills

• Manipulating DataFrames with new tools
• Resolving missing data
• Joining DataFrames
• Reshaping data using the tidyr package

## Course outline

### Data Cleaning With R 2h

Lesson Objectives
• Identify data cleaning needs prior to analysis
• Simplify DataFrames
• Change the data types of multiple variables at once
• Create new variables by calculating summary statistics from existing variables
• Employ functionals to check for duplicated observations

### String Manipulation 1h

Lesson Objectives
• Manipulate strings to create new variables
• Manipulate strings to Update variables

### Relational Data 1h

Lesson Objectives
• Define relational data, joins, and keys
• Combine DataFrames using joins

### Correlations and Reshaping Data 1h

Lesson Objectives
• Reshape data using tidyverse tools
• Identify relationships between variables using correlation analysis
• Identify trends in your data using correlation matrices

### Dealing With Missing Data 1h

Lesson Objectives
• Omit missing values from calculations
• Resolve missing values using different approaches
• Perform analysis when data is missing

### Guided Project: NYC Schools Perceptions 1h

Lesson Objectives
• Employ R Notebooks to showcase your work
• Reshape a large survey dataset
• Interpret metadata to inform your data cleaning
• Explore perceptions of NYC schools using data visualization and correlation

## Projects in this course

### Guided Project: NYC Schools Perceptions

For this project, you’ll become a data analyst using R Notebooks to clean, reshape and visualize NYC school survey data, uncovering insights into school quality perceptions.

## The Dataquest guarantee

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

## Master skills faster with Dataquest

### Go from zero to job-ready

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

### Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

### Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

### Showcase your path certification

Share the evidence of your hard work with your network and potential employers.

## Grow your career withDataquest.

98%
of learners recommend
Dataquest for career advancement
4.85
Dataquest rating
SwitchUp Best Bootcamps
\$30k
Average salary boost
for learners who complete a path

### Aaron Melton

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”

### Jessica Ko

#### Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

### Victoria E. Guzik

#### Associate Data Scientist at Callisto Media

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”

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