About this course: Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
Created by: Johns Hopkins University
Taught by: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| Basic Info | Course 3 of 10 in the Data Science Specialization |
| Language | English, Subtitles: Russian, French, Chinese (Simplified) |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Week 1
In this first week of the course, we look at finding data and reading different file types.
9 videos, 4 readings
- 阅读: Welcome to Week 1
- 阅读: Syllabus
- 阅读: Pre-Course Survey
- Video: Raw and Processed Data
- Video: Components of Tidy Data
- Video: Downloading Files
- Video: Reading Local Files
- Video: Reading Excel Files
- Video: Reading XML
- Video: Reading JSON
- Video: The data.table Package
- 阅读: Practical R Exercises in swirl Part 1
Graded: Week 1 Quiz
WEEK 2
Week 2
Welcome to Week 2 of Getting and Cleaning Data! The primary goal is to introduce you to the most common data storage systems and the appropriate tools to extract data from web or from databases like MySQL.
5 videos
- Video: Reading from HDF5
- Video: Reading from The Web
- Video: Reading From APIs
- Video: Reading From Other Sources
Graded: Week 2 Quiz
WEEK 3
Week 3
Welcome to Week 3 of Getting and Cleaning Data! This week the lectures will focus on organizing, merging and managing the data you have collected using the lectures from Weeks 1 and 2.
7 videos, 1 reading
- Video: Summarizing Data
- Video: Creating New Variables
- Video: Reshaping Data
- Video: Managing Data Frames with dplyr - Introduction
- Video: Managing Data Frames with dplyr - Basic Tools
- Video: Merging Data
- 阅读: Practical R Exercises in swirl Part 2
- 未分级程序开发: swirl Lesson 1: Manipulating Data with dplyr
- 未分级程序开发: swirl Lesson 2: Grouping and Chaining with dplyr
- 未分级程序开发: swirl Lesson 3: Tidying Data with tidyr
Graded: Week 3 Quiz
WEEK 4
Week 4
Welcome to Week 4 of Getting and Cleaning Data! This week we finish up with lectures on text and date manipulation in R. In this final week we will also focus on peer grading of Course Projects.
5 videos, 2 readings
- Video: Regular Expressions I
- Video: Regular Expressions II
- Video: Working with Dates
- Video: Data Resources
- 阅读: Practical R Exercises in swirl Part 4
- 未分级程序开发: swirl Lesson 1: Dates and Times with lubridate
- 阅读: Post-Course Survey
Graded: Week 4 Quiz
Graded: Getting and Cleaning Data Course Project
FAQs
How It Works
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
来自同学的帮助
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
证书
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
Creators
Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Pricing
| Purchase Course | |
|---|---|
| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Ratings and Reviews
Excellent course, and quizzes and lecture were very teaching. But some materials needs to be updated up to date , like subsetting columns in data.table the slides were absolute.
Great experience..
Useful, the course is as much an introduction to R- part 2 as about cleaning data
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A very nice course!