课程信息
4.5
15,922 ratings
3,306 reviews
In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio....
Stacks

Course 1 of 10 in the

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100% 在线课程

立即开始,按照自己的计划学习。
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建议:1-4 hours/week

完成时间大约为8 小时
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English

字幕:English, French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew

您将学到的内容有

  • Check
    Create a Github repository
  • Check
    Explain essential study design concepts
  • Check
    Set up R, R-Studio, Github and other useful tools
  • Check
    Understand the data, problems, and tools that data analysts work with

您将获得的技能

GithubRstudioData ScienceR Programming
Stacks

Course 1 of 10 in the

Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Clock

建议:1-4 hours/week

完成时间大约为8 小时
Comment Dots

English

字幕:English, French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew

教学大纲 - 您将从这门课程中学到什么

1

章节
Clock
完成时间为 2 小时

Week 1

During Week 1, you'll learn about the goals and objectives of the Data Science Specialization and each of its components. You'll also get an overview of the field as well as instructions on how to install R....
Reading
16 个视频(共 51 分钟), 5 个阅读材料, 1 个测验
Video16 个视频
The Data Scientist's Toolbox5分钟
Getting Help8分钟
Finding Answers4分钟
R Programming Overview2分钟
Getting Data Overview1分钟
Exploratory Data Analysis Overview1分钟
Reproducible Research Overview1分钟
Statistical Inference Overview1分钟
Regression Models Overview1分钟
Practical Machine Learning Overview1分钟
Building Data Products Overview1分钟
Installing R on Windows {Roger Peng}3分钟
Install R on a Mac {Roger Peng}2分钟
Installing Rstudio {Roger Peng}1分钟
Installing Outside Software on Mac (OS X Mavericks)1分钟
Reading5 个阅读材料
Welcome to the Data Scientist's Toolbox10分钟
Pre-Course Survey10分钟
Syllabus10分钟
Specialization Textbooks10分钟
The Elements of Data Analytic Style10分钟
Quiz1 个练习
Week 1 Quiz10分钟

2

章节
Clock
完成时间为 1 小时

Week 2: Installing the Toolbox

This is the most lecture-intensive week of the course. The primary goal is to get you set up with R, Rstudio, Github, and the other tools we will use throughout the Data Science Specialization and your ongoing work as a data scientist. ...
Reading
9 个视频(共 51 分钟), 1 个测验
Video9 个视频
Command Line Interface16分钟
Introduction to Git4分钟
Introduction to Github3分钟
Creating a Github Repository5分钟
Basic Git Commands5分钟
Basic Markdown2分钟
Installing R Packages5分钟
Installing Rtools2分钟
Quiz1 个练习
Week 2 Quiz10分钟

3

章节
Clock
完成时间为 1 小时

Week 3: Conceptual Issues

The Week 3 lectures focus on conceptual issues behind study design and turning data into knowledge. If you have trouble or want to explore issues in more depth, please seek out answers on the forums. They are a great resource! If you happen to be a superstar who already gets it, please take the time to help your classmates by answering their questions as well. This is one of the best ways to practice using and explaining your skills to others. These are two of the key characteristics of excellent data scientists. ...
Reading
4 个视频(共 35 分钟), 1 个测验
Video4 个视频
What is Data?5分钟
What About Big Data?4分钟
Experimental Design15分钟
Quiz1 个练习
Week 3 Quiz10分钟

4

章节
Clock
完成时间为 2 小时

Week 4: Course Project Submission & Evaluation

In Week 4, we'll focus on the Course Project. This is your opportunity to install the tools and set up the accounts that you'll need for the rest of the specialization and for work in data science....
Reading
1 个阅读材料, 1 个测验
Reading1 个阅读材料
Post-Course Survey10分钟
4.5
Direction Signs

36%

完成这些课程后已开始新的职业生涯
Briefcase

83%

通过此课程获得实实在在的工作福利

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Introductory course
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Foundational tools
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创建者 LRSep 8th 2017

It was really insightful, coming from knowing almost nothing about statistics or experimental design, it was easy to understand while not feeling shallow. Just the right amount of information density.

创建者 AMJul 22nd 2017

Great Primer for what Data Science is about. It also provides the infrastructure of tools needed. This was what I was after, a way to provide other data scientist hardware and infrastructure support.

讲师

Jeff Leek, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

关于 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....

关于 Data Science 专项课程

Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material....
Data Science

常见问题

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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