課程信息
4.5
16,396 個評分
3,386 個審閱
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

Globe

100% 在線課程

立即開始,按照自己的計劃學習。
Calendar

可靈活調整截止日期

根據您的日程表重置截止日期。
Clock

Approx. 8 hours to complete

建議:1-4 hours/week...
Comment Dots

English

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

您將學到的內容有

  • 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

您將獲得的技能

Data ScienceGithubR ProgrammingRstudio
Stacks

Course 1 of 10 in the

Globe

100% 在線課程

立即開始,按照自己的計劃學習。
Calendar

可靈活調整截止日期

根據您的日程表重置截止日期。
Clock

Approx. 8 hours to complete

建議:1-4 hours/week...
Comment Dots

English

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

教學大綱 - 您將從這門課程中學到什麼

Week
1
Clock
完成時間為 2 小時

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 個視頻(共 51 分鐘), 5 個閱讀材料, 1 個測驗
Video16 個視頻
The Data Scientist's Toolbox5分鐘
Getting Help8分鐘
Finding Answers4分鐘
R Programming Overview2分鐘
Getting Data Overview1分鐘
Exploratory Data Analysis Overview1分鐘
Reproducible Research Overview1分鐘
Statistical Inference Overview1分鐘
Regression Models Overview1分鐘
Practical Machine Learning Overview1分鐘
Building Data Products Overview1分鐘
Installing R on Windows {Roger Peng}3分鐘
Install R on a Mac {Roger Peng}2分鐘
Installing Rstudio {Roger Peng}1分鐘
Installing Outside Software on Mac (OS X Mavericks)1分鐘
Reading5 個閱讀材料
Welcome to the Data Scientist's Toolbox10分鐘
Pre-Course Survey10分鐘
Syllabus10分鐘
Specialization Textbooks10分鐘
The Elements of Data Analytic Style10分鐘
Quiz1 個練習
Week 1 Quiz10分鐘
Week
2
Clock
完成時間為 1 小時

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 個視頻(共 51 分鐘), 1 個測驗
Video9 個視頻
Command Line Interface16分鐘
Introduction to Git4分鐘
Introduction to Github3分鐘
Creating a Github Repository5分鐘
Basic Git Commands5分鐘
Basic Markdown2分鐘
Installing R Packages5分鐘
Installing Rtools2分鐘
Quiz1 個練習
Week 2 Quiz10分鐘
Week
3
Clock
完成時間為 1 小時

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 個視頻(共 35 分鐘), 1 個測驗
Video4 個視頻
What is Data?5分鐘
What About Big Data?4分鐘
Experimental Design15分鐘
Quiz1 個練習
Week 3 Quiz10分鐘
Week
4
Clock
完成時間為 2 小時

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 個閱讀材料, 1 個測驗
Reading1 個閱讀材料
Post-Course Survey10分鐘
4.5
Direction Signs

36%

完成這些課程後已開始新的職業生涯
Briefcase

83%

通過此課程獲得實實在在的工作福利

熱門審閱

突出顯示
Introductory course
(1056)
Foundational tools
(243)
創建者 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.

創建者 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.

講師

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

關於 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....

關於 Data Science 專項課程

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

常見問題

  • 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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