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.
課程信息
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您將學到的內容有
Create a Github repository
Explain essential study design concepts
Set up R, R-Studio, Github and other useful tools
Understand the data, problems, and tools that data analysts work with
您將獲得的技能
Data ScienceGithubR ProgrammingRstudio
教學大綱 - 您將從這門課程中學到什麼
週
1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.
16 個視頻 （總計 51 分鐘）, 5 個閱讀材料, 1 個測驗
16 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
5 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
1 個練習
Week 1 Quiz10分鐘
週
2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.
9 個視頻 （總計 51 分鐘）, 1 個測驗
9 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
1 個練習
Week 2 Quiz10分鐘
週
3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.
4 個視頻 （總計 35 分鐘）, 1 個測驗
1 個練習
Week 3 Quiz10分鐘
週
4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.
1 個閱讀材料, 1 個測驗
1 個閱讀材料
Post-Course Survey10分鐘
4.5
3834 個審閱38%
完成這些課程後已開始新的職業生涯
34%
通過此課程獲得實實在在的工作福利
來自数据科学家的工具箱（中文版）的熱門評論
突出顯示
Introductory course
(1056)Foundational tools
(243)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.
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, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
關於 约翰霍普金斯大学
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 数据科学 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.
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