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.
제공자:
About this Course
최근 조회 345,891회
배울 내용
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
귀하가 습득할 기술
Data ScienceGithubR ProgrammingRstudio
Course을(를) 수강하는 학습자
- Data Analysts
- Scientists
- Biostatisticians
- Geologists
- Economists
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 2시간 필요
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.
16개 동영상 (총 51분), 5 readings, 1 quiz
16개의 동영상
The Data Scientist's Toolbox5m
Getting Help8m
Finding Answers4m
R Programming Overview2m
Getting Data Overview1m
Exploratory Data Analysis Overview1m
Reproducible Research Overview1m
Statistical Inference Overview1m
Regression Models Overview1m
Practical Machine Learning Overview1m
Building Data Products Overview1m
Installing R on Windows {Roger Peng}3m
Install R on a Mac {Roger Peng}2m
Installing Rstudio {Roger Peng}1m
Installing Outside Software on Mac (OS X Mavericks)1m
5개의 읽기 자료
Welcome to the Data Scientist's Toolbox10m
Pre-Course Survey10m
Syllabus10m
Specialization Textbooks10m
The Elements of Data Analytic Style10m
1개 연습문제
Week 1 Quiz10m
주
2완료하는 데 1시간 필요
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.
9개 동영상 (총 51분), 1 quiz
9개의 동영상
Command Line Interface16m
Introduction to Git4m
Introduction to Github3m
Creating a Github Repository5m
Basic Git Commands5m
Basic Markdown2m
Installing R Packages5m
Installing Rtools2m
1개 연습문제
Week 2 Quiz10m
주
3완료하는 데 1시간 필요
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.
4개 동영상 (총 35분), 1 quiz
1개 연습문제
Week 3 Quiz10m
주
4완료하는 데 2시간 필요
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 reading, 1 quiz
1개의 읽기 자료
Post-Course Survey10m
4.5
4076개의 리뷰38%
이 강좌를 수료한 후 새로운 경력 시작하기
35%
이 강좌를 통해 확실한 경력상 이점 얻기
The Data Scientist’s Toolbox의 최상위 리뷰
하이라이트
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.
강사
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
존스홉킨스대학교 정보
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.
데이터 과학 전문 분야 정보
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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