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.
À propos de ce cours
4.5
17,049 notes
Ce que vous allez apprendre
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
Compétences que vous acquerrez
Data ScienceGithubR ProgrammingRstudio
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1Week 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 videos (Total 51 min), 5 lectures, 1 quiz
16 vidéos
The Data Scientist's Toolbox5 min
Getting Help8 min
Finding Answers4 min
R Programming Overview2 min
Getting Data Overview1 min
Exploratory Data Analysis Overview1 min
Reproducible Research Overview1 min
Statistical Inference Overview1 min
Regression Models Overview1 min
Practical Machine Learning Overview1 min
Building Data Products Overview1 min
Installing R on Windows {Roger Peng}3 min
Install R on a Mac {Roger Peng}2 min
Installing Rstudio {Roger Peng}1 min
Installing Outside Software on Mac (OS X Mavericks)1 min
5 lectures
Welcome to the Data Scientist's Toolbox10 min
Pre-Course Survey10 min
Syllabus10 min
Specialization Textbooks10 min
The Elements of Data Analytic Style10 min
1 exercice pour s'entraîner
Week 1 Quiz10 min
Semaine
2Week 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 videos (Total 51 min), 1 quiz
9 vidéos
Command Line Interface16 min
Introduction to Git4 min
Introduction to Github3 min
Creating a Github Repository5 min
Basic Git Commands5 min
Basic Markdown2 min
Installing R Packages5 min
Installing Rtools2 min
1 exercice pour s'entraîner
Week 2 Quiz10 min
Semaine
3Week 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 videos (Total 35 min), 1 quiz
1 exercice pour s'entraîner
Week 3 Quiz10 min
Semaine
4Week 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 lecture, 1 quiz
1 lecture
Post-Course Survey10 min
4.5
3,502 avis36%
a commencé une nouvelle carrière après avoir terminé ces cours
83%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Meilleurs avis
Points forts
Introductory course
(1056)Foundational tools
(243)par LR•Sep 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.
par AM•Jul 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.
Enseignants
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
À propos de 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.
À propos de la Spécialisation Data Science
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.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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