À propos de ce cours : 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.
Créé par : Johns Hopkins University
Enseigné par : Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public HealthEnseigné par : Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public HealthEnseigné par : Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| Informations de base | Cours 1 de 10 dans Data Science Specialization |
| Engagement | 1-4 hours/week |
| Langue | English, Sous-titres : French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew, Japanese |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
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 vidéos, 5 lectures
- Reading: Welcome to the Data Scientist's Toolbox
- Reading: Pre-Course Survey
- Reading: Syllabus
- Reading: Specialization Textbooks
- Reading: The Elements of Data Analytic Style
- Vidéo: The Data Scientist's Toolbox
- Vidéo: Getting Help
- Vidéo: Finding Answers
- Vidéo: R Programming Overview
- Vidéo: Getting Data Overview
- Vidéo: Exploratory Data Analysis Overview
- Vidéo: Reproducible Research Overview
- Vidéo: Statistical Inference Overview
- Vidéo: Regression Models Overview
- Vidéo: Practical Machine Learning Overview
- Vidéo: Building Data Products Overview
- Vidéo: Installing R on Windows {Roger Peng}
- Vidéo: Install R on a Mac {Roger Peng}
- Vidéo: Installing Rstudio {Roger Peng}
- Vidéo: Installing Outside Software on Mac (OS X Mavericks)
Noté: Week 1 Quiz
SEMAINE 2
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 vidéos
- Vidéo: Command Line Interface
- Vidéo: Introduction to Git
- Vidéo: Introduction to Github
- Vidéo: Creating a Github Repository
- Vidéo: Basic Git Commands
- Vidéo: Basic Markdown
- Vidéo: Installing R Packages
- Vidéo: Installing Rtools
Noté: Week 2 Quiz
SEMAINE 3
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 vidéos
- Vidéo: What is Data?
- Vidéo: What About Big Data?
- Vidéo: Experimental Design
Noté: Week 3 Quiz
SEMAINE 4
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 lecture
- Reading: Post-Course Survey
Noté: Course Project
FAQ
Comment cela fonctionne
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
Thank you very much for your support.
got a general view of tools that data scientists need to know about or master. really useful and inspiring. this course encourages me to figure out problems I might meet during data analyzing like a data scientist. cheers.
Brilliant !
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A very useful course with a lot to learn about the basics of programming and data analysis. I have learned a lot of things and also this course has enhanced my confidence level to a higher level. Thank you coursera and John Hopkins University for providing such a wonderful opportunity to all.