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.
Este curso faz parte do Programa de cursos integrados Ciência de Dados
As Ferramentas do Cientista de Dados
oferecido por
Informações sobre o curso
4.5
19,036 classificações
O que você vai aprender
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
Habilidades que você terá
Data ScienceGithubR ProgrammingRstudio
Programa - O que você aprenderá com este curso
Semana
12 horas para concluir
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 vídeos (total de (Total 51 mín.) min), 5 leituras, 1 teste
16 videos
The Data Scientist's Toolbox5min
Getting Help8min
Finding Answers4min
R Programming Overview2min
Getting Data Overview1min
Exploratory Data Analysis Overview1min
Reproducible Research Overview1min
Statistical Inference Overview1min
Regression Models Overview1min
Practical Machine Learning Overview1min
Building Data Products Overview1min
Installing R on Windows {Roger Peng}3min
Install R on a Mac {Roger Peng}2min
Installing Rstudio {Roger Peng}1min
Installing Outside Software on Mac (OS X Mavericks)1min
5 leituras
Welcome to the Data Scientist's Toolbox10min
Pre-Course Survey10min
Syllabus10min
Specialization Textbooks10min
The Elements of Data Analytic Style10min
1 exercício prático
Week 1 Quiz10min
Semana
21 hora para concluir
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 vídeos (total de (Total 51 mín.) min), 1 teste
9 videos
Command Line Interface16min
Introduction to Git4min
Introduction to Github3min
Creating a Github Repository5min
Basic Git Commands5min
Basic Markdown2min
Installing R Packages5min
Installing Rtools2min
1 exercício prático
Week 2 Quiz10min
Semana
31 hora para concluir
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 vídeos (total de (Total 35 mín.) min), 1 teste
1 exercício prático
Week 3 Quiz10min
Semana
42 horas para concluir
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 leitura, 1 teste
1 leituras
Post-Course Survey10min
38%
comecei uma nova carreira após concluir estes cursos
34%
consegui um benefício significativo de carreira com este curso
Melhores avaliações
Destaques
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.
This course was a good intro especially in setting all the necessary software for future courses. I suggest to read the manuals, books and other readings the profs suggest. The resources are helpful.
Instrutores
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
Sobre Universidade Johns Hopkins
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.
Sobre o Programa de cursos integrados Ciência de Dados
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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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