What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Cognitive Class Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Data Science Experience and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.
oferecido por
IBMInformações sobre o curso
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Resultados de carreira do aprendiz
46%
comecei uma nova carreira após concluir estes cursos
45%
consegui um benefício significativo de carreira com este curso
19%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível iniciante
Aprox. 15 horas para completar
Inglês
Legendas: Inglês
Resultados de carreira do aprendiz
46%
comecei uma nova carreira após concluir estes cursos
45%
consegui um benefício significativo de carreira com este curso
19%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível iniciante
Aprox. 15 horas para completar
Inglês
Legendas: Inglês
oferecido por
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
Programa - O que você aprenderá com este curso
4 horas para concluir
Data Scientist's Toolkit
This week, you will get an overview of the programming languages commonly used, including Python, R, Scala, and SQL. You’ll be introduced to the open source and commercial data science tools available. You’ll also learn about the packages, APIs, data sets and models frequently used by data scientists.
4 horas para concluir
17 vídeos (Total 84 mín.), 1 leitura, 4 testes
17 videos
Languages of Data Science2min
Introduction to Python3min
Introduction to R Language3min
Introduction to SQL3min
Other Languages6min
Categories of Data Science Tools2min
Open Source Tools for Data Science - Part 17min
Open Source Tools for Data Science - Part 25min
Commercial Tools for Data Science5min
Cloud Based Tools for Data Science8min
Libraries for Data Science4min
Application Programming Interfaces (API)4min
Data Sets - Powering Data Science6min
Sharing Enterprise Data - Data Asset eXchange3min
Machine Learning Models7min
The Model Asset Exchange5min
1 leituras
Lab - Explore Data Sets and Models 10min
4 exercícios práticos
Practice Quiz - Languages 30min
Practice Quiz - Tools30min
Practice Quiz - Packages, APIs, Data Sets, Models30min
Graded Quiz30min
6 horas para concluir
Open Source Tools
This week, you will learn about three popular tools used in data science: GitHub, Jupyter Notebooks, and RStudio IDE. You will become familiar with the features of each tool, and what makes these tools so popular among data scientists today.
6 horas para concluir
10 vídeos (Total 57 mín.), 6 leituras, 7 testes
10 videos
GitHub on Mac/Linux - Part 19min
GitHub on Mac/Linux - Part 2 (Optional)5min
GitHub on Mac/Linux - Part 3 (Optional)8min
Getting Started with Jupyter Notebook5min
Getting Started with JupyterLab6min
Jupyter Architecture6min
What is RStudio IDE?4min
Installing Packages and Loading Libraries in RStudio IDE2min
Plotting Within RStudio IDE3min
6 leituras
GitHub Part 1 Instructions for Windows10min
GitHub Part 2 Instructions for Windows (Optional)10min
GitHub Part 3 Instructions for Windows (Optional)10min
Jupyter Notebooks on the Internet10min
Lab: RStudio – The Basics10min
Lab: Creating an Interactive Map in R30min
4 exercícios práticos
Practice Quiz - GitHub30min
Practice Quiz - Jupyter Notebook30min
Practice Quiz - RStudio IDE30min
Graded Quiz30min
3 horas para concluir
IBM Tools for Data Science
This week, you will learn about an enterprise-ready data science platform by IBM, called Watson Studio. You'll learn about some of the features and capabilities of what data scientists use in the industry. You’ll also learn about other IBM tools used to support data science projects, such as IBM Watson Knowledge Catalog, Data Refinery, and the SPSS Modeler.
3 horas para concluir
15 vídeos (Total 72 mín.), 2 leituras, 3 testes
15 videos
Watson Studio Introduction4min
Creating an Account on IBM Watson Studio2min
Jupyter Notebook in Watson Studio - Part 1 2min
Jupyter Notebook in Watson Studio - Part 23min
Linking GitHub to Watson Studio2min
Other IBM Tools for Data Science1min
IBM Watson Knowledge Catalog6min
Data Refinery7min
SPSS Modeler Flows in Watson Studio6min
IBM SPSS Modeler7min
SPSS Statistics7min
Model Deployment with Watson Machine Learning4min
Auto AI in Watson Studio4min
IBM Watson OpenScale7min
2 leituras
Lab: Creating a Watson Studio Project with Jupyter Notebook10min
Lab: Modeler Flows in Watson Studio10min
3 exercícios práticos
Practice Quiz - Watson Studio30min
Practice Quiz - Other IBM Tools30min
Graded Quiz30min
3 horas para concluir
Final Assignment: Create and Share Your Jupyter Notebook
This week, you will demonstrate your skills by creating and configuring a Jupyter Notebook. As part of your grade for this course, you will share your Jupyter Notebook with your peers for review.
3 horas para concluir
1 leitura
1 leituras
IBM Digital Badge2min
Avaliações
Principais avaliações do TOOLS FOR DATA SCIENCE
por CB1 de Nov de 2018
Tools are fantastic and will make a significant contribution to my education. Videos need to be updated for changes to Watson Studios. Support from IBM on their cloud services should also be improved.
por RR24 de Abr de 2019
To the contrast of other reviews, I find the content very well bifurcated and fed to the learners. The course very easily digestable and I have had a great amount of fun learning it.. Go for it!!!!
por FC5 de Nov de 2019
It would be nice if you could update the material since some tools have changed either name or the way they look compared to the videos/images. Very good material though, I enjoyed the course much.
por AP11 de Jan de 2020
Excellent introduction to a variety of open source tools available and commonly used by Data Science Professionals. Some of the video material is slightly outdated due to updates to the platform.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
O que recebo ao me inscrever para este certificado?
Quando você se inscreve em um curso, ganha acesso a todos os cursos no certificado e obtém um certificado quando completar o trabalho. Seu certificado eletrônico pode ser adicionado à sua página de Participações, de onde você pode imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, pode participar como ouvinte sem custo.
Qual é a política de reembolso?
Se você se inscrever, terá 7 dias para testar sem custo e, durante este período, pode cancelar sem multa. Depois disso, não reembolsamos, mas você pode cancelar sua inscrição a qualquer momento. Veja nossa política para o reembolso total.
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