Informações sobre o curso
53,740 visualizações recentes

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível iniciante

Aprox. 13 horas para completar

Sugerido: 14 hours/week...


Legendas: Inglês, Vietnamita

Habilidades que você terá

StatisticsData ScienceInternet Of Things (IOT)Apache Spark

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível iniciante

Aprox. 13 horas para completar

Sugerido: 14 hours/week...


Legendas: Inglês, Vietnamita

Programa - O que você aprenderá com este curso

4 horas para concluir

Introduction to exploratory analysis

Analysis of data starts with a hypothesis and through exploration, those hypothesis are tested. Exploratory analysis in IoT considers large amounts of data, past or current, from multiple sources and summarizes its main characteristics. Data is strategically inspected, cleaned, and models are created with the purpose of gaining insight, predicting future data, and supporting decision making. This learning module introduces methods for turning raw IoT data into insight

2 vídeos ((Total 3 mín.)), 1 leitura, 3 testes
2 videos
Overview of technology used within the course1min
1 leituras
Latest Video summary on environment setup10min
1 exercício prático
Challenges, terminology, methods and technology2min
5 horas para concluir

Tools that support BigData solutions

Data analysis for IoT indicates that you have to build a solution for performing scalable analytics, on a large amount of data that arrives in great volumes and velocity. Such a solution needs to be supported by a number of tools. This module introduces common and popular tools, and highlights how they help data analyst produce viable end-to-end solutions.

8 vídeos ((Total 52 mín.)), 2 leituras, 4 testes
8 videos
Parallel data processing strategies of Apache Spark7min
Programming language options on ApacheSpark10min
Functional programming basics6min
Introduction of Cloudant2min
Resilient Distributed Dataset and DataFrames - ApacheSparkSQL6min
Overview of how the test data has been generated (optional)8min
IBM Watson Studio (formerly Data Science Experience)3min
2 leituras
Apache Parquet (optional)10min
Create the data on your own (optional)10min
3 exercícios práticos
Data storage solutions, and ApacheSpark12min
Programming language options and functional programming12min
ApacheSparkSQL, Cloudant, and the End to End Scenario12min
4 horas para concluir

Scaling Math for Statistics on Apache Spark

This learning module explores mathematical foundations supporting Exploratory Data Analysis (EDA) techniques.

7 vídeos ((Total 35 mín.)), 1 leitura, 4 testes
7 videos
Standard deviation3min
Covariance, Covariance matrices, correlation13min
Multidimensional vector spaces5min
1 leituras
Exercise 210min
3 exercícios práticos
Averages and standard deviation10min
Skewness and kurtosis10min
Covariance, correlation and multidimensional Vector Spaces16min
4 horas para concluir

Data Visualization of Big Data

This learning module details a variety of methods for plotting IoT time series sensor data using different methods in order to gain insights of hidden patterns in your data

4 vídeos ((Total 24 mín.)), 2 leituras, 2 testes
4 videos
Plotting with ApacheSpark and python's matplotlib12min
Dimensionality reduction4min
2 leituras
Exercise 3.110min
Exercise 3.210min
1 exercício prático
Visualization and dimension reduction10min
110 avaliaçõesChevron Right


comecei uma nova carreira após concluir estes cursos


consegui um benefício significativo de carreira com este curso

Principais avaliações do Fundamentals of Scalable Data Science

por HSSep 10th 2017

A perfect course to pace off with exploration towards sensor-data analytics using Apache Spark and python libraries.\n\nKudos man.

por MTFeb 8th 2019

Good course content, however, some of the material especially the IBM cloud environment setup sometimes confusing



Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT

Sobre 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....

Sobre o Programa de cursos integrados Advanced Data Science with IBM

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability. If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link
Advanced Data Science with IBM

Perguntas Frequentes – FAQ

  • 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.

  • 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.

  • If you have started a course that depends on the IBM Bluemix, and your trial has expired, you can continue taking the course on the same environment by providing your credit card information. To avoid being charged, close any application instances you are not using and pay attention to the usage of your environment details.

    Alternative, you can export any projects you are working on. Then, you can register for a new trial using a different email account, not used on IBM Bluemix before. Finally, import the projects to the new account.

    When exporting your projects, for Node-RED use the process used when submitting assignments (export flow form the old project, then import to the new project via clipboard). For Node.js you can redeploy the code to Bluemix using your new account credentials.

    If you have customized your GIT repository, or registered devices, migrating to a new environment will require you to redo those steps to reflect in the new environment.

  • If you already have an IBM Bluemix account, but your trial period has expired, you can always create a new account with a different email address.

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.