À propos de ce cours
4.3
383 notes
84 avis

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Dates limites flexibles

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Niveau débutant

Approx. 14 heures pour terminer

Recommandé : 14 hours/week...

Anglais

Sous-titres : Anglais, Vietnamien

Compétences que vous acquerrez

StatisticsData ScienceInternet Of Things (IOT)Apache Spark

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau débutant

Approx. 14 heures pour terminer

Recommandé : 14 hours/week...

Anglais

Sous-titres : Anglais, Vietnamien

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
4 heures pour terminer

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 vidéos (Total 3 min), 1 lecture, 3 quiz
2 vidéos
Overview of technology used within the course1 min
1 lecture
Latest Video summary on environment setup10 min
1 exercice pour s'entraîner
Challenges, terminology, methods and technology2 min
Semaine
2
4 heures pour terminer

Tools that support IoT 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 vidéos (Total 52 min), 4 quiz
8 vidéos
ApacheSpark and how it supports the data scientist7 min
Programming language options on ApacheSpark10 min
Functional programming basics6 min
Introduction of Cloudant2 min
ApacheSparkSQL6 min
Overview of how the test data has been generated (optional)8 min
IBM Watson Studio (formerly Data Science Experience)3 min
3 exercices pour s'entraîner
Data storage solutions, and ApacheSpark12 min
Programming language options and functional programming12 min
ApacheSparkSQL, Cloudant, and the End to End Scenario12 min
Semaine
3
4 heures pour terminer

Mathematical Foundations on Exploratory Data Analysis

This learning module explores mathematical foundations supporting Exploratory Data Analysis (EDA) techniques. ...
7 vidéos (Total 35 min), 1 lecture, 4 quiz
7 vidéos
Averages5 min
Standard deviation3 min
Skewness3 min
Kurtosis2 min
Covariance, Covariance matrices, correlation13 min
Multidimensional vector spaces5 min
1 lecture
Exercise 210 min
3 exercices pour s'entraîner
Averages and standard deviation10 min
Skewness and kurtosis10 min
Covariance, correlation and multidimensional Vector Spaces16 min
Semaine
4
4 heures pour terminer

Data Visualization

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 vidéos (Total 24 min), 2 lectures, 2 quiz
4 vidéos
Plotting with ApacheSpark and python's matplotlib12 min
Dimensionality reduction4 min
PCA5 min
2 lectures
Exercise 3.110 min
Exercise 3.210 min
1 exercice pour s'entraîner
Visualization and dimension reduction10 min
4.3
84 avisChevron Right

64%

a commencé une nouvelle carrière après avoir terminé ces cours

44%

a bénéficié d'un avantage concret dans sa carrière grâce à ce cours

Meilleurs avis

par HSSep 10th 2017

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

par MTFeb 8th 2019

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

Enseignant

Avatar

Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT

À propos de 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....

À propos de la Spécialisation 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 ibm.biz/badging....
Advanced Data Science with IBM

Foire Aux Questions

  • 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.

  • 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.

  • 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.

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