About this Course
최근 조회 48,532

다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

초급 단계

완료하는 데 약 13시간 필요

권장: 14 hours/week...

영어

자막: 영어, 베트남어

귀하가 습득할 기술

StatisticsData ScienceInternet Of Things (IOT)Apache Spark

다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

초급 단계

완료하는 데 약 13시간 필요

권장: 14 hours/week...

영어

자막: 영어, 베트남어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 4시간 필요

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 videos (Total 3 min), 1 reading, 3 quizzes
1개의 읽기 자료
Latest Video summary on environment setup10m
1개 연습문제
Challenges, terminology, methods and technology2m
2
완료하는 데 5시간 필요

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 videos (Total 52 min), 2 readings, 4 quizzes
8개의 동영상
Functional programming basics6m
Introduction of Cloudant2m
Resilient Distributed Dataset and DataFrames - ApacheSparkSQL6m
Overview of how the test data has been generated (optional)8m
IBM Watson Studio (formerly Data Science Experience)3m
2개의 읽기 자료
Apache Parquet (optional)10m
Create the data on your own (optional)10m
3개 연습문제
Data storage solutions, and ApacheSpark12m
Programming language options and functional programming12m
ApacheSparkSQL, Cloudant, and the End to End Scenario12m
3
완료하는 데 4시간 필요

Scaling Math for Statistics on Apache Spark

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

...
7 videos (Total 35 min), 1 reading, 4 quizzes
7개의 동영상
Skewness3m
Kurtosis2m
Covariance, Covariance matrices, correlation13m
Multidimensional vector spaces5m
1개의 읽기 자료
Exercise 210m
3개 연습문제
Averages and standard deviation10m
Skewness and kurtosis10m
Covariance, correlation and multidimensional Vector Spaces16m
4
완료하는 데 4시간 필요

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 videos (Total 24 min), 2 readings, 2 quizzes
2개의 읽기 자료
Exercise 3.110m
Exercise 3.210m
1개 연습문제
Visualization and dimension reduction10m
4.3
110개의 리뷰Chevron Right

62%

이 강좌를 수료한 후 새로운 경력 시작하기

50%

이 강좌를 통해 확실한 경력상 이점 얻기

Fundamentals of Scalable Data Science의 최상위 리뷰

대학: HSSep 10th 2017

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

대학: MTFeb 8th 2019

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

강사

Avatar

Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT

IBM 정보

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....

Advanced Data Science with IBM 전문 분야 정보

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

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

  • 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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