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다음 전문 분야의 4개 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 7시간 필요

권장: 5 hours/week...

영어

자막: 영어, 스페인어

다음 전문 분야의 4개 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 7시간 필요

권장: 5 hours/week...

영어

자막: 영어, 스페인어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 1시간 필요

Big Data Rankings & Products

The first module “Big Data Rankings & Products” focuses on the relation and market shares of big data hardware, software, and professional services. This information provides an insight to how future industry, products, services, schools, and government organizations will be influenced by big data technology. To have a deeper view into the world’s top big data products line and service types, the lecture provides an overview on the major big data company, which include IBM, SAP, Oracle, HPE, Splunk, Dell, Teradata, Microsoft, Cisco, and AWS. In order to understand the power of big data technology, the difference of big data analysis compared to traditional data analysis is explained. This is followed by a lecture on the 4 V big challenges of big data technology, which deal with issues in the volume, variety, velocity, and veracity of the massive data. Based on this introduction information, big data technology used in adding global insights on investments, help locate new stores and factories, and run real-time recommendation systems by Wal-Mart, Amazon, and Citibank is introduced.

...
6 videos (Total 28 min), 2 quizzes
6개의 동영상
1.1 Big Data Market Analysis1m
1.2 IBM / 1.3 SAP8m
1.4 Oracle / 1.5 Splunk / 1.6 Accenture / 1.7 Dell / 1.8 Teradata6m
1.9 Microsoft / 1.10 Cisco / 1.11 AWS3m
1.12 Big Data Landscape1m
2개 연습문제
Ungraded Quiz8m
Graded Quiz
2
완료하는 데 1시간 필요

Big Data & Hadoop

The second module “Big Data & Hadoop” focuses on the characteristics and operations of Hadoop, which is the original big data system that was used by Google. The lectures explain the functionality of MapReduce, HDFS (Hadoop Distributed FileSystem), and the processing of data blocks. These functions are executed on a cluster of nodes that are assigned the role of NameNode or DataNodes, where the data processing is conducted by the JobTracker and TaskTrackers, which are explained in the lectures. In addition, the characteristics of metadata types and the differences in the data analysis processes of Hadoop and SQL (Structured Query Language) are explained. Then the Hadoop Release Series is introduced which include the descriptions of Hadoop YARN (Yet Another Resource Negotiator), HDFS Federation, and HDFS HA (High Availability) big data technology.

...
8 videos (Total 68 min), 2 quizzes
8개의 동영상
2.3 Big Data's 4 Vs / 2.4 How is Big Data being Used?8m
2.5 HADOOP11m
2.6 MapReduce vs. RDBMS6m
2.7 MapReduce9m
2.8 Hadoop vs. SQL(RDBMS & RDSMS)12m
2.9 HDFS Enhancements4m
2.10 Hadoop vs. Hadoop YARN6m
2개 연습문제
Ungraded Quiz12m
Graded Quiz
3
완료하는 데 2시간 필요

Spark

The third module “Spark” focuses on the operations and characteristics of Spark, which is currently the most popular big data technology in the world. The lecture first covers the differences in data analysis characteristics of Spark and Hadoop, then goes into the features of Spark big data processing based on the RDD (Resilient Distributed Datasets), Spark Core, Spark SQL, Spark Streaming, MLlib (Machine Learning Library), and GraphX core units. Details of the features of Spark DAG (Directed Acyclic Graph) stages and pipeline processes that are formed based on Spark transformations and actions are explained. Especially, the definition and advantages of lazy transformations and DAG operations are described along with the characteristics of Spark variables and serialization. In addition, the process of Spark cluster operations based on Mesos, Standalone, and YARN are introduced.

...
11 videos (Total 101 min), 2 quizzes
11개의 동영상
3.2 Spark Architecture / 3.3 Spark Family9m
3.4 Spark vs. Hadoop11m
3.5 Spark RDD6m
3.6 Spark Transformations / 3.7 Spark Actions / 3.8 Spark DAG12m
3.9 Spark Programming7m
3.10 Spark Core / 3.11 Spark Variables & Serialization7m
3.12 Spark Cluster Operations / 3.13 Spark Standalone / 3.14 Spark Mesos14m
3.15 Spark YARN9m
3.16 Spark SQL / 3.17 Spark GraphX5m
3.18 Relational DB & Graph DB12m
2개 연습문제
Ungraded Quiz
Graded Quiz
4
완료하는 데 1시간 필요

Spark ML & Streaming

The fourth module “Spark ML & Streaming” focuses on how Spark ML (Machine Learning) works and how Spark streaming operations are conducted. The Spark ML algorithms include featurization, pipelines, persistence, and utilities which operate on the RDDs (Resilient Distributed Datasets) to extract information form the massive datasets. The lectures explain the characteristics of the DataFrame-based API, which is the primary ML API in the spark.ml package. Spark ML basic statistics algorithms based on correlation and hypothesis testing (P-value) are first introduced followed by the Spark ML classification and regression algorithms based on linear models, naive Bayes, and decision tree techniques. Then the characteristics of Spark streaming, streaming input and output, as well as streaming receiver types (which include basic, custom, and advanced) are explained, followed by how the Spark Streaming process and DStream (Discretized Stream) enable big data streaming operations for real-time and near-real-time applications.

...
4 videos (Total 31 min), 2 quizzes
4개의 동영상
4.2 Spark ML Algorithms part 18m
4.2 Spark ML Algorithms part 29m
4.3 Spark Streaming10m
2개 연습문제
Ungraded Quiz
Graded Quiz

강사

Avatar

Jong-Moon Chung

Professor, School of Electrical & Electronic Engineering
Director, Communications & Networking Laboratory

연세 대학교 정보

Yonsei University was established in 1885 and is the oldest private university in Korea. Yonsei’s main campus is situated minutes away from the economic, political, and cultural centers of Seoul’s metropolitan downtown. Yonsei has 3,500 eminent faculty members who are conducting cutting-edge research across all academic disciplines. There are 18 graduate schools, 22 colleges and 133 subsidiary institutions hosting a selective pool of students from around the world. Yonsei is proud of its history and reputation as a leading institution of higher education and research in Asia....

Emerging Technologies: From Smartphones to IoT to Big Data 전문 분야 정보

This Specialization is intended for researchers and business experts seeking state-of-the-art knowledge in advanced science and technology. The 4 courses cover details on Big Data (Hadoop, Spark, Storm), Smartphones, Smart Watches, Android, iOS, CPU/GPU/SoC, Mobile Communications (1G to 5G), Sensors, IoT, Wi-Fi, Bluetooth, LP-WAN, Cloud Computing, AR (Augmented Reality), Skype, YouTube, H.264/MPEG-4 AVC, MPEG-DASH, CDN, and Video Streaming Services. The Specialization includes projects on Big Data using IBM SPSS Statistics, AR applications, Cloud Computing using AWS (Amazon Web Service) EC2 (Elastic Compute Cloud), and Smartphone applications to analyze mobile communication, Wi-Fi, and Bluetooth networks. The course contents are for expert level research, design, development, industrial strategic planning, business, administration, and management....
Emerging Technologies: From Smartphones to IoT to Big Data

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.

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