Every time you use Google to search something, every time you use Facebook, Twitter, Instagram or any other SNS (Social Network Service), and every time you buy from a recommended list of products on Amazon.com you are using a big data system. In addition, big data technology supports your smartphone, smartwatch, Alexa, Siri, and automobile (if it is a newer model) every day. The top companies in the world are currently using big data technology, and every company is in need of advanced big data technology support. Simply put, big data technology is not an option for your company, it is a necessity for survival and growth. So now is the right time to learn what big data is and how to use it in advantage of your company. This 6 module course first focuses on the world’s industry market share rankings of big data hardware, software, and professional services, and then covers the world’s top big data product line and service types of the major big data companies. Then the lectures focused on how big data analysis is possible based on the world’s most popular three big data technologies Hadoop, Spark, and Storm. The last part focuses on providing experience on one of the most famous and widely used big data statistical analysis systems in the world, the IBM SPSS Statistics. This course was designed to prepare you to be more successful in businesses strategic planning in the upcoming big data era. Welcome to the amazing Big Data world!
Ce cours fait partie de la Spécialisation Technologies émergentes : des smartphones à l'Internet des objets et aux Big Data
Big Data Emerging Technologies
proposé par
Spécialisation Technologies émergentes : des smartphones à l'Internet des objets et aux Big DataUniversité Yonsei
2,204 inscrit(s)
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
11 heure pour terminer
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 vidéos (Total 28 min), 2 quiz
6 vidéos
Introduction6 min
1.1 Big Data Market Analysis1 min
1.2 IBM / 1.3 SAP8 min
1.4 Oracle / 1.5 Splunk / 1.6 Accenture / 1.7 Dell / 1.8 Teradata6 min
1.9 Microsoft / 1.10 Cisco / 1.11 AWS3 min
1.12 Big Data Landscape1 min
2 exercices pour s'entraîner
Ungraded Quiz8 min
Graded Quiz
Semaine
21 heure pour terminer
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 vidéos (Total 68 min), 2 quiz
8 vidéos
2.3 Big Data's 4 Vs / 2.4 How is Big Data being Used?8 min
2.5 HADOOP11 min
2.6 MapReduce vs. RDBMS6 min
2.7 MapReduce9 min
2.8 Hadoop vs. SQL(RDBMS & RDSMS)12 min
2.9 HDFS Enhancements4 min
2.10 Hadoop vs. Hadoop YARN6 min
2 exercices pour s'entraîner
Ungraded Quiz12 min
Graded Quiz
Semaine
32 heures pour terminer
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 vidéos (Total 101 min), 2 quiz
11 vidéos
3.1 Apache Spark5 min
3.2 Spark Architecture / 3.3 Spark Family9 min
3.4 Spark vs. Hadoop11 min
3.5 Spark RDD6 min
3.6 Spark Transformations / 3.7 Spark Actions / 3.8 Spark DAG12 min
3.9 Spark Programming7 min
3.10 Spark Core / 3.11 Spark Variables & Serialization7 min
3.12 Spark Cluster Operations / 3.13 Spark Standalone / 3.14 Spark Mesos14 min
3.15 Spark YARN9 min
3.16 Spark SQL / 3.17 Spark GraphX5 min
3.18 Relational DB & Graph DB12 min
2 exercices pour s'entraîner
Ungraded Quiz
Graded Quiz
Semaine
41 heure pour terminer
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 vidéos (Total 31 min), 2 quiz
4 vidéos
4.2 Spark ML Algorithms part 18 min
4.2 Spark ML Algorithms part 29 min
4.3 Spark Streaming10 min
2 exercices pour s'entraîner
Ungraded Quiz
Graded Quiz
Semaine
51 heure pour terminer
Storm
The fifth module “Storm” focuses on the characteristics and operations of Storm big data systems. The lecture first covers the differences in data analysis characteristics of Storm, Spark, and Hadoop technology. Then the features of Storm big data processing based on the nimbus, spouts, and bolts are described followed by the Storm streams, supervisor, and ZooKeeper details. Further details on Storm reliable and unreliable spouts and bolts are provided followed by the advantages of Storm DAG (Directed Acyclic Graph) and data stream queue management. In addition, the advantages of using Storm based fast real-time applications, which include real-time analytics, online ML (Machine Learning), continuous computation, DRPC (Distributed Remote Procedure Call), and ETL (Extract, Transform, Load) are introduced.
5 vidéos (Total 36 min), 2 quiz
5 vidéos
5.1 Apache Storm7 min
5.2 Storm Applications14 min
5.3 Storm Spouts & Streams6 min
5.4 Storm Topology & Management5 min
5.5 Storm Trident2 min
2 exercices pour s'entraîner
Ungraded Quiz
Graded Quiz
Semaine
624 heures pour terminer
IBM SPSS Statistics Project
The sixth and last module “IBM SPSS Statistics Project” focuses on providing experience on one of the most famous and widely used big data statistical analysis systems in the world. First, the lecture starts with how to setup and use IBM SPSS Statistics, and continues on to describe how IBM SPSS Statistics can be used to gain corporate data analysis experience. Then the data processing statistical results of two projects based on using the IBM SPSS Statistics big data system is conducted. The projects are conducted so the student can discover new ways to use, analyze, and draw charts of the relationship between datasets, and also compare the statistical results using IBM SPSS Statistics.
1 vidéo (Total 9 min), 1 quiz
1 vidéo
Enseignant
Jong-Moon Chung
Professor, School of Electrical & Electronic EngineeringDirector, Communications & Networking Laboratory
À propos de Université Yonsei
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.
À propos de la Spécialisation Technologies émergentes : des smartphones à l'Internet des objets et aux Big Data
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.
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