Learn about the concept and importance of enabling technologies in data science, which implement the data science tools of the trade as software solutions. Jungwoo provides concrete examples such as Proxmox, Hadoop, Spark, and Weka.
- Cloud computing, virtualization,…distributed computing and machine learning…are all enabling technologies that allow data scientists…to do their job effectively and efficiently.…These tools are indispensable and interconnected.…Distributed computing builds on cloud computing…and virtualization.…Machine learning in turn relies on distributed computing.…
I'm excited to report that I've found…excellent examples you can experiment with…for all of these enabling technologies for data science.…Proxmox is my choice for cloud computing and virtualization.…It is relatively easy to install and you can get a good…sense of what it takes to build your own cloud…by installing and configuring the software.…
Hardoop or Spark or open source platforms…for distributed computing.…Hardoop is more comprehensive in its features,…and Spark can be plugged into Hardoop…to enhance its distributed processing feature.…Weka is a powerful machine learning tool…that allows its users to run various machine learning…algorithms with ample options…
Author
Released
8/30/2018- Enabling technologies in data science
- Cloud computing and virtualization
- Installing and working with Proxmox, Hadoop, Spark, and Weka
- Managing virtual machines on Proxmox
- Distributed processing with Spark
- Fundamental applications of machine learning
- Distributed systems and distributed processing
- How Hadoop, Spark, and Weka can work together
Skill Level Beginner
Duration
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Introduction
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Course organization1m 17s
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1. Introduction to Data Science
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Introduction1m 51s
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Data science2m 53s
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Fundamental skills3m 42s
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Enabling technologies2m 4s
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2. Cloud Computing
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Cloud fundamentals3m 29s
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Types of cloud3m 19s
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Solution providers2m 22s
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Proxmox: Installation2m 26s
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3. Distributed File Systems
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Distributed file systems2m 44s
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Fundamentals2m 45s
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Hadoop hands-on2m 8s
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Hadoop: Preparation4m 11s
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Hadoop: Installation4m 18s
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Hadoop: MapReduce hands-on8m 52s
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4. Distributed Processing
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Spark: Installation6m 24s
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Spark: Spark shell4m 28s
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Spark: pyspark4m 32s
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Spark: Application4m 1s
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5. Machine Learning
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Machine learning2m 41s
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Fundamentals2m 16s
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Types of machine learning2m 59s
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Weka: Installation2m 33s
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Weka: GUI3m 35s
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Weka: Training vs. testing3m 21s
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Weka: Clustering2m 12s
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6. Case Study
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Putting it all together2m 42s
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Hadoop cluster: Operation4m 57s
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Spark, YARN, and Hadoop6m 42s
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Weka and Spark3m 12s
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Conclusion
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Next steps41s
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Video: Enabling technologies