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…
- 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
Course organization1m 17s
1. Introduction to Data Science
2. Cloud Computing
3. Distributed File Systems
4. Distributed Processing
5. Machine Learning
6. Case Study
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