Learn about the concept and importance of data science tools of the trade. Jungwoo identifies the tools as: cloud computing, virtualization, distributed file systems, distributed processing, and machine learning.
- Data science requires support…from advanced tools and technology.…The most fundamental among the two…are cloud computing and virtualization.…Because of the ever increasing size,…speed and accuracy requirements…for the data sets we have to manage,…it's no longer possible to store and process them…on an isolated computer with fixed amount of capacity.…
Cloud computing provides a flexible solution…to this challenge.…It can easily handle the dynamic scalability requirement…for computing resources.…Without touching the physical hardware,…a Cloud Service Provider or CSP…can easily add more CPUs, memory and storage to a project…as its demand grows.…This is referred to as elasticity.…
However, cloud infrastructure alone…cannot solve all problems.…You need many more software tools on top of it…because the cloud primarily provides…processing power and storage space.…What makes the cloud even more relevant to data science…is the software applications that connect virtual machines…through a high speed network…and implement distributed file systems…
- 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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