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…
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
Views
Related Courses
-
Introduction
-
Course organization1m 17s
-
1. Introduction to Data Science
-
Introduction1m 51s
-
Data science2m 53s
-
Fundamental skills3m 42s
-
Enabling technologies2m 4s
-
-
2. Cloud Computing
-
Cloud fundamentals3m 29s
-
Types of cloud3m 19s
-
Solution providers2m 22s
-
Proxmox: Installation2m 26s
-
3. Distributed File Systems
-
Distributed file systems2m 44s
-
Fundamentals2m 45s
-
Hadoop hands-on2m 8s
-
Hadoop: Preparation4m 11s
-
Hadoop: Installation4m 18s
-
Hadoop: MapReduce hands-on8m 52s
-
-
4. Distributed Processing
-
Spark: Installation6m 24s
-
Spark: Spark shell4m 28s
-
Spark: pyspark4m 32s
-
Spark: Application4m 1s
-
5. Machine Learning
-
Machine learning2m 41s
-
Fundamentals2m 16s
-
Types of machine learning2m 59s
-
Weka: Installation2m 33s
-
Weka: GUI3m 35s
-
Weka: Training vs. testing3m 21s
-
Weka: Clustering2m 12s
-
-
6. Case Study
-
Putting it all together2m 42s
-
Hadoop cluster: Operation4m 57s
-
Spark, YARN, and Hadoop6m 42s
-
Weka and Spark3m 12s
-
-
Conclusion
-
Next steps41s
-
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.
CancelTake notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.
Share this video
Embed this video
Video: Tools of trade