Learn about the concept and importance of fundamental data science skills. Jungwoo emphasizes the ability to write code, use database management systems, leverage infrastructure software, take advantage of machine learning, and visualize data.
- To be a successful data scientist,…you first need to acquire…a baseline knowledge of statistics.…But since so much of the data industry is driven by IT,…it's also important for data scientists…to be well-versed in the underlying technologies…that support what they do on the job everyday.…These include the ability to write code…in languages such as Python or R…which come with powerful libraries…that implement statistical functions…as well as visualization features.…
Ability to program in general is invaluable.…A data scientist who is also a good programmer…can automate mundane but necessary tasks…and focus on solving larger problems.…Knowing how database management systems work…is another essential skill to master.…Whether structured or unstructured,…much of our data is stored in databases…and familiarity in Structured Query Language or SQL…is highly desired because it allows you…to interact with databases natively.…
Developing a good understanding…of data science infrastructure technologies…is important too.…Distributed file systems like Hadoop…
Author
Jungwoo Ryoo
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
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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: Fundamental skills