Learn about the concept of data science literacy and fluency. Jungwoo explains why it's important to go beyond data science literacy and ultimately reach fluency.
- Our everyday production of data is increasing at an exponential rate. What's more is that we accelerate this trend by connecting more and more things around us to the internet. We even have a name for this phenomenon called the Internet of Things, or IoT. This explosion of data is also forcing us to be a smart data consumer on individual and professional levels.
Because of this practical need for data in every aspect of our lives, it is critical for us to develop more data science literacy. This is even more true when it comes to working professionals, especially those working in information technology. In fact, you should go beyond basic data literacy to be truly comparative. Data fluency should be your goal. Achieving data fluency implies that you are capable of not only understanding the diverse interpretations of data but also creating your own for others, by leveraging the various data science tools of the trade.
There are many ways to begin your journey into developing data science fluency, but the path that could be most appealing to IT professionals is the technology route. Especially the enabling IT technologies that allow data scientists to do their jobs more effectively are probably what we can relate to very easily. I want to help you jumpstart with these data science technologies, by giving you succinct exposure to all the essential data science tools of the trade.
- 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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