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Data science: overview
1. Data science: overview
Data science consists of a useful combination of a number of areas. Let us look at each in turn.
Questions raised here would be addressed (if not answered) during one or more courses on data science.
2. Coding
What is discrete vs. continuous?
What it data? What is information?
What is computer science?
3. Visualization
Explain the difference between data visualization and information visualization.
4. Business
What is a business?
5. Machine learning
Machine learning is sometimes called artificial intelligence.
6. Information technology
7. Domain knowledge
8. Specialized areas
Specialized areas:
Concurrent and parallel algorithms and programming
No-SQL databases
Security, privacy, and intellectual property
9. Simplified view
In a simplified view, data science consists the following.
code = coding (e.g., Python)
stats = statistics
domain = domain knowledge (and business)
ml = machine learning
tr = traditional research
?! = danger zone
10. Other terms
Here are some other terms that are sometimes used for data science or for important parts of data science.
data mining
big data
business analytics (people oriented)
business intelligence
decision science
data engineering, knowledge engineering
11. Data
The goal is for someone (e.g., manager, decision-maker) or something (e.g., computer) to make a decision.
The emphasis is on data, not just coding.
12. Goal
Remember that the goal of data science is for someone (e.g., manager) or some machine (e.g., computer) to make a decision.
13. End of page
