Data science consists of a useful combination of a number of areas. Let us look at each in turn.

• What is discrete vs. continuous?
• What it data? What is information?
• What is computer science?

What is the difference between data visualization and information visualization.

What is a business?

Machine learning is sometimes called artificial intelligence.

Specialized areas:

• Concurrent and parallel algorithms and programming
• No-SQL databases
• Security, privacy, and intellectual property

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

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

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.

The topics of data science will be interleaved in the course, but the main areas are as follows.

• code: Python
• numerical computation (Python, NumPy, SciPy, etc.)
• data: Python (Python, Python libraries, including Pandas, etc.)
• visualization (matplotlib)
• machine learning (various libraries and packages)