Data Science Fundamentals

The Data Science Fundamentals content teaches the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The materials is presented with applied, example-driven lessons in Python and its associated ecosystem of libraries, where participants get their hands dirty with real datasets and see real results.

At the conclusion of the course, participants will have analyzed a number of datasets, built a handful of applications, and applied machine learning algorithms in meaningful ways to get real results. Along the way, participants learn the best practices and computational techniques used by professional data scientists. More specifically, participants learn how to acquire data that is openly accessible on the Internet by working with APIs and parse XML and JSON data to load it into a relational database.

At the conclusion of this content, participants will be able to:

• How to get up and running with a Python data science environment
• T he essentials of Python 3, including object-oriented programming
• The basics of the data science process and what each step entails
• How to build a simple recommendation engine for website listings
• Where to find quality data sources and how to work with APIs programmatically
• Strategies for parsing JSON and XML into a structured form
• The basics of relational databases and how to use an ORM to interface with them in Python
• Be st practices of data validation, including common data quality checks