Data Science Fundamentals: Basic Concepts, Data Wrangling, and Databases with Python

Colleagues, this series focuses on the fundamentals of acquiring, parsing, validating, and wrangling data with Python and its associated ecosystem of libraries. After an introduction to Data Science as a field and a primer on the Python programming language, you walk through the data science process by building a simple recommendation system. After this introduction, you dive deeper into each of the specific steps involved in the first half of the data science process–mainly how to acquire, transform, and store data (often referred to as an ETL pipeline). You learn how to download and parse this XML and JSON data. With this structured data, you learn how to build data models, store and query data, and work with relational databases. Skill-based lessons include: 1) Introduction to Data Science with Python, 2) The Data Science Process–Building Your First Application (an AirBnB listing recommender), 3) Acquiring Data–Sources and Methods, 4) Adding Structure–Parsing Data and Data Models (including Foursquare dataset), 5) Storing Data–Persistence with Relational Databases, and 6) Validating Data–Provenance and Quality Control.

Enroll today (individuals & teams are welcome): tinyurl.com/3lblk6ef

Much career success, Lawrence E. Wilson - Online Learning Central