Data Science Fundamentals

Colleagues, the Data Science Fundamentals Part 1 - Learning Basic Concepts, Data Wrangling, and Databases with Python program 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 data that is openly accessible on the Internet by working with APIs and websites, and how to 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. Along the way, you learn the fundamentals of programming with Python (including object-oriented programming and the standard library) as well as the best practices of building sustainable data science applications. Skill-based training modules address: 1: Introduction to Data Science with Python, 2: The Data Science Process–Building Your First Application, 3: Acquiring Data–Sources and Methods, 4: Adding Structure–Parsing Data and Data Models, 5: Storing Data–Persistence with Relational Databases, and 6: Validating Data–Provenance and Quality Control. 

Enroll today (teams & execs welcome): https://tinyurl.com/bd6r76y7 

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Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share & subscribe)