40+ hours of video instruction and code-along sessions that teach you the foundational concepts, theory, and techniques you need to know to become an effective data scientist with Python.

This repository contains the exercises and data for Data Science Fundamentals Part 1: Learning Basic Concepts, Data Wrangling, and Databases with Python and Data Science Fundamentals Part 2: Machine Learning and Statistical Analysis. It teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The videos present you with applied, example-driven lessons in Python and its associated ecosystem of libraries, where you get your hands dirty with real datasets and see real results.

The code, slides, and exercises in this repository are (and will always be) freely available. The corresponding videos can be purchased on:

• InformIT: Part 1 & Part 2 (individual purchase)
• O'Reilly Learning: Part 1 & Part 2 (subscription)

If you find any errors in the code or materials, please open a Github issue in this repository or send an email to [email protected]

Beginner

• How to get up and running with a Python data science environment
• The 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 (yet powerful) recommendation engine for Airbnb listings
• Where to find high quality data sources and scrape websites if no existing dataset is available.
• How to work with APIs programmatically, including (but not limited to) the Foursquare API.
• Strategies for parsing JSON and XML into a structured form
• How to build data models and work with database schemas
• The basics of relational databases with SQLite and how to use an ORM to interface with them in Python
• Best practices of data validation, including common data quality checks
• How to query data in a database, including joining data tables and aggregating data
• The fundamentals of exploratory data analysis
• How to find and handle missing or malformed data
• The importance of creating reproducible analyses and how to share them effectively

• Aspiring data scientists looking to break into the field and learn the essentials necessary
• Journalists, consultants, analysts, or anyone else who works with data and looking to take a programmatic approach to exploring data and conducting analyses
• Quantitative researchers interested in a programmatic and systematic approach to working with data and data pipelines.
• Software engineers interested in the fundamentals and best practices of working with data.
• Practicing data scientists already familiar with another programming environment looking to learn how to do data science with Python

• Basic understanding of programming
• Familiarity with Python and relational databases are a plus

Please refer to video 1.4 Getting Set Up with a Data Science Development Environment or at the corresponding slides.

• Inside Airbnb
• Foursquare API