Data Community DC and District Data Labs are hosting another session of their Building Data Apps with Python workshop on Saturday February 6th from 9am - 5pm. If you're interested in learning about the data science pipeline and how to build and end-to-end data product using Python, you won't want to miss it. Register before January 23rd for an early bird discount!
Data products are usually software applications that derive their value from data by leveraging the data science pipeline and generate data through their operation. They aren’t apps with data, nor are they one time analyses that produce insights - they are operational and interactive. The rise of these types of applications has directly contributed to the rise of the data scientist and the idea that data scientists are professionals “who are better at statistics than any software engineer and better at software engineering than any statistician.”
These applications have been largely built with Python. Python is flexible enough to develop extremely quickly on many different types of servers and has a rich tradition in web applications. Python contributes to every stage of the data science pipeline including real time ingestion and the production of APIs, and it is powerful enough to perform machine learning computations. In this class we’ll produce a data product with Python, leveraging every stage of the data science pipeline to produce a book recommender.
WHAT YOU WILL LEARN
Python is one of the most popular programming languages for data analysis. Because of this, it is important to have a basic working knowledge of the language in order to access more complex topics in data science and natural language processing. The purpose of this one-day course is to introduce the development process in Python using a project-based, hands-on approach. In particular you will learn how to structure a data product using every stage of the data science pipeline including ingesting data from the web, wrangling data into a structured database, computing a non-negative matrix factorization with Python, and then producing a web based report.
The workshop will cover the following topics:
- Basic project structure of a Python application
- virtualenv & virtualenvwrapper
- Managing requirements outside the stdlib
- Creating a testing framework with nose
- Ingesting data with requests.py
- Wrangling data into SQLite Databases using SQLAlchemy
- Building a recommender system with Python
- Computing a matrix factorization with Numpy
- Storing computational models using pickles
- Reporting data with JSON
- Data visualization with Jinja2
After this course you should understand how to build a data product using Python and will have built a recommender system that implements the entire data science pipeline.
For more info and registration, see the DDL course page.