This workshop covers how to gain access to data available through a RESTful API through Python. Workshop participants will learn how to traverse a tree structure and convert it to other data structures, such as a tabular dataframe, prior to analysis.
In this workshop, participants will use MongoDB and the CORD-19 dataset from the Allen institute to: 1) Query a Sample Mongo DB, using filters and aggregations. 2) Use PyMongo to populate a remote (Atlas) MongoDB with JSON documents downloaded from the CORD-19 research data set. 3) Query the CORD-19 research data set, create a text search index, and parse results as JSON documents and Pandas dataframes.
This workshop will provide a review of basic machine learning concepts, and will introduce Python and scikit-learn tools for machine learning. Empahsis will be on the programming work involved in preparing data, populating and building a ML model using scikit-learn libraries, and reading results.
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications.
Explore the world of programming languages through Python and learn the building blocks of writing programs. This book covers Python 3.10, explaining it through six key concepts. Each chapter contains a real-world example with practical advice and a section on advanced concepts.