Workshops at Data Day Texas 2023

We still have discount rooms at the AT&T. If you are coming from out of town, this is where all the action is. For the best selection, Book a room now.

The mini-workshops are 90 minutes - the same length as two regular Data Day sessions. These workshops run throughout the day, and are held in tiered classrooms with space to open and plug-in your laptop. The goal of each workshop is to set you up with a new tool/skill and enough knowledge to continue on your own.
We're just beginning to announce the workshops for Data Day Texas 2023. We'll be adding more each week.

Hands-on Introduction to Web Scraping with Python 2023

Ryan Mitchell - (GLG)

Ryan Mitchell, author of Web Scraping with Python (3rd edition in progress), has brought her 4 hour web scraping workshop multiple times to the Data Day Texas. It's always been the day before Data Day and required separate registration. For 2023, Ryan will be returning to Data Day, this time offering a 90 minute version of her workshop - included as part of Data Day - with no additional registration fee. Best practices for web scraping have changed considerably in the last few years. Ryan will be covering the latest tools, tips and tricks. Ryan is pretty much the #1 goto trainer for webs scraping - and someone for whom we get frequent requests to bring back.
Workshop requirements and goals will be published in the next week.

Introduction to Graph Data Science for Python Developers

Sean Robinson - (Graphable)

This workshop will cover a variety of graph data science techniques using Python, Neo4j, and other libraries. The goal of the workshop is to serve as a springboard for attendees to identify which graph-based tools/techniques can provide novel value to existing workflows. Some of the techniques to be covered are:
• How to think about data as a graph and the implications that has on downstream analysis
• How to use graph algorithms at scale using both Neo4j and other pythonic libraries
• How to enhance traditional ML models with graph embeddings
• How to visualize these insights in the context of a graph for greater business intelligence
• How to integrate these techniques with your existing data science tool belt

Hands-On Introduction To GraphQL For Data Scientists & Developers

William Lyon - (Neo4j)

This hands-on workshop will introduce GraphQL and explore how to build GraphQL APIs backed by Neo4j, a native graph database, and show why GraphQL is relevant for both developers and data scientists. This workshop will show how to use the Neo4j GraphQL Library, which allows developers to quickly design and implement fully functional GraphQL APIs without writing boilerplate code, to build a Node.js GraphQL API, including adding custom logic, authorization rules, and operationalizing data science techniques.

Outline
- Overview of GraphQL and building GraphQL APIs
- Building Node.js GraphQL APIs backed by a native graph database using the Neo4j GraphQL Library
- Adding custom logic to our GraphQL API using the @cypher schema directive and custom resolvers
- Adding authentication and authorization rules to our GraphQL API

Prerequisites
We will be using online hosted environments so no local development setup is required. Specifically, we will use Neo4j Aura database-as-a-service and CodeSandbox for running our GraphQL API application. Prior to the workshop please register for Neo4j Aura and create a "Free Tier" database: dev.neo4j.com/neo4j-aura. You will also need a GitHub account to sign-in to CodeSandbox or create a CodeSandbox account at codesandbox.io.

Ontology for Data Scientists - 90 minute tutorial

Michael Uschold - (Semantic Arts)

We start with an interactive discussion to identify what are the main things that data scientists do and why and what some key challenges are. We give a brief overview of ontology and semantic technology with the goal of identifying how and where it may be useful for data scientists.
The main part of the tutorial is to give a deeper understanding of what an ontologies are and how they are used. This technology grew out of core AI research in the 70s and 80s. It was formalized and standardized in the 00s by the W3C under the rubric of the Semantic Web. We introduce the following foundational concepts for building an ontology in OWL, the W3C standard language for representing ontologies.
- Individual things are OWL individuals - e.g., JaneDoe
- Kinds of things are OWL classes - e.g., Organization
- Kinds of relationships are OWL properties - e.g., worksFor
Through interactive sessions, participants will identify what the key things are in everyday subjects and how they are related to each other. We will start to build an ontology in healthcare, using this as a driver to introduce key OWL constructs that we use to describe the meaning of data. Key topics and learning points will be:
- An ontology is a model of subject matter that you care about, represented as triples.
- Populating the ontology as triples using TARQL, R2RML and SHACL
- The ontology is used as a schema that gives data meaning.
- Building a semantic application using SPARQL.
We close the loop by again considering how ontology and semantic technology can help data scientists, and what next steps they may wish to take to learn more.

Introduction to Taxonomies for Data Scientists - 90 minute tutorial

Heather Hedden - (Semantic Web Company)

This tutorial/workshop teaches the fundamentals and best practices for creating quality taxonomies, whether for the enterprise or for specific knowledge bases in any industry. Emphasis is on serving users rather than on theory. Topics to be covered include: the appropriateness of different kinds of knowledge organization systems (taxonomies, thesauri, ontologies, etc.), standards, taxonomy concept creation and labeling, taxonomy relationship creation. The connection of taxonomies to ontologies and knowledge graphs will also be briefly discussed. There will be some interactive activities and hands-on exercises. This session will cover:
Introduction to taxonomies and their relevance to data
• Comparisons of taxonomies and knowledge organization system types
• Standards for taxonomies and knowledge organization systems
• Taxonomy concept creation
• Preferred and alternative label creation
• Taxonomy relationship creation
• Taxonomy relationships to ontologies and knowledge graphs
• Best practices and taxonomy management software use