Data Science Practitioners

Introduction to Domino

Course summary: Get a high-level overview of some of the main features in Domino and how they can be applied to solve common problems for data scientists, data science leaders, and the IT teams that support their efforts.

Duration: 1 hour

What you’ll learn:

• Using Domino to speed up the development of your model by accessing the newest tools and more compute power
• How to develop a model in Domino and create a schedule report
• Using Domino for collaboration and sharing work
• How to easily monitor progress and track usage and cost

Domino for Practitioners

Course summary: Learn about the full data science lifecycle within Domino. Learn how to connect to data sources and integrate with git, work with large data, and develop your model in an interactive environment with easy access to the tools you prefer. Then deploy the model using a model API, scheduled reports, and an interactive dashboard. Lastly, learn how to work with Domino programmatically.

Duration: Half-day

What you’ll learn:

• Collaboration, reproducibility, and tracking work in Domino
• How to create interactive dashboards, model APIs, and self-service web forms in Domino
• Using Domino datasets to work with large data
• Git integration and connecting to data sources
• Setting up environments to get access to the newest tools
• Using Domino programmatically through the API and command line interface

Managing Large Data with Domino Datasets

Course summary: Learn how to use Domino’s high-performance revisioned data store to work with large or numerous files.

Duration: 1 hour

What you’ll learn:

• How to share and access large versioned file-based data (1 TB+) during interactive runs or batch jobs
• Work with temporary “scratch spaces” for exploratory and iterative work on big data sets
• Automate a data pipeline using the Domino API to control datasets

Getting Started with Data Science

Course summary: Learn how to move from Excel analysis to Python programming while using Domino to collaborate, simplify access to data science tools, and speed up experiments. You’ll also learn how to get started with some of the first steps in data science and machine learning.

Duration: Full day

What you’ll learn:

• Move from Excel analysis to Python programming
• Work through each stage of the data science lifecycle
• Learn about the importance of data cleansing and preparation
• Build a machine learning model using real-world data

Introduction to Machine Learning

Course summary: Learn about each step in the data science workflow while using Python to clean real-world data and develop a machine learning model. We’ll cover the basics of both supervised and unsupervised models, how to determine the efficacy of your analysis, and how to improve your models. Throughout this course, we’ll see how Domino can speed up experiments and help you share and collaborate with your colleagues.

Duration: Full day

What you’ll learn:

• The basics of supervised and unsupervised machine learning models
• Various metrics for evaluating model performance
• How to improve a model through hyperparameter tuning, feature engineering and cross-validation
• Deploying your model for use in an interactive dashboard

Best Practices for Collaboration in Domino

Course summary: Learn about the Domino features that enable seamless collaboration and how to integrate these into your existing workflow.

Duration: 1 hour

What you’ll learn:

• Sharing projects
• Integrating with git
• Fork and merge within projects
• Using organizations to manage resources and access
• Tracking work with comments, project tags and goals