Course Overview
This three-day, role-specific course is intended for participants interested in developing skills and experience using Snowflake AI Data Cloud for data science workloads. The participant will gain exposure to the rich features of Snowflake, diverse machine learning datasets, relevant and popular open source ML frameworks and libraries, and model deployment practices that will provide practical skills applicable to data science jobs. This course consists of lectures, demos, labs, and discussions.
What are the skills covered
- Collect and access data from Snowflake Data Marketplace and other sources.
- Manage and architect data lakes and real-time streams.
- Employ Snowflake-recommended best practices for developing or querying semi-structured and other data types.
- Work with supervised and unsupervised machine learning models using some of the most relevant open source frameworks and libraries.
- Formulate data science and machine learning workflows and data pipelines.
- Manage and deploy machine learning models at scale with APIs.
- Visualize and collaborate on machine learning results.
Who should attend this course
- Data scientists who build and train machine learning models.
- Data scientists and data analysts who use machine learning models to conduct predictive and prescriptive analytics.
Course Curriculum
What are the Prerequisites
- Recommended completion of the “Snowflake Multi-Factor Authentication (MFA) Essentials” free on-demand course.
- Completion of “Snowflake Foundations” one-day course or equivalent Snowflake knowledge.
- Basic knowledge of SQL is required.
- Foundational knowledge of databases.
- Python or some other object-oriented programming language.
- A background in data science, machine learning, or statistical modeling is required.
Course Modules
Exam & Certification
SnowPro® Advanced: Data Scientist Certification DSA-C03
SNOWPRO ADVANCED: DATA SCIENTIST OVERVIEW
This certification will test the ability to:
- •Outline data science concepts
- •Implement Snowflake data science best practices
- •Prepare data and use feature engineering in Snowflake
- •Train and use machine learning models
- •Use GenAI and LLM capabilities in Snowflake
