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.

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.

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.

Target Audience

  • 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

Module 1: Overview of Data Science with Snowflake

  • Introduction to Data Science Workload
  • Connecting to Snowflake

Module 2: Snowflake Data Storage

  • Supported Object Types
  • Supported Data Types
  • SQL Support
  • The Variant Data Type
  • Introduction to Unstructured Data

Module 3: Acquire Data

  • Accessing External Data
  • Loading Data into Snowflake
  • Accessing Snowflake Data Worldwide with the Data Cloud
  • Snowflake ML Functions
  • Cortex LLM

Module 4: Prepare Data

  • What is Snowpark?
  • Sampling Data
  • Tidying Tables
  • Transforming Data with Snowpark
  • Leveraging Unstructured Data
  • Table Streams and Tasks

Module 5: Perform EDA (Exploratory Data Analysis)

  • Tools for EDA
  • Univariate Regression in Snowflake
  • Estimation Functions

Module 6: Perform Feature Engineering

  • Feature Engineering
  • Pandas on Snowflake
  • Feature Engineering with Snowpark

Module 7: Train Models

  • Overview of Machine Learning
  • Snowpark ML
  • Snowflake Model Registry
  • Training Models with Snowpark Stored Procedures
  • Auto ML

Module 8: Deploy Models

  • Batch Scoring
  • Python Worksheets
  • UDFs
  • Stored Procedures
  • Snowpark UDFs for Model Inference
  • External Functions

Module 9: Beyond Deployment: ML Ops

  • Improving Runtime Performance
  • Vectorized UDFs
  • Monitoring
  • MLOps

Dates & Locations

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Trainocate exam and cert

Exam & Certification

SnowPro® Advanced: Data Scientist Certification DSA-C03

The SnowPro® Advanced: Data Scientist Certification will validate advanced knowledge and skills used to apply comprehensive data science principles, tools, and methodologies using Snowflake.

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

Training & Certification Guide

  • xam Version: DSA-C03
  • Total Number of Questions: 65
  • Question Types: Multiple Select, Multiple Choice, Interactive
  • Time Limit: 115 minutes
  • Language: English
  • Registration Fee: $375 USD
  • India Registration Fee: $300 USD
  • Passing Score: 750+ scaled score out of 1000
  • Unscored Content: Exams may include unscored items for statistical purposes. These questions are not identified and do not affect your score. Additional time is included to account for these items.
  • Prerequisites: SnowPro Core Certified

  • 1.0 Data Science Concepts – 17%
  • 2.0 Data Preparation and Feature Engineering – 27%
  • 3.0 Model Development – 31%
  • 4.0 Model Deployment – 25%

Frequently Asked Questions

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