Snowflake Advanced Analytics Course Overview

Prerequisites for Snowflake Advanced Analytics Course

To ensure a successful learning experience in the Snowflake Advanced Analytics course, participants should ideally have the following minimum prerequisites:

• Basic knowledge of Snowflake
• Proficiency in SQL
• Experience in mathematical analysis (helpful but not mandatory)
• Familiarity with Python programming (useful but not mandatory)

These prerequisites will help you maximize your learning and effectively leverage the advanced analytical features of the Snowflake Data Cloud. If you have any questions or need further clarification, please do not hesitate to reach out to our support team.

Target Audience for Snowflake Advanced Analytics

Introduction:
The Snowflake Advanced Analytics course equips experienced professionals with advanced data analysis, machine learning, and visualization skills tailored for the Snowflake Data Cloud.

Target Audience and Job Roles:

• Data Analysts
• Data Scientists
• Business Intelligence Analysts
• Database Administrators
• IT Managers
• Data Engineers
• Machine Learning Engineers
• Software Developers
• Business Consultants
• Analytics Managers
• Big Data Specialists
• Statisticians
• Quantitative Analysts
• Python Programmers with data focus
• SQL Developers
• Project Managers in Data-related Projects
• Cloud Solution Architects
• Data Architects
• Artificial Intelligence Specialists
• Financial Analysts using data for forecasting and trend analysis

Learning Objectives - What you will Learn in this Snowflake Advanced Analytics?

Introduction: The Snowflake Advanced Analytics course provides a comprehensive deep dive into the analytical capabilities of Snowflake Data Cloud, enabling participants to extract deeper insights, identify patterns, and forecast potential outcomes using advanced data analytics techniques.

Learning Objectives and Outcomes:

• Utilize Snowsight Analytics:

  • Load, visualize, and run complex queries on data within Snowflake.

• Perform Exploration and Analysis:

  • Apply set operators and joins.
  • Use advanced features like Time Travel.
  • Conduct linear regression analysis.

• Implement Windowing Functions:

  • Master the over clause.
  • Utilize rank, dense rank, row number, lead, and lag functions.

• Analyze Semi-structured Data:

  • Flatten and unnest semi-structured data.
  • Extract, check, and manage data types.
  • Differentiate between structured and semi-structured tables.

• Conduct Geospatial Analysis:

  • Understand geospatial concepts including geography and geometry.

• Process and Analyze Big Data:

  • Compare large datasets.
  • Analyze data using top-k frequency, distinct counting, percentile distributions, and statistical tests.

• Leverage Streamlit:

  • Integrate and