Snowflake for Data Science: Intro to Snowpark ML for Python from Coursera

Picture this: a data-driven revolution where Snowflake's data cloud and Python converge to unveil new dimensions in data science. Get ready for a transformative learning experience with our Snowpark ML for Python guided-project!

This Guided Project was crafted to help data scientists leverage Snowpark ML for Python. By the end of this project, you will be equipped to seamlessly construct an end-to-end machine learning workflow – commencing from data preprocessing, advancing through model training, and culminating in the realms of inference and deployment.

Embark on this 2-hour journey where you will learn how to:

Load data into Snowflake and setup Python workspace.

Transform data using Snowpark ML API and build a preprocessing pipeline.

Train an XGBoost model using the Snowpark ML Model API and deploy the model as a UDF for inference.

To achieve the project's objectives, you will create a machine learning model (XGBoost) using Snowpark in Snowflake. The project scenario involves stepping into the role of a data scientist for a renowned Diamond retailer. The goal is to address challenges faced by salespeople in accurately estimating diamond prices. Leveraging a dataset with attributes like Carat, Weight, and Cut Quality stored in Snowflake, you will build a predictive model to recommend optimal diamond purchase prices.

This project uniquely integrates Snowflake's robust data capabilities with the flexibility of Python for machine learning. Success requires an intermediate level of Python and prior experience with Snowflake. Don't miss the chance to enhance your data science skills with Snowpark ML and Python!

What's inside

Syllabus

Project Overview

This Guided Project was crafted to help data scientists and business analysts accomplish the mastery of leveraging Snowpark ML for Python. By the end of this project, you will be equipped to seamlessly construct an end-to-end machine learning workflow – commencing from data preprocessing, advancing through model training, and culminating in the realms of inference and deployment.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops a core data science skillset, including data preprocessing, machine learning model training, inference, and deployment

Takes a project-based approach, providing learners hands-on experience in Snowpark ML for Python

Focuses on a practical application, helping learners build a predictive model for diamond price estimation

Requires intermediate Python skills and prior experience with Snowflake

Assumes learners have background knowledge in machine learning and data science concepts

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Snowflake for Data Science: Intro to Snowpark ML for Python with these activities:

Explore Snowpark ML documentation and resources

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Enhance your understanding of Snowpark ML by delving into official documentation and exploring additional resources. This will provide a solid foundation for your learning and support your progress throughout the course.

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Review the Snowpark ML documentation
Access Snowpark ML tutorials and code samples
Join the Snowpark ML community forum
Explore blog posts and articles on Snowpark ML

Practice data transformations using Snowpark ML API

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Reinforce your understanding of the Snowpark ML API and hone your data transformation skills to effectively process and prepare data for machine learning.

Browse courses on Data Transformation

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Create a sample dataset and load it into Snowflake
Explore the Snowpark ML API for data transformations
Implement transformations such as filtering, sorting, and aggregation
Build a data preprocessing pipeline using Snowpark ML API
Evaluate the transformed dataset and ensure data integrity

Deploy XGBoost model as a UDF for inference

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Put your machine learning skills into practice by deploying your XGBoost model as a UDF. This will enable you to apply your model to real-world data for accurate and efficient predictions.

Browse courses on XGBoost

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Train an XGBoost model using Snowpark ML Model API
Create a UDF in Python to wrap the XGBoost model
Register the UDF in Snowflake
Invoke the UDF on a new dataset to make predictions
Evaluate the performance of the deployed model

One other activity

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Summarize and present your XGBoost model analysis

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Consolidate and showcase your understanding of the XGBoost model. By summarizing and presenting the results of your analysis, you will not only reinforce your knowledge but also develop valuable communication skills essential for data scientists.

Browse courses on XGBoost

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Analyze the performance of the XGBoost model
Identify key insights and conclusions from the analysis
Create a presentation to convey your findings
Present your analysis and respond to questions

Career center

Learners who complete Snowflake for Data Science: Intro to Snowpark ML for Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for developing and implementing machine learning models to solve business problems. This course, which focuses on using Snowpark ML for Python to build and deploy machine learning models, would be highly relevant to a Data Scientist as it provides hands-on experience with the tools and techniques used in the field. The hands-on projects in this course, such as building an XGBoost model to predict diamond prices, provide practical experience that can be directly applied to real-world data science projects.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for the design, development, and deployment of machine learning models. This course, with its focus on using Snowpark ML for Python, provides a solid foundation for Machine Learning Engineers as it covers the full machine learning workflow, from data preprocessing to model deployment. The course content directly aligns with the responsibilities of a Machine Learning Engineer, as it provides hands-on experience in building and deploying machine learning models using industry-standard tools such as XGBoost and Python.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course, which provides an overview of Snowpark ML for Python, can be useful for Data Analysts who want to expand their skills into machine learning. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Data Analysts who work in fields such as healthcare, finance, and marketing.

See salaries and explore the career path for Data Analyst

Data Architect

Data Architects are responsible for designing and managing data architectures. This course, which provides an overview of Snowpark ML for Python, may be useful for Data Architects who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Data Architects who work in fields such as data management, data governance, and data integration.

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Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software applications. This course, which provides an overview of Snowpark ML for Python, may be useful for Software Engineers who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Software Engineers who work in fields such as web development, mobile development, and data engineering.

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Business Analyst

Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. This course, which provides an overview of Snowpark ML for Python, may be useful for Business Analysts who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Business Analysts who work in fields such as operations, finance, and marketing.

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Database Administrator

Database Administrators are responsible for managing and maintaining databases. This course, which provides an overview of Snowpark ML for Python, may be useful for Database Administrators who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Database Administrators who work in fields such as database management, data security, and data recovery.

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Statistician

Statisticians are responsible for collecting, analyzing, and interpreting data. This course, which provides an overview of Snowpark ML for Python, may be useful for Statisticians who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Statisticians who work in fields such as healthcare, finance, and marketing.

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Actuary

Actuaries are responsible for assessing and managing financial risks. This course, which provides an overview of Snowpark ML for Python, may be useful for Actuaries who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Actuaries who work in fields such as life insurance, health insurance, and pensions.

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Operations Research Analyst

Operations Research Analysts are responsible for applying mathematical and analytical techniques to solve business problems. This course, which provides an overview of Snowpark ML for Python, may be useful for Operations Research Analysts who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Operations Research Analysts who work in fields such as supply chain management, logistics, and healthcare.

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Data Science Manager

Data Science Managers are responsible for leading and managing data science teams. This course, which provides an overview of Snowpark ML for Python, may be useful for Data Science Managers who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Data Science Managers who work in fields such as healthcare, finance, and marketing.

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Financial Analyst

Financial Analysts are responsible for analyzing financial data and making investment recommendations. This course, which provides an overview of Snowpark ML for Python, may be useful for Financial Analysts who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Financial Analysts who work in fields such as investment management, portfolio management, and risk management.

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Marketing Analyst

Marketing Analysts are responsible for analyzing marketing data and making marketing recommendations. This course, which provides an overview of Snowpark ML for Python, may be useful for Marketing Analysts who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Marketing Analysts who work in fields such as digital marketing, social media marketing, and email marketing.

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Risk Analyst

Risk Analysts are responsible for identifying, assessing, and managing risks. This course, which provides an overview of Snowpark ML for Python, may be useful for Risk Analysts who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Risk Analysts who work in fields such as financial risk management, operational risk management, and compliance.

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Quantitative Analyst

Quantitative Analysts are responsible for developing and implementing mathematical and statistical models to solve financial problems. This course, which provides an overview of Snowpark ML for Python, may be useful for Quantitative Analysts who want to expand their skills into data science. The course provides a foundation in the use of Python for data manipulation and analysis, as well as an introduction to the use of machine learning for predictive modeling. These skills can be valuable for Quantitative Analysts who work in fields such as investment management, portfolio management, and risk management.

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