Introduction to Vertex Forecasting and Time Series in Practice from Coursera

What's inside

Syllabus

Introduction

This module addresses the reasons to build a forecasting solution on Google Cloud and introduces the learning objectives.

Time series and forecasting fundamentals

This module provides a theoretical foundation of types of sequence models, time series patterns and analysis, and forecasting notations.

Forecasting options on Google Cloud

This module introduces two major options to build a forecasting solution on Google Cloud: BigQuery ML and Vertex AI Forecast (AutoML). It also investigates the unique features of Vertex AI Forecast and explores an end-to-end workflow with AutoML.

Data preparation

This module explores the transformation of original data to the data types and format supported by Vertex AI. It also introduces the different types of features in time series and the best practices for data ingestion.

Model training

This module walks learners through the model training and demonstrates the configuration details such as the setup of context window, forecast horizon, and optimization objective.

Model evaluation

This module describes the training data split, demonstrates the evaluation metrics, and recommends the approaches to improve the model performance.

Model deployment

This module demonstrates model prediction, specifically the batch prediction with Vertex AI Forecast. It also explores machine learning operations (MLOps) and the transition from development to production.

Model monitoring

This module describes model drift and the approach of model retraining. It also demonstrates the automation of the forecasting workflow by using Vertex AI Pipelines.

Vertex forecasting in retail

This module describes a use case to build a forecasting solution with Vertex AI Forecast in a retail store. It demonstrates the steps and considerations, walks through a pilot study with two different datasets, and discusses the challenges and lessons.

Summary

This module addresses the main features of Vertex AI Forecast and summarizes the main topics of each module.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills that are highly sought after in the data science industry

Utilizes real-world retail data, showcasing practical applications

Suitable for learners starting their journey in time series forecasting

Offers hands-on experience through model building and deployment

Leverages Google Cloud's Vertex AI platform, providing industry-standard tools

Covers both theoretical foundations and practical implementation

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 Introduction to Vertex Forecasting and Time Series in Practice with these activities:

Read 'Forecasting: Principles and Practice'

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This book provides a comprehensive overview of forecasting methods and will help you build a strong foundation in the subject.

View Forecasting: Principles and Practice on Amazon

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Read Chapters 1-3 to gain an understanding of the basics of forecasting.
Work through the examples in Chapter 4 to practice applying forecasting methods.
Complete the exercises at the end of each chapter to test your understanding.

Join a study group for this course

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This study group will provide you with an opportunity to collaborate with other students and learn from each other.

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Find a study group for this course.
Attend the study group meetings.
Participate in the discussions.

Solve forecasting practice problems

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These practice problems will help you to develop your skills in forecasting.

Browse courses on Time Series Forecasting

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Find a set of forecasting practice problems online.
Attempt to solve the problems on your own.
Check your answers against the provided solutions.

One other activity

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Build a forecasting model for a real-world dataset

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This project will give you hands-on experience in building and evaluating a forecasting model.

Browse courses on Time Series Modeling

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Choose a dataset that you are interested in forecasting.
Explore the data and identify any trends or patterns.
Select a forecasting algorithm and train a model on the data.
Evaluate the performance of the model using metrics such as MAE and RMSE.
Deploy the model and use it to make predictions.

Career center

Learners who complete Introduction to Vertex Forecasting and Time Series in Practice will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts study data using specialized applications and statistical techniques. They analyze data to help businesses understand trends, improve performance, and make better decisions. This course helps build a foundation for Data Analysts because it teaches the fundamental principles of time series forecasting, which is a critical skill for analyzing data over time. The course also introduces learners to Vertex AI, a powerful cloud-based platform that provides tools and services for building and deploying machine learning models.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They help businesses make investment decisions and manage risk. This course may be useful for Quantitative Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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Data Scientist

Data Scientists use their knowledge of math, statistics, and computer science to extract insights from data. They develop and implement machine learning models to solve business problems. This course may be useful for Data Scientists who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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Machine Learning Engineer

Machine Learning Engineers design, build, and deploy machine learning models. They work closely with Data Scientists and other engineers to develop and implement machine learning solutions. This course may be useful for Machine Learning Engineers who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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Business Analyst

Business Analysts study business processes and identify ways to improve them. They use data analysis to understand customer needs and develop strategies for growth. This course may be useful for Business Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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Market Researcher

Market Researchers study market trends and customer behavior. They use data analysis to identify opportunities for new products and services. This course may be useful for Market Researchers who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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Financial Analyst

Financial Analysts study financial data to identify investment opportunities and make recommendations to clients. They use a variety of techniques to analyze data, including time series forecasting. This course may be useful for Financial Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. They work with businesses to improve efficiency and productivity. This course may be useful for Operations Research Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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System Analyst

System Analysts design and implement computer systems. They work with businesses to identify their needs and develop systems that meet those needs. This course may be useful for System Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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Statistician

Statisticians collect, analyze, and interpret data. They use statistical methods to develop models and make predictions. This course may be useful for Statisticians who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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Risk Analyst

Risk Analysts identify and assess risks to businesses. They use data analysis to develop strategies for managing risk. This course may be useful for Risk Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. This course may be useful for Product Managers who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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User Experience Researcher

User Experience Researchers study how users interact with products and services. They use data analysis to identify areas for improvement. This course may be useful for User Experience Researchers who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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Web Analyst

Web Analysts study website traffic and user behavior. They use data analysis to identify ways to improve the user experience and increase website traffic. This course may be useful for Web Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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Sales Analyst

Sales Analysts study sales data to identify trends and opportunities. They use data analysis to develop strategies for increasing sales. This course may be useful for Sales Analysts who want to learn more about time series forecasting and how to use Vertex AI to build and deploy forecasting models.

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