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Updated in [May 19th, 2023]

This course provides an overview of NCAA March Madness and how to use Google Cloud to analyze NCAA basketball data with SQL. Students will learn how to use BigQuery to query and analyze NCAA basketball data, and build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games. The course will also cover topics such as data visualization, data cleaning, and data analysis. At the end of the course, students will have a better understanding of NCAA March Madness and how to use Google Cloud to analyze NCAA basketball data.

[Applications]
After completing this course, students can apply their newfound knowledge of BigQuery and Machine Learning to analyze data from other sports, such as football, baseball, and soccer. They can also use the same techniques to analyze data from other industries, such as finance, healthcare, and retail. Additionally, students can use the same techniques to build predictive models for other types of events, such as elections, stock market trends, and weather patterns.

[Career Paths]
1. Data Scientist: Data Scientists use their knowledge of statistics, mathematics, and computer science to analyze large datasets and uncover insights. They use a variety of tools and techniques to extract, clean, and transform data, and then use predictive analytics and machine learning to build models and make predictions. With the skills learned in this course, Data Scientists can use BigQuery and Machine Learning to analyze NCAA basketball data and build models to predict the outcomes of NCAA March Madness basketball tournament games.

2. Data Analyst: Data Analysts use their knowledge of statistics, mathematics, and computer science to analyze large datasets and uncover insights. They use a variety of tools and techniques to extract, clean, and transform data, and then use descriptive analytics to draw conclusions and make recommendations. With the skills learned in this course, Data Analysts can use BigQuery to analyze NCAA basketball data and draw conclusions about the performance of teams and players.

3. Business Intelligence Analyst: Business Intelligence Analysts use their knowledge of statistics, mathematics, and computer science to analyze large datasets and uncover insights. They use a variety of tools and techniques to extract, clean, and transform data, and then use descriptive analytics to draw conclusions and make recommendations. With the skills learned in this course, Business Intelligence Analysts can use BigQuery to analyze NCAA basketball data and draw conclusions about the performance of teams and players, and make recommendations to improve performance.

4. Machine Learning Engineer: Machine Learning Engineers use their knowledge of statistics, mathematics, and computer science to build and deploy machine learning models. They use a variety of tools and techniques to extract, clean, and transform data, and then use predictive analytics and machine learning to build models and make predictions. With the skills learned in this course, Machine Learning Engineers can use BigQuery and Machine Learning to analyze NCAA basketball data and build models to predict the outcomes of NCAA March Madness basketball tournament games.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, such as programming, software engineering, and computer architecture. It also covers topics such as artificial intelligence, machine learning, and data science. With the increasing demand for data-driven decision making, this degree path is becoming increasingly popular and is a great way to gain the skills needed to work with BigQuery and other data analysis tools.

2. Bachelor of Science in Data Science: This degree path focuses on the application of data science to solve real-world problems. It covers topics such as data mining, machine learning, and predictive analytics. With the increasing demand for data-driven decision making, this degree path is becoming increasingly popular and is a great way to gain the skills needed to work with BigQuery and other data analysis tools.

3. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence systems and their applications. It covers topics such as machine learning, natural language processing, and computer vision. With the increasing demand for AI-driven decision making, this degree path is becoming increasingly popular and is a great way to gain the skills needed to work with BigQuery and other data analysis tools.

4. Master of Science in Data Science: This degree path focuses on the application of data science to solve real-world problems. It covers topics such as data mining, machine learning, and predictive analytics. With the increasing demand for data-driven decision making, this degree path is becoming increasingly popular and is a great way to gain the skills needed to work with BigQuery and other data analysis tools.

Course Syllabus

Using BigQuery in the Google Cloud Console

BigQuery: Qwik Start - Command Line

Introduction to SQL for BigQuery and Cloud SQL

Exploring NCAA Data with BigQuery

Bracketology with Google Machine Learning