❗The content presented here is sourced directly from Youtube platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

This course provides an introduction to the Queryverse, a Julia data science stack. Participants will learn the basics of the Queryverse, including its components, how to use them, and how to combine them to create powerful data science applications. The course will cover topics such as data manipulation, data visualization, machine learning, and more. Participants will also learn how to use the Queryverse to create reproducible data science workflows. By the end of the course, participants will have a solid understanding of the Queryverse and be able to use it to create data science applications.

[Applications]
After completing this course, students should be able to apply the Queryverse to their own data science projects. They should be able to use the Queryverse to query, manipulate, and visualize data. Additionally, they should be able to use the Queryverse to build machine learning models and deploy them in production. Finally, they should be able to use the Queryverse to create interactive web applications.

[Career Paths]
1. Data Scientist: Data Scientists use their knowledge of mathematics, statistics, and computer science to analyze large datasets and uncover insights. They use a variety of tools and techniques to explore, visualize, and interpret data. As the demand for data-driven decision making increases, the need for Data Scientists is growing.

2. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use a variety of techniques, such as supervised and unsupervised learning, to build models that can be used to make predictions and automate tasks. As the demand for automation and predictive analytics increases, the need for Machine Learning Engineers is growing.

3. Data Engineer: Data Engineers are responsible for designing, building, and maintaining data pipelines. They use a variety of tools and techniques to extract, transform, and load data from various sources. As the demand for data-driven decision making increases, the need for Data Engineers is growing.

4. Business Intelligence Analyst: Business Intelligence Analysts use their knowledge of data analysis and visualization to help organizations make better decisions. They use a variety of tools and techniques to analyze data and create reports and dashboards. As the demand for data-driven decision making increases, the need for Business Intelligence Analysts is growing.

[Education Paths]
1. Bachelor of Science in Data Science: This degree path focuses on the fundamentals of data science, including data analysis, data visualization, machine learning, and data engineering. Students learn to use the Queryverse stack to analyze and interpret data, as well as develop predictive models. This degree path is becoming increasingly popular as businesses and organizations rely more heavily on data-driven decision making.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence systems and algorithms. Students learn to use the Queryverse stack to develop AI models and applications. This degree path is becoming increasingly popular as AI technology is becoming more widely used in various industries.

3. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of machine learning algorithms and systems. Students learn to use the Queryverse stack to develop and optimize machine learning models. This degree path is becoming increasingly popular as machine learning technology is becoming more widely used in various industries.

4. Master of Science in Data Science and Analytics: This degree path focuses on the fundamentals of data science and analytics. Students learn to use the Queryverse stack to analyze and interpret data, as well as develop predictive models. This degree path is becoming increasingly popular as businesses and organizations rely more heavily on data-driven decision making.