Add this hands-on Data Science program to innovate your course offerings, attract a new set of students, and produce valuable job outcomes for your students in the fields of Data Science, Machine Learning, and AI Engineering.
Produce students with strong technical skills who are able to solve challenging problems through a data-driven lens. Rize courses are structured around portfolio-building projects which teach hard skills, but also demonstrate those skills and higher level knowledge in the way hiring managers like to see.
The result - your students have a better shot at landing the job they want when they graduate.
In an increasingly data-driven world, everyone should be able to understand the numbers that govern our lives. Whether or not you want to work as a data analyst, being “data literate” will help you in your chosen field. In this course, you’ll learn the core concepts of inference and data analysis by working with real data. By the end of the term, you’ll be able to analyze large datasets and present your results.
This course is intended as a continuation of Foundations of Data Analytics I. In this course, you’ll learn how Data Analytics are applied within the workforce. Particular attention will be paid to the role of the Data Scientist or Analyst, machine learning and the applications of Big Data. By the end of the term, you will be able to design and execute a range of data-driven experiments.
This course is an introduction to data science in Python. Students will use advanced visualization and predictive modeling tools to turn raw data into actionable insights. Students will also learn how to use SQL to navigate databases.
This course takes a deep dive into machine learning models, natural language processing, and time series in Python. Students will build machine learning models that solve a range of different business problems, using both structured and unstructured data. Students will learn to create actionable and ethical predictions by balancing accuracy, usability, and interpretability, and bias in model selection, design, training, and inference
This course is a technical approach to cutting-edge AI methods. Students will productionize machine learning models to solve business problems, evaluate modern AI use cases (such as computer vision) and adapt Large Language Models (LLMs) for specific applications.
This course is a capstone project in which students are asked to work through a full data science workflow using real-world data. This course exists to prepare students for the kind of work they will do on Data Science teams, and as such also features an emphasis on interviewing for jobs in the space and communicating results to stakeholders.