The demand for Data Science programs is skyrocketing with 64% more Data Science enrollments in the last year. Rize’s technically rigorous, hands-on Data Science program includes two foundational Data Analytics Core courses, project-based Python skill development, and a deep focus on predictive modeling and AI systems such as Large Language Models (LLMs).
Leverage over 3+ years of continuous course refinement, incorporating valuable feedback from instructors, students, curriculum committees, and extensive course testing.
You can launch this Data Science program by adding 6 Rize courses to your existing catalog, enabling you to market a Data Science degree to prospective students in as little as one semester. Expand below to read the course descriptions.
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.
.png)
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.
