Add this hands-on Data Analytics program to innovate your course offerings, attract a new set of students, and produce valuable job outcomes for your students.
A well-built portfolio is essential to landing a great career in Data Science/Data Analytics. 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 based heavily on UC Berkeley’s Data 100 class. Data Analytics combines data, computation, and inferential thinking to solve challenging problems and understand their intricacies. This class explores key principles and techniques of data science and teaches students how to create informative data visualizations. It also explores particular concepts of Linear Algebra which are central to Data Science.
This course builds on Principles and Techniques of Data Analytics I to provide students with a more robust understanding of the tools of a Data Scientist. Data Analytics combines data, computation, and inferential thinking to solve challenging problems to thereby better understand the world. This class explores key principles and techniques of data science, including quantitative critical thinking and algorithms for machine learning methods. It will also introduce students to the ways in which data analytics is deployed in healthcare, marketing, political science, criminal justice, and other fields.
This course is a capstone project in which students are asked to work through a full data science workflow on a set of real data drawn from sports, politics, business or public health. This course exists to prepare students for the kind of work they will do on Data Science or Analytics teams, and as such, also features an emphasis on interviewing for jobs in the space and communicating results to stakeholders.