What you will learn
- The basic objects of linear algebra – how to compute with them, how they fit together theoretically, and how they can be used to solve real problems
- Data models and systems for processing signals, images, and big data sets
- Practical implementation of signal processing and machine learning algorithms on data from the real world
- Ability to navigate the data science process as an expert instead of relying on trial and error with black box methods
Across industries, data science is becoming an ever-increasing necessity for organizations to be successful. Collecting, analyzing and strategically acting on big data sets based on key signals is critical, and data scientists are the ones leading the way and informing decision makers.
This online Intermediate-level program is designed for working adults looking to pursue a career as a data scientist and roles focused on machine learning. Whether you already work with data in your current role or are interested in the larger field of computer science, this program is designed to build a solid foundation in underlying algorithms and principles of the tools used. This Foundational Data Science MicroBachelors program consists of two courses that develop key mathematical skills and explores terminology, models, and algorithms found in signal processing and machine learning.
With the successful completion of this program, passing all courses with a 70% or better via the verified (paid) track, you’ll not only receive a certificate highlighting your achievement, but also have the option to collect real college credit (included in the price!) that you can count towards a pursuit of a bachelor’s degree.
Prerequisite - In addition to the math skills developed in the Linear Algebra course, calculus (which is not a part of this program) is required.
Courses in this program
RICEx's Elements of Data Science MicroBachelors® Program
- 6–8 hours per week, for 8 weeks
This course is an introduction to linear algebra. You will discover the basic objects of linear algebra – how to compute with them, how they fit together theoretically, and how they can be used to solve real problems.
- 6–8 hours per week, for 8 weeks
Learn the mathematical backbone of data science. Signals, systems, and transforms: from their theoretical mathematical foundations, to practical implementation in circuits and computer algorithms, to machine learning algorithms that convert signals into inferences.
Program Certificate Requirement
In order to be eligible for credit and a program certificate, you must purchase, complete and pass with a 70% grade or higher in all Elements of Data Science program courses.
edX has partnered with Thomas Edison State University, a public university in New Jersey that specializes in adult education, to provide academic credit for MicroBachelors programs at no additional cost.
Please note, academic credit for this program is not provided by Rice University. This program is currently under credit review by Thomas Edison State University.
Check back here for an update on the credit review status for the Elements of Data Science program. Learn more about credit.
- Data Science is one of the fastest growing job areas in the US with jobs projected to grow 19% over the next 10 years. (Source: Burning Glass)
- The average salary for an Entry Level Data Scientist is about $100,000. (Source: Burning Glass)
- Skills in Data Science are needed to pursue careers as a Data Analyst, Quality Assurance Engineer and other data-driven entry-level roles.