3611

Please select the start dates for your courses below.

1
The Math of Data Science: Linear Algebra

Scheduled Start

2
Discrete Time Signals and Systems

Scheduled Start

3
Signals, Systems, and Learning

Scheduled Start

Program Overview

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.

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

Courses in this program

1
The Math of Data Science: Linear Algebra

Course Details
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.

2
Discrete Time Signals and Systems

Course Details
Enter the world of signal processing: analyze and extract meaning from the signals around us!

3
Signals, Systems, and Learning

Course Details
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.

Meet your instructors

Richard G. Baraniuk

Professor Richard G. Baraniuk grew up in Winnipeg, Canada, the coldest city in the world with a population over 600,000. He studied Electrical Engineering at the University of Manitoba, the University of Wisconsin-Madison, and the University of Illinois at Urbana-Champaign. Dr. Baraniuk joined Rice University in Houston, Texas, in 1993 and is now the Victor E. Cameron Professor of Electrical and Computer Engineering. He is a member of the Digital Signal Processing (DSP) group and Director of the Rice center for Digital Learning and Scholarship (RDLS). Dr. Baraniuk’s research interests lie in the areas of signal, image, and information processing and include machine learning and compressive sensing. He is Director of Connexions, a non-profit publishing project to bring learning materials to the Internet Age which is used by over 2 million people from nearly 200 countries.

Stephen Wang

Stephen Wang is an Associate Teaching Professor of Mathematics at Rice University, where he has been a faculty member since 2015. He earned a PhD in Mathematics from the University of Chicago, where he specialized in geometry. He has received teaching awards from both Chicago and Harvard University. Dr. Wang has also been a professor at Haverford College and Bucknell University, and was part of the team that created the MIT Calculus course on edX.
3611

Duration

6 Months

Learning Partner

Rice University