Microcredential
Elements of Data Science
Rice University via edX Microbachelors
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.
Syllabus
Course 1: The Math of Data Science: Linear Algebra
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.
Course 2: Discrete Time Signals and Systems
Enter the world of signal processing: analyze and extract meaning from the signals around us!
Course 3: Signals, Systems, and Learning
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.
Courses
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Coming Soon February 2021. Technological innovations have revolutionized the way we view and interact with the world around us. Editing a photo, re-mixing a song, automatically measuring and adjusting chemical concentrations in a tank: each of these tasks requires real-world data to be captured by a computer and then manipulated digitally to extract the salient information. Ever wonder how signals from the physical world are sampled, stored, and processed without losing the information required to make predictions and extract meaning from the data?
Students will find out in this rigorous mathematical introduction to the engineering field of signal processing: the study of signals and systems that extract information from the world around us. This course will teach students to analyze discrete-time signals and systems in both the time and frequency domains. Students will learn convolution, discrete Fourier transforms, the z-transform, and digital filtering. Students will apply these concepts in interactive MATLAB programming exercises (all done in browser, no download required).
Learners should have strong problem solving skills, the ability to understand mathematical representations of physical systems, and advanced mathematical background (one-dimensional integration, matrices, vectors, basic linear algebra, imaginary numbers, and sum and series notation). This course is an excerpt from an advanced undergraduate class at Rice University taught to all electrical and computer engineering majors.
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Coming Soon March 2021. Data science is of growing importance in every STEM field. While data science tools are more readily available now than ever before, properly using these tools requires a mathematical understanding of the algorithms within. This class develops a principled approach to using the terminology, models, and algorithms found in signal processing and machine learning, the mathematical backbone of data science.
Taught by
Richard G. Baraniuk and Stephen Wang
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