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.

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

Linear Algebra, Part 1 — 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.
Discrete Time Signals and Systems — Enter the world of signal processing: analyze and extract meaning from the signals around us!

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Linear algebra is at the core of all of modern mathematics, and is used everywhere from statistics and data science, to economics, physics and electrical engineering. However, learning the subject is not principally about acquiring computational ability, but is more a matter of fluency in its language and theory.

In this course, we will start with systems of linear equations, and connect them to vectors and vector spaces, matrices, and linear transformations. We will be emphasizing the vocabulary throughout, so that students become comfortable working with the different aspects.

We will then introduce matrix and vector operations such as matrix multiplication and inverses, paying particular attention to their underlying purposes. Students will learn not just how to calculate them, but also why they work the way that they do.

We willdiscuss the key concepts of basis and dimension, which form the foundation for many of the more advanced concepts of linear algebra.

The last chapter concerns inner products, which allow us to use linear algebra for approximating solutions; we will see how this allows for applications ranging from statistics and linear regression to digital audio.