Elements of Data Science with Rice University
Skills Covered: Linear Algebra, Data Models, Data Systems, Discrete-Time Signals, Discrete-Time Systems, Big Data Sets, Signal Processing, Machine Learning, Algorithms, Black-Box Methods
ABOUT THIS MICRO BACHELOR
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
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