This Specialization, offered by Howard University, is ideal for individuals exploring or pursuing careers in data science or seeking to understand data science for their current roles. It builds upon mathematical foundations and equips learners with key applied tools for using and analyzing large datasets.
Whether you are a student considering a career in data science, a business professional seeking to apply data science principles to your work, or a curious lifelong learner, this course provides the support and information needed to get started. The comprehensive curriculum covers linear algebra fundamentals, matrix algebra using Python, regression model building, and a capstone project to apply these concepts in a real-world data science problem.
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This course comprises four modules covering linear algebra fundamentals, matrix algebra using Python, building regression models, and a capstone project applying these concepts to a real-world data science problem.
This course introduces beginners to applying basic data science concepts to real-world problems. Topics include systems of linear equations, matrix operations, and vector equations, catering to both new learners and those seeking a refresher.
Explore finding inverses and matrix algebra using Python, practicing row reduction, defining linear transformations, and gaining proficiency in fundamental linear algebra concepts.
Develop skills in distinguishing between different types of regression models, applying the Method of Least Squares to datasets by hand and using Python, and employing linear regression models to identify scenarios.
Review the specifics of the Capstone project, create and run a regression model, and share results with peers to apply linear algebra concepts to a real-world data science problem.
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