This course is the first of a series that is designed for beginners who want to learn how to apply basic data science concepts to real-world problems. You might be a student who is considering pursuing a career in data science and wanting to learn more, or you might be a business professional who wants to apply some data science principles to your work. Or, you might simply be a curious, lifelong learner intrigued by the powerful tools that data science and math provides. Regardless of your motivation, we’ll provide you with the support and information you need to get started.
Este curso faz parte do Programa de cursos integrados Linear Algebra for Data Science Using Python
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Informações sobre o curso
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Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
NÃvel iniciante
Aprox. 12 horas para completar
Inglês
Legendas: Inglês
Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
NÃvel iniciante
Aprox. 12 horas para completar
Inglês
Legendas: Inglês
oferecido por
Howard University
Founded in 1867, Howard University is a private, research university that is comprised of 14 schools and colleges. Students pursue more than 140 programs of study leading to undergraduate, graduate and professional degrees. The University operates with a commitment to Excellence in Truth and Service and has produced one Schwarzman Scholar, three Marshall Scholars, four Rhodes Scholars, 12 Truman Scholars, 25 Pickering Fellows and more than 165 Fulbright recipients. Howard also produces more on-campus African American Ph.D. recipients than any other university in the United States. For more information on Howard University, visit www.howard.edu.
Programa - O que você aprenderá com este curso
5 horas para concluir
Introduction to Matrices and Linear Algebra
In module 1, you'll learn how to explain fundamental concepts of linear algebra and how to use Python, one of the most powerful programming languages, to model different data. We will cover the following learning objectives.
5 horas para concluir
15 vÃdeos (Total 85 mÃn.), 6 leituras, 3 testes
3 horas para concluir
Using Linear Algebra Concepts in Python
Let's recap! In module 1, you performed software installation, learned some best practices, and learned how graphs are used to model data in Python. In module 2, you'll gain the knowledge you need to use linear algebra to solve data science problems. You'll also perform matrix algebra on large data sets using Python. We will cover the following learning objectives.
3 horas para concluir
7 vÃdeos (Total 46 mÃn.), 1 leitura, 3 testes
2 horas para concluir
Vector Equations and Systems of Linear Equations
Let's recap! In module 2, you learned how to use linear algebra to solve data science problems. Using Python, you also learned how to perform matrix algebra on large data sets. In module 3, you will learn how to define vector equations and use vector equations to model data. We will cover the following learning objectives.
2 horas para concluir
7 vÃdeos (Total 47 mÃn.)
2 horas para concluir
Real-World Applications of Vector Equations
Welcome to the final module of this course! Over the past 3 modules, you have been introduced to and gained knowledge on the following topics:- Version control - Git Bash, Jupyter Notebook via Anaconda, NumPy and SymPy, and other software tools, Modeling data, Matrix algebra and, Vector equations. In the final module of the course, you'll apply what you've learned to concrete, real-world examples. You'll practice using vector equations to study data sets and provide peer reviews. We will cover the following learning objectives.
2 horas para concluir
4 vÃdeos (Total 24 mÃn.), 1 leitura, 2 testes
Sobre Programa de cursos integrados Linear Algebra for Data Science Using Python
This Specialization is for learners interested in exploring or pursuing careers in data science or understanding some data science for their current roles. This course will build upon your previous mathematical foundations and equip you with key applied tools for using and analyzing large data sets.
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