Analítica de Datos e Inteligencia Artificial para Principiantes

Resumen

Learn the basic concepts of data analytics, AI, business intelligence, big data, machine learning, and deep learning. What you'll learn:

A brief overview of the history of analyzing data, from medieval statistics to the sophisticated techniques developed by the likes of Google and Microsoft.A look at data stores, which are growing exponentially, and the challenges of wrangling “big data.”Understanding of data mining—what it entails, different approaches, and who’s leading the way.A two-part discussion of business intelligence, including the principles of sound dashboard design and data presentation.The key differences between the four types of analytics—diagnostic, descriptive, predictive, and prescriptiveAn overview of specific analytics processes and models.A first look at AI, its evolution, its functions, and what it can do for businesses today.An exploration of machine learning—how systems can learn from data, identify patterns, and make decisions with little human intervention.A survey of deep learning technologies, including a variety of neural networks.An overview of the most important machine learning data modeling techniquesA practical and honest appraisal of the analytics and AI landscape today and moving forward, including the tremendous promise and the potential pitfalls.Resources for continued study on these topics. **This course includes downloadable exercise files to work with**The richest data store is only as good as your ability to search, sort, analyze, and present the data within it.

This introductory-level course will give students a broad overview of the theory and practice of data analytics and the many ways in which artificial intelligence (AI) contributes to it.Your instructor will begin with a brief history of data analytics and then proceed into discussions of data warehouses, data mining, business intelligence, machine learning, and other emerging AI techniques to make sense of big data.Students will learn how data is captured, cleansed, analyzed, and presented on business intelligence dashboards that captivate and persuade an audience. “It is a capital mistake to theorize before one has data," Sherlock Holmes once said.Whether you are investigating analytics as a potential career move or wish to better understand the terminology you encounter with increasing frequency in your professional circles, this course will give you the foundation you are looking for.This program includes 3 hours of instruction and a practice-based assessment, which will help students simulate real-world data analytics scenarios that are critical for success in today's increasingly complex workplace.Students will gain:

A brief overview of the history of analyzing data, from medieval statistics to the sophisticated techniques developed by the likes of Google and Microsoft.A look at data stores, which are growing exponentially, and the challenges of wrangling “big data.”Understanding of data mining—what it entails, different approaches, and who’s leading the way.A two-part discussion of business intelligence, including the principles of sound dashboard design and data presentation.The key differences between the four types of analytics—diagnostic, descriptive, predictive, and prescriptive—and how they relate to and build upon each other, and how they apply to various industries.An overview of specific analytics processes and models.A first look at AI, its evolution, its functions, and what it can do for businesses today.An exploration of machine learning—how systems can learn from data, identify patterns, and make decisions with little human intervention.A survey of deep learning technologies, including a variety of neural networks.An overview of the most important machine learning data modeling techniquesA practical and honest appraisal of the analytics and AI landscape today and moving forward, including the tremendous promise and the potential pitfalls.Resources for continued study on these topics.This course includes:

3 hours of video tutorials20 individual video lecturesCourse and Exercise files to follow alongCertificate of completion

Programa

• Introducción a la Analítica de Datos y la Inteligencia Artificial
Visión general de la analítica de datos
Fundamentos de la inteligencia artificial
Estructura del curso y descarga de archivos de ejercicios
• Fundamentos de la Analítica de Datos
Tipos de datos: estructurados y no estructurados
Recolección de datos y limpieza de datos
Introducción a las herramientas de visualización de datos
• Estadísticas Básicas para el Análisis de Datos
Estadísticas descriptivas: media, mediana, moda
Estadísticas inferenciales: muestreo y pruebas de hipótesis
Correlación y causalidad
• Toma de Decisiones Basada en Datos
Entendimiento de los problemas empresariales
Estrategias basadas en datos
Análisis e interpretación de resultados de datos
• Introducción a los Conceptos de IA
Evolución histórica y el papel de la IA hoy
Terminología básica de la IA
Panorama de aplicaciones populares de IA
• Fundamentos del Aprendizaje Automático
Aprendizaje supervisado vs. no supervisado
Algoritmos comunes: árboles de decisión, k-medias y regresión lineal
Introducción a las redes neuronales
• Herramientas y Plataformas para la Analítica de Datos y la IA
Visión general de herramientas: Python, R, Excel
Introducción a los cuadernos de Jupyter
Instrucciones de descarga e instalación para herramientas de software
• Ejercicios Prácticos y Estudios de Caso
Ejercicios prácticos con conjuntos de datos proporcionados
Estudios de caso del mundo real en diversas industrias
Discusiones en grupo y trabajo de proyectos
• Consideraciones Éticas en la Ciencia de Datos y la IA
Privacidad de datos y seguridad
Sesgo en los algoritmos de IA
Prácticas éticas en IA
• Conclusión y Próximos Pasos
Resumen de conceptos clave aprendidos
Recursos para continuar aprendiendo
Trayectorias profesionales en analítica de datos y IA

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Impartido por

Simon Sez IT

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Materias

Data Science