We may earn an affiliate commission when you visit our partners.
Course image
Silvia Guadalupe López Alonzo

Históricamente, las matemáticas nacieron por primera vez, debido a la necesidad de entender nuestro entorno y tomar decisiones. En particular, la ciencia de datos (data science), se enfoca en el procesamiento de datos: analizar, explorar e interpretar conjuntos de datos, y con base en ello, tener un panorama completo del presente.

Read more

Históricamente, las matemáticas nacieron por primera vez, debido a la necesidad de entender nuestro entorno y tomar decisiones. En particular, la ciencia de datos (data science), se enfoca en el procesamiento de datos: analizar, explorar e interpretar conjuntos de datos, y con base en ello, tener un panorama completo del presente.

Al escuchar sobre la ciencia de los datos, se podría pensar que es un tema únicamente relacionado con los científicos de datos y el big data, dirigido a personas que hablan lenguajes de programación como python o que concierne solo a empresas de base tecnológica e inteligencia artificial como IBM o Amazon. Sin embargo, cada vez más, es necesario hacer uso de la inteligencia de negocios y por medio de la minería de datos obtener resultados para una mejor toma de decisiones sin importar el giro de la empresa.

Considerando que la mercadotecnia tiene entre sus objetivos la identificación de necesidades y preferencias de los consumidores para satisfacer sus necesidades, aprender a realizar encuestas que reúnan datos y conocer las herramientas de análisis de datos idóneas para grandes cantidades de datos, se convierte imprescindible para el crecimiento de toda empresa o negocio.

Sin necesidad de ser un experto en data science o programador, actualmente existen una serie de técnicas estadísticas que pueden ser utilizadas por un marketer, como excel y spss, y estas le permitirán realizar data mining o extracción de datos, para identificar las variables importantes de un producto, clasificar a los consumidores, organizar sus gustos y en general, tomar decisiones precisas con una base matemática a problemas complejos.

Este curso se encuentra enfocado en su totalidad al estudio de mercado, desde los ejemplos hasta las técnicas de análisis estadístico seleccionadas, tanto para pequeños como grandes volúmenes. Utilizando softwares estadísticos para realizar el proceso matemático en las bases de datos y la visualización de datos, permitiendo un enfoque interpretativo de los resultados para la toma de decisiones.

Utiliza el business intelligence a tu favor y adquiere las herramientas necesarias para llevar el marketing de tu empresa o negocio al siguiente nivel y realizar una toma de decisiones acertada.

Enroll now

What's inside

Syllabus

Inicia aquí
Módulo 1: Análisis de regresión
Módulo 2: Análisis de conjunto
Read more
Módulo 3: Análisis multidimensional
Módulo 4: Análisis factorial
Módulo 5: Análisis discriminante
Fin del Curso

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Esto ofrece una base sólida en los principios, procesos, herramientas y aplicaciones de la mercadotecnia para principiantes completos
Brinda una perspectiva integral del amplio campo de la mercadotecnia, cubriendo aspectos fundamentales y tendencias contemporáneas
Explora casos de estudio del mundo real y ejemplos prácticos para ilustrar los conceptos y estrategias de mercadotecnia
Los instructores tienen experiencia en la industria y comparten conocimientos prácticos y basados en la experiencia

Save this course

Save Ciencia de Datos Aplicada al Marketing to your list so you can find it easily later:
Save

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Ciencia de Datos Aplicada al Marketing with these activities:
Desarrollar un plan de marketing basado en datos
Crea un plan de marketing completo utilizando los conocimientos adquiridos en el curso para demostrar tu capacidad de aplicar el análisis de datos a la estrategia de marketing de la vida real.
Show steps
  • Identifica un problema o necesidad de marketing.
  • Recopila y analiza datos relevantes del mercado.
  • Desarrolla y evalúa estrategias de marketing alternativas.
  • Crea un plan de marketing completo que integre los resultados del análisis de datos.
Show all one activities

Career center

Learners who complete Ciencia de Datos Aplicada al Marketing will develop knowledge and skills that may be useful to these careers:
Marketing Manager
Marketing Managers oversee the development and execution of marketing campaigns for companies and organizations. They research markets, identify target audiences, and develop strategies to reach and engage with them. Taking this course can help Marketing Managers learn how to use data analysis to better understand their target audience, identify trends, and make informed decisions about their marketing campaigns.
Market Research Analyst
Market Research Analysts conduct research to understand consumer behavior and market trends. They use this information to help businesses make informed decisions about their products and services. This course can help Market Research Analysts learn how to collect, analyze, and interpret data to gain insights into consumer behavior.
Data Analyst
Data Analysts use their knowledge of data analysis techniques to help businesses make informed decisions. They collect, clean, and analyze data to identify trends and patterns. This course can help Data Analysts learn how to use statistical techniques to analyze data and make informed recommendations.
Business Analyst
Business Analysts use their knowledge of business processes and data analysis to help businesses improve their operations. They work with stakeholders to identify areas for improvement and then develop and implement solutions. This course can help Business Analysts learn how to use data analysis techniques to identify and solve business problems.
Consultant
Consultants provide advice to businesses on a variety of topics, including marketing, finance, and operations. They use their knowledge of business principles and data analysis techniques to help businesses improve their performance. This course can help Consultants learn how to use data analysis techniques to identify and solve business problems.
Product Manager
Product Managers are responsible for the development and management of products and services. They work with cross-functional teams to define product requirements, prioritize features, and track progress. This course can help Product Managers learn how to use data analysis techniques to understand customer needs and make informed decisions about product development.
Financial Analyst
Financial Analysts use their knowledge of finance and data analysis to help businesses make informed investment decisions. They analyze financial data to identify trends and patterns, and they make recommendations on how to invest money. This course can help Financial Analysts learn how to use data analysis techniques to analyze financial data and make informed investment decisions.
Statistician
Statisticians use their knowledge of statistics to collect, analyze, and interpret data. They work in a variety of fields, including marketing, finance, and healthcare. This course can help Statisticians learn how to use statistical techniques to analyze data and make informed decisions.
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics and data analysis to help businesses improve their operations. They use mathematical models to simulate and analyze business processes, and they make recommendations on how to improve efficiency and effectiveness. This course can help Operations Research Analysts learn how to use data analysis techniques to analyze business processes and make informed recommendations.
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of computer science and data analysis techniques to create software that meets the needs of businesses and consumers. This course may be useful for Software Engineers who want to learn how to use data analysis techniques to improve the quality of their software applications.
Computer Scientist
Computer Scientists conduct research in the field of computer science. They develop new theories and algorithms, and they design and implement new computer systems. This course may be useful for Computer Scientists who want to learn how to use data analysis techniques to analyze data and make informed decisions.
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. They use their knowledge of data analysis and machine learning to create models that can learn from data and make predictions. This course may be useful for Machine Learning Engineers who want to learn how to use data analysis techniques to improve the quality of their machine learning models.
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics and data analysis to analyze financial data. They develop models to predict future financial trends, and they make recommendations on how to invest money. This course may be useful for Quantitative Analysts who want to learn how to use data analysis techniques to improve the quality of their financial models.
Data Scientist
Data Scientists use their knowledge of data analysis and machine learning to solve business problems. They work with large datasets to identify trends and patterns, and they develop models to predict future outcomes. This course may be useful for Data Scientists who want to learn how to use data analysis techniques to solve business problems.
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. They use their knowledge of data analysis and artificial intelligence to create systems that can learn from data and make decisions. This course may be useful for Artificial Intelligence Engineers who want to learn how to use data analysis techniques to improve the quality of their artificial intelligence systems.

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Ciencia de Datos Aplicada al Marketing.
Provides a comprehensive overview of data science techniques used in marketing, including data mining, predictive modeling, and customer segmentation. It valuable reference for marketers looking to leverage data to improve their decision-making.
Provides a practical introduction to data science for business professionals. It covers topics such as data mining, machine learning, and statistical analysis.
Provides a thorough introduction to data mining techniques and their applications in business intelligence. It covers a wide range of topics, including data preparation, feature selection, and model evaluation.
Provides a comprehensive overview of marketing research methods and techniques. It covers topics such as research design, data collection, data analysis, and reporting.
Provides a comprehensive overview of social media marketing strategies and tactics. It covers a wide range of topics, including social media platforms, content marketing, and social media analytics.
Provides a comprehensive overview of digital marketing analytics techniques. It covers a wide range of topics, including web analytics, social media analytics, and mobile analytics.
Provides a comprehensive overview of marketing management principles and practices. It covers a wide range of topics, including marketing strategy, product development, pricing, and distribution.
Provides a comprehensive overview of big data techniques used in marketing. It covers a wide range of topics, including data collection, data analysis, and data visualization.
Provides a comprehensive overview of marketing analytics techniques using Microsoft Excel. It covers a wide range of topics, including data analysis, data visualization, and data mining.
Provides a practical introduction to marketing data science using Python. It covers topics such as data collection, data cleaning, data analysis, and data visualization.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Ciencia de Datos Aplicada al Marketing.
Ciencia de Datos Aplicada al Marketing
Most relevant
Introducción al Análisis de Datos
Most relevant
Big Data sin misterios
Most relevant
Minería de Datos: Análisis de la Canasta de Compra
Most relevant
Introducción a SQL y bases de datos relacionales
Most relevant
Herramientas para el Análisis de Big Data
Most relevant
Mejora tu Negocio con Inteligencia Artificial
Most relevant
Visualización de Datos y Storytelling
Most relevant
Diseño de sistemas de información gerencial para intranet...
Most relevant
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser