Analítica y ciencia de datos para negocios

Go to Course: https://www.coursera.org/learn/analtica-y-ciencia-de-datos-para-negocio

Introduction

**Course Review: "Analítica y ciencia de datos para negocios" on Coursera** **Overview** The "Analítica y ciencia de datos para negocios" course on Coursera is designed for those looking to deepen their understanding of data analytics and its application in business contexts. With a strong emphasis on the principles of analytics, data science, and the stages involved in executing a data analysis project, this course is a valuable resource for both budding and experienced professionals. Participants will learn about essential pre-processing techniques, including handling missing data, detecting outliers, and applying normalization and discretization methods to prepare data for analysis. **Syllabus Breakdown** The course is structured into several modules that progressively build the student's knowledge and practical skills in data analytics: 1. **INTRODUCCIÓN** The course begins with an introduction to essential concepts, forming the foundational knowledge that is crucial for success in the subsequent modules. This segment ensures that students are well-prepared for the content that follows, allowing them to revisit important concepts as needed. 2. **MÓDULO 1: CONCEPTOS GENERALES** This module offers a comprehensive overview of the primary business analytics techniques. It provides insight into the various analytic models commonly used in business environments, arming students with the knowledge necessary to identify and apply the right techniques for their specific needs. 3. **MÓDULO 2: PREPROCESAMIENTO DE DATOS (1): Datos faltantes** Delving into data pre-processing, this module focuses on addressing missing data—one of the most common and challenging issues in data analysis. Through detailed explanations and practical examples, students will learn various strategies to handle missing data effectively. 4. **MÓDULO 3: PREPROCESAMIENTO DE DATOS (2): Detección de outliers** Continuing the theme of pre-processing, this module tackles the identification and treatment of outliers. By understanding how to detect extreme values, students can enhance the accuracy and reliability of their data analyses. 5. **MÓDULO 4: PREPROCESAMIENTO DE DATOS (3): Normalización, Discretización y Reducción dimensional** In the final module, participants will explore advanced techniques such as normalization, discretization, and dimensionality reduction. These skills are crucial for transforming and preparing data for effective analysis, ensuring that students are well-versed in how to optimize their datasets. **Recommendation** I wholeheartedly recommend the "Analítica y ciencia de datos para negocios" course for anyone looking to gain practical skills in data analytics applied to business. The course is well-structured, facilitating a gradual and comprehensive learning experience. Each module builds upon the previous one, ensuring that students not only acquire theoretical knowledge but also practical skills that can be applied in real-world scenarios. Whether you are a business professional wanting to leverage data for decision-making or a student aspiring to enter the field of data analytics, this course will equip you with the necessary tools and insights. The focus on techniques like handling missing data and detecting outliers is particularly beneficial, as these are common hurdles in data analysis. Moreover, the ability to revisit fundamental concepts through the introduction section ensures that learners are supported at every step of their journey. In a world increasingly driven by data, this course is a significant investment for your professional development. Enroll today and take the first step towards becoming a proficient data analyst!

Syllabus

INTRODUCCIÓN

Esta sección presenta los conceptos esenciales para poder desarrollar el curso con éxito. Te invitamos a revisarlo todas las veces que lo consideres necesario.

En este módulo, presentaré las principales técnicas de business analytics, a partir de una revisión general del universo de técnicas de analítica de datos y cuáles son la s más comúnmente usadas en los modelos de analítica aplicados en negocios.

En este Módulo se desarrolla el preprocesamiento de datos que corresponde a la etapa más compleja y de mayor duración de tiempo dentro de un proyecto de analítica de negocios. En particular, abordaremos todo lo referente al problema de los datos faltantes y las opciones de tratamiento frente a dicho problema.

En este módulo, continuaremos aprendiendo sobre la etapa del preprocesamiento, pero ahora nos enfocaremos en la detección y el tratamiento de los valores atípicos o extremos, llamados también outliers.

En este último módulo se continuará explicando la etapa del preprocesamiento, pero ahora nos enfocaremos en las técnicas de transformación y la reducción dimensional de datos.