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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learning-path-r-real-world-data-mining-with-r/
课程评论：没有评论
课程名称：学习路径：R：真实世界数据挖掘与R 课程概述：Packt的在线视频学习路径是一系列独立视频产品的集合，按照逻辑顺序排列，使得每个视频的学习技能都建立在前一个视频的基础之上。随着数据生成速度的不断加快，数据挖掘在市场上的需求日益增长。R作为一种流行的统计编程语言，对于日常数据分析任务非常有用。数据挖掘是一个广泛的主题，学习起来需要一定的时间。本学习路径旨在帮助您快速理解数学基础，然后能够直接在R中应用所学知识。 该学习路径将探讨数据挖掘技术，展示如何将不同的挖掘概念应用于各种统计和数据应用，横跨多个领域。本课程是针对数据分析爱好者的完整学习过程。我们将首先全面介绍数据挖掘以及R如何通过其众多包简化数据挖掘过程。接着，我们将深入探讨数据挖掘技术，展示如何利用R的广泛算法将不同的挖掘概念应用到实际问题中。 本课程的目标是帮助您理解数据挖掘的基本概念，然后让您参与真实世界的数据集和项目。课程由领域内的一流专家创作。 授课专家介绍： - Romeo Kienzler：IBM Watson IoT部门的首席数据科学家，负责以顾问架构师的身份帮助全球客户解决数据分析问题。他拥有瑞士联邦理工学院的信息系统、生物信息学和应用统计的硕士学位，目前担任瑞士大学的数据挖掘副教授，研究重点是使用R等开源技术进行云规模数据挖掘。 - Pradeepta Mishra：数据科学家，预测建模专家，深度学习和机器学习实践者，经济计量学家。他目前领导印度班加罗尔的Ma Foi Analytics的数据科学和机器学习实践。他在多个领域（如医疗、保险、零售和电子商务等）解决分类、回归、模式识别、时间序列预测等项目，拥有超过10年的经验。 通过此课程，您将获得在数据挖掘领域中所需的必要技能，并能够应用R进行实际数据分析。
Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It is very useful for day-to-day data analysis tasks. Data mining is a very broad topic and takes some time to learn. This Learning Path will help you to understand the mathematical basics quickly, and then you can directly apply what you've learned in R. This Learning Path explores data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields. This Learning Path is the complete learning process for data-happy people. We begin with a thorough introduction to data mining and how R makes it easy with its many packages. We then move on to exploring data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields using R's vast set of algorithms. The goal of this Learning Path is to help you understand the basics of data mining with R and then get you working on real-world datasets and projects. This Learning Path is authored by some of the best in their fields. Romeo Kienzler Romeo Kienzler is the Chief Data Scientist of the IBM Watson IoT Division and working as an Advisory Architect helping client worldwide to solve their data analysis problems. He holds an M. Sc. of Information System, Bioinformatics and Applied Statistics from the Swiss Federal Institute of Technology. He works as an Associate Professor for data mining at a Swiss University and his current research focus is on cloud-scale data mining using open source technologies including R, ApacheSpark, SystemML, ApacheFlink, and DeepLearning4J. He also contributes to various open source projects. Additionally, he is currently writing a chapter on Hyperledger for a book on Blockchain technologies. Pradeepta Mishra Pradeepta Mishra is a data scientist, predictive modeling expert, deep learning and machine learning practitioner, and econometrician. He currently leads the data science and machine learning practice for Ma Foi Analytics, Bangalore, India. Ma Foi Analytics is an advanced analytics provider for Tomorrow's Cognitive Insights Ecology, using a combination of cutting-edge artificial intelligence, a proprietary big data platform, and data science expertise. He holds a patent for enhancing the planogram design for the retail industry. Pradeepta has published and presented research papers at IIM Ahmedabad, India. He is a visiting faculty member at various leading B-schools and regularly gives talks on data science and machine learning. Pradeepta has spent more than 10 years solving various projects relating to classification, regression, pattern recognition, time series forecasting, and unstructured data analysis using text mining procedures, spanning across domains such as healthcare, insurance, retail and e-commerce, manufacturing, and so on.
