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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-for-everyone/
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课程名称：《人人都能学的数据科学》 概述：欢迎参加《人人都能学的数据科学》课程。在这一系列讲座中，我将为您提供数据科学的基本知识。本课程主要面向非数据科学家的管理人员，他们需要管理数据分析项目，或希望引入数据驱动的管理方法。因此，本课程提供的知识既有理论性，又富有实用性，但不涉及数学和编码的细节。然而，对于数据科学的初学者来说，本课程同样适用，它为学习数据科学的技术方面奠定了基础。 您将学习到： - 数据科学技术学习的基本概念和理论 - 解读数据及数据分析结果的实用知识 - 无数学细节和编码内容 目标受众： - 非数据科学家的管理人员，需要管理数据分析项目 - 希望引入数据驱动管理的管理人员 - 数据科学初学者 课程内容涵盖以下主题： - 数据素养与DIKW框架 - 数据驱动决策 - 探索性数据分析：概率论，描述性统计 - 数据预处理 - 数据可视化 - 诊断分析：假设检验（理论与方法） - 预测分析：机器学习，深度学习 在最后两章中，您将获得人工神经网络的基本且重要的理解。希望您能够享受本课程的学习过程。
Welcome to this course, data science for everyone. In this series of lectures, I will provide you with essentials of data science.This course is targeted for managers who are not data scientist but need to manage data analytic projects. It is also targeted for managers who want to introduce data-driven management. So, the knowledge provided in this course is both theoretical and pragmatic, but not includes details of mathematics and coding. However, anyone who are beginners in data science are also welcome because this course can provide you with essentials for learning technical aspects of data science.You will learn: - Essentials concepts and theories for learning technical aspects of data science.- Pragmatic knowledge for interpreting data and results of data analytics.- Not includes mathematics details and coding.Target Audience:- Managers who are not data scientist but need to manage data analytic projects.- Managers who want to introduce data-driven management.- Anyone who are beginners in data scienceThis course covers the following topics. As you can see, the contents include fundamental concepts of data science, and basics of descriptive, diagnostic, and predictive analytics. This course also covers the very basics of deep learning. In the final two chapters, you can gain a basic but essential and robust understanding of artificial neural networks.I hope you enjoy this course.Contents:- Data Literacy and DIKW- Data-Driven Decision Making- Exploratory Data Analysis: Probability theory, Descriptive Statistics- Data Preprocessing- Data Visualization- Diagnostic Analytics: Hypothesis Testing (Theory and Methods)- Predictive Analytics: Machine Learning, Deep Learning
