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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-for-supply-chain-fundamentals
课程评论：没有评论
课程名称：供应链机器学习基础 概述：本课程将教您如何利用Python的强大功能来理解复杂的供应链数据集。即使您对供应链基础知识不熟悉，我们将使用的丰富数据集将帮助您掌握多种Python工具和最佳实践，以进行探索性数据分析（EDA）。因此，虽然所有数据集都是针对供应链专业人士的，但课程内容同样可以推广到其他使用场景中。 课程大纲： 1. **编程概念与Python实践介绍** - 描述：欢迎参加本课程！在这一模块中，我们将学习编程的基础知识和Python语言。我们将从基本数据结构、函数和循环开始，然后熟悉模块和库的导入。最后，我们将通过使用线性规划技术优化一个供应约束问题来检验我们的新技能。 2. **深入数据：数据科学的常用工具** - 描述：在下一个模块中，我们将深入了解数据科学中最常用的工具：Python和Numpy。我们将首先学习Numpy，习惯使用np数组及其主要功能。在熟悉加载各种类型的数据后，我们将学习一些基本的数据描述和清洗技术。同时，我们还将学习如何处理Dataframe中的索引和列。最后，我们将介绍绘图和汇总统计，并以常见的供应链数据集为基础进行探索。 3. **高级数据处理与操作** - 描述：在这个模块中，我们将把我们的Pandas和Numpy技能提升到一个新水平，学习如何有效地组合和重塑数据。我们将学习如何通过合并和透视数据来调整数据以满足我们的需求。这为我们解决机器学习算法所需的常见数据预处理步骤（如独热编码）奠定基础。最后，我们将接触到Pandas中最重要的工具（Groupby-Apply-Transform）并探讨其变革性功能。 4. **课程最终项目** - 描述：在这个最终项目中，我们将利用多个数据集，涉及仓库容量、产品需求和货运费用，来优化产品生产和运输成本。 通过本课程的学习，您将能够掌握数据分析的核心技能，并将其应用于实际的供应链问题中。
Name:Introduction to Programming Concepts and Python Practices
Description:Welcome to the course! In this first module, we’ll learn about the fundamentals of programming and Python. We’ll start with basic data structures, functions, and loops and then some time becoming familiar with importing modules and libraries. Finally, we'll put our new skills to the test by optimizing a supply constraint problem using linear programming techniques.
Name:Digging Into Data: Common Tools for Data Science
Description:In this next module, we'll dive into the most common tools used for data science: Python, and Numpy. We'll start with Numpy, getting used to np arrays and their main functionality. After getting familiar with loading in data of all types, we'll learn about some basic data description and cleaning techniques. We'll also learn to work with indexes and columns in Dataframes. We'll end with an introduction to plotting and summary statistics. We will use common supply chain data sets for our explorations
Name:Higher Level Data Wrangling and Manipulation
Description:In this third module, we'll take our Pandas and Numpy skills to the next level, learning how to effectively combine and reshape data. We'll learn how to reshape data to fit with our needs through merges and pivots. This setup will help us tackle common data preprocessing steps necessary to run machine learning algorithms, such as one-hot encoding. Finally, we'll encounter the most important tools in our Pandas arsenal (Groupby-Apply-Transform) and explore its transformative functionality.
Name:Course 1 Final Project
Description:In this final project, we'll take collection of various data sets involving warehouse capacities, product demand, and freight rates to optimize cost of producing and shipping products.
This course will teach you how to leverage the power of Python to understand complicated supply chain datasets. Even if you are not familiar with supply chain fundamentals, the rich data sets that we will use as a canvas will help orient you with several Pythonic tools and best practices for exploratory data analysis (EDA). As such, though all datasets are geared towards supply chain minded professionals, the lessons are easily generalizable to other use cases.
