In “Data Mining in Python,” you will learn how to extract useful knowledge from large-scale datasets. This course introduces basic concepts and general tasks for data mining. You will explore a wide range of real-world data sets, including grocery store, restaurant reviews, business operations, social media posts, and more.
Data Mining in Python
您将学到什么
Understand basic concepts, tasks, and procedures of data mining.
Formulate real-world information using basic data representations: itemsets, vectors, matrices, sequences, time series, and networks.
Use data mining algorithms to extract patterns and similarities from real-world datasets.
Calculate the importance of patterns and prepare for downstream machine-learning tasks.
您将获得的技能
要了解的详细信息
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作业
20 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 More Applied Data Science with Python 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
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该课程共有4个模块
Welcome to Module 1—an Introduction to Data Mining! We will begin this module with an introduction to the basic concepts, views, and tasks of data mining. We will focus on how to formulate real world information as different data representations (e.g., itemsets, vectors, sequences, time series, networks, data streams, etc.). Then, we will elaborate on two basic functionalities of data mining: patterns and similarity. We will learn how they can be used to build more complex data mining tasks. Let’s get started!
涵盖的内容
12个视频9篇阅读材料4个作业1个编程作业1个讨论话题
Welcome to Module 2—Mining Itemset Data! In this module, we will learn how to represent data as itemsets and the basic data mining operations with itemset data. We will focus on how to extract frequent patterns from a collection of itemsets, how to evaluate the interestingness of itemset patterns, and how to compute Jaccard similarity between two itemsets. Let’s get started!
涵盖的内容
8个视频5篇阅读材料5个作业3个编程作业
Welcome to Module 3—Mining Vector and Matrix Data! We are halfway through our course on Data Mining! In this module, we will learn in how to mine data represented as vectors and matrices. We will focus on how to represent data as vectors, different similarity/distance metrics of vector data, what are the patterns in matrix data, and how to apply these concepts to real world scenarios. Let’s get started!
涵盖的内容
11个视频3篇阅读材料6个作业4个编程作业
Welcome to Module 4—Mining Sequences, our last course module!! We will conclude our course by learning how to represent data as sequences. We will focus on commonly used sequential patterns (ngrams and skipgrams), distance measures for sequence data (Edit Distance and Shingling), and how they can be applied to real world tasks. Let’s get started!
涵盖的内容
10个视频3篇阅读材料5个作业4个编程作业
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University of Michigan
University of Michigan
University of Michigan
