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Data Mining in Python

Data Mining in Python

本课程是 More Applied Data Science with Python 专项课程的一部分

位教师：Qiaozhu Mei

4,286 人已注册

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4个模块

深入了解一个主题并学习基础知识。

11 条评论

高级设置等级

推荐体验

5 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

11 条评论

高级设置等级

推荐体验

5 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

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.

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您将学习的工具

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）

91% of learners achieved a positive career outcome

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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

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.

You will learn how to formally describe real-world information with general data representations (e.g., itemsets, vectors, matrices, sequences, and more). You will then learn how to formulate data in the wild with one or more of these representations. This course will teach you how to characterize and explain your data by looking for patterns and similarities, which are basic building blocks for advanced analysis and machine learning models. This is the first course in “More Applied Data Science with Python,” a four-course series focused on helping you apply advanced data science techniques using Python. It is recommended that all learners complete the Applied Data Science with Python specialization prior to beginning this course.

单元详情

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

12个视频总计76分钟

Welcome to Data Mining in Python3分钟
What is Data Mining14分钟
Data Mining Functionalities (Part 1)7分钟
Data Mining Functionalities (Part 2)7分钟
Data Mining Functionalities (Part 3)4分钟
Representing Itemsets, Vectors, and Matrices7分钟
Representing Sequences4分钟
Representing Time-Series and Spatial/Temporal Data9分钟
Representing Graph Data5分钟
Representing Stream Data5分钟
Data Mining Based on Patterns5分钟
Data Mining Based on Similarities 7分钟

9篇阅读材料总计85分钟

MADSwPY Certificate Roadmap 5分钟
Course Syllabus10分钟
Help Us Learn About You10分钟
Introduction to the Basic Functionalities of Data Mining10分钟
Introduction to Basic Data Representations10分钟
Case Study: Representations of Real-World Text Data10分钟
Introduction to Patterns and Similarities10分钟
Introduction to Module 1 Programming Assignment: Visualizing Different Data10分钟
Module 1 Optional Readings & Resources10分钟

4个作业总计65分钟

Module 1 Quiz: Introduction to Data Mining20分钟
Knowledge Check: Basic Functionalities of Data Mining15分钟
Knowledge Check: Basic Data Representations (Part 1)15分钟
Knowledge Check: Basic Data Representations (Part 2)15分钟

1个编程作业总计180分钟

Module 1 Programming Assignment: Warming Up180分钟

1个讨论话题总计15分钟

Meet Your Fellow Learners15分钟

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

8个视频总计61分钟

Frequent Itemsets9分钟
Counting Strategies3分钟
The Apriori Algorithm7分钟
From Patterns to Association Rules7分钟
Measuring Correlations Using Lift8分钟
Mutual Information12分钟
Limitation of Correlation Measures4分钟
The Jaccard Similarity10分钟

5篇阅读材料总计50分钟

Introduction to Itemsets Representation10分钟
Introduction to Module 2 Programming Assignment: Dealing with Itemset Real-World Data10分钟
Additional Interestingness Measures10分钟
Introduction to Itemset Similarity10分钟
Module 2 Optional Readings & Resources10分钟

5个作业总计150分钟

Module 2 Quiz: Mining Itemset Data30分钟
Knowledge Check: Mining Frequent Itemsets30分钟
Knowledge Check: Evaluating Frequent Itemsets (Part 1)30分钟
Knowledge Check: Evaluating Frequent Itemsets (Part 2)30分钟
Knowledge Check: Similarity of Itemsets30分钟

3个编程作业总计540分钟

Module 2 Programming Assignment : Part 1180分钟
Module 2 Programming Assignment: Part 2180分钟
Module 2 Programming Assignment: Part 3180分钟

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

11个视频总计79分钟

From Itemsets to Vectors5分钟
Vectors and Matrices6分钟
The “Vector Space”6分钟
Vector Similarity Functions and Dot Product11分钟
Manhattan Distance and Euclidean Distance7分钟
Cosine Similarity4分钟
Pearson Correlation Coefficient8分钟
Applications of Vector Similarity5分钟
Eigenvectors7分钟
Eigendecomposition5分钟
Transforming the Coordinate System14分钟

3篇阅读材料总计30分钟

Introduction to Module 3 Programming Assignment: Dealing with Vector and Matrix Real-World Data10分钟
Dimensionality Reduction10分钟
Module 3 Optional Readings & Resources10分钟

6个作业总计180分钟

Module 3 Quiz: Mining Vector and Matrix Data30分钟
Knowledge Check: Vector Representation of Data30分钟
Knowledge Check: Similarity of Vectors (Part 1)30分钟
Knowledge Check: Similarity of Vectors (Part 2)30分钟
Knowledge Check: Patterns in Matrix Data (Part 1)30分钟
Knowledge Check: Patterns in Matrix Data (Part 2)30分钟

4个编程作业总计720分钟

Module 3 Programming Assignment: Part 1180分钟
Module 3 Programming Assignment: Part 2180分钟
Module 3 Programming Assignment: Part 3180分钟
Module 3 Programming Assignment: Part 4180分钟

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

10个视频总计99分钟

Representing Data as Sequences5分钟
Subsequences11分钟
Functionalities of Sequence Data5分钟
Frequent Sequential Patterns9分钟
Ngrams and Skipgrams14分钟
Sequence Similarity Basics8分钟
Edit Distance (Part 1)20分钟
Edit Distance (Part 2)16分钟
Shingling: Transform Sequences into Itemsets8分钟
Course Summary3分钟

3篇阅读材料总计30分钟

Sequential Patterns in Text Data10分钟
Introduction to Module 4 Programming Assignment: Dealing with Sequences Real-World Data10分钟
Module 4 Optional Readings & Resources10分钟