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
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Data Mining in Python
This course is part of More Applied Data Science with Python Specialization
Instructor: Qiaozhu Mei
Included with 
Gain insight into a topic and learn the fundamentals.
Advanced level
Recommended experience
5 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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.
Skills you'll gain
Details to know
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Recently updated!
June 2025
Assessments
20 assignments
Taught in English
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This course is part of the More Applied Data Science with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
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!
What's included
12 videos9 readings4 assignments1 programming assignment1 discussion prompt1 plugin
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!
What's included
8 videos5 readings5 assignments3 programming assignments
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!
What's included
11 videos3 readings6 assignments4 programming assignments
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!
What's included
10 videos3 readings5 assignments4 programming assignments1 plugin
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University of Michigan
University of Michigan
University of Michigan
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