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!

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!

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!

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!