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Data Mining in Python

Data Mining in Python

This course is part of More Applied Data Science with Python Specialization

Instructor: Qiaozhu Mei

4,280 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

11 reviews

Advanced level

Recommended experience

5 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

11 reviews

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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.

Module details

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 prompt

12 videosTotal 76 minutes

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

9 readingsTotal 85 minutes

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

4 assignmentsTotal 65 minutes

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

1 programming assignmentTotal 180 minutes

Module 1 Programming Assignment: Warming Up180 minutes

1 discussion promptTotal 15 minutes

Meet Your Fellow Learners15 minutes

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

8 videosTotal 61 minutes

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

5 readingsTotal 50 minutes

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

5 assignmentsTotal 150 minutes

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

3 programming assignmentsTotal 540 minutes

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

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

11 videosTotal 79 minutes

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

3 readingsTotal 30 minutes

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

6 assignmentsTotal 180 minutes

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

4 programming assignmentsTotal 720 minutes

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

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 assignments

10 videosTotal 99 minutes

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

3 readingsTotal 30 minutes

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