Data Mining in Python

Data Mining in Python

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

Instructor: Qiaozhu Mei

Access provided by PUCP

3,555 already enrolled

4 modules

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

4 modules

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

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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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.

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 videos Total 76 minutes

Welcome to Data Mining in Python 3 minutes
What is Data Mining 14 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 Matrices 7 minutes
Representing Sequences 4 minutes
Representing Time-Series and Spatial/Temporal Data 9 minutes
Representing Graph Data 5 minutes
Representing Stream Data 5 minutes
Data Mining Based on Patterns 5 minutes
Data Mining Based on Similarities 7 minutes

9 readings Total 85 minutes

MADSwPY Certificate Roadmap 5 minutes
Course Syllabus 10 minutes
Help Us Learn About You 10 minutes
Introduction to the Basic Functionalities of Data Mining 10 minutes
Introduction to Basic Data Representations 10 minutes
Case Study: Representations of Real-World Text Data 10 minutes
Introduction to Patterns and Similarities 10 minutes
Introduction to Module 1 Programming Assignment: Visualizing Different Data 10 minutes
Module 1 Optional Readings & Resources 10 minutes

4 assignments Total 65 minutes

Module 1 Quiz: Introduction to Data Mining 20 minutes
Knowledge Check: Basic Functionalities of Data Mining 15 minutes
Knowledge Check: Basic Data Representations (Part 1) 15 minutes
Knowledge Check: Basic Data Representations (Part 2) 15 minutes

1 programming assignment Total 180 minutes

Module 1 Programming Assignment: Warming Up 180 minutes

1 discussion prompt Total 15 minutes

Meet Your Fellow Learners 15 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 videos Total 61 minutes

Frequent Itemsets 9 minutes
Counting Strategies 3 minutes
The Apriori Algorithm 7 minutes
From Patterns to Association Rules 7 minutes
Measuring Correlations Using Lift 8 minutes
Mutual Information 12 minutes
Limitation of Correlation Measures 4 minutes
The Jaccard Similarity 10 minutes

5 readings Total 50 minutes

Introduction to Itemsets Representation 10 minutes
Introduction to Module 2 Programming Assignment: Dealing with Itemset Real-World Data 10 minutes
Additional Interestingness Measures 10 minutes
Introduction to Itemset Similarity 10 minutes
Module 2 Optional Readings & Resources 10 minutes

5 assignments Total 150 minutes

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

3 programming assignments Total 540 minutes

Module 2 Programming Assignment : Part 1 180 minutes
Module 2 Programming Assignment: Part 2 180 minutes
Module 2 Programming Assignment: Part 3 180 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 videos Total 79 minutes

From Itemsets to Vectors 5 minutes
Vectors and Matrices 6 minutes
The “Vector Space” 6 minutes
Vector Similarity Functions and Dot Product 11 minutes
Manhattan Distance and Euclidean Distance 7 minutes
Cosine Similarity 4 minutes
Pearson Correlation Coefficient 8 minutes
Applications of Vector Similarity 5 minutes
Eigenvectors 7 minutes
Eigendecomposition 5 minutes
Transforming the Coordinate System 14 minutes

3 readings Total 30 minutes

Introduction to Module 3 Programming Assignment: Dealing with Vector and Matrix Real-World Data 10 minutes
Dimensionality Reduction 10 minutes
Module 3 Optional Readings & Resources 10 minutes

6 assignments Total 180 minutes

Module 3 Quiz: Mining Vector and Matrix Data 30 minutes
Knowledge Check: Vector Representation of Data 30 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 assignments Total 720 minutes

Module 3 Programming Assignment: Part 1 180 minutes
Module 3 Programming Assignment: Part 2 180 minutes
Module 3 Programming Assignment: Part 3 180 minutes
Module 3 Programming Assignment: Part 4 180 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 videos Total 99 minutes

Representing Data as Sequences 5 minutes
Subsequences 11 minutes
Functionalities of Sequence Data 5 minutes
Frequent Sequential Patterns 9 minutes
Ngrams and Skipgrams 14 minutes
Sequence Similarity Basics 8 minutes
Edit Distance (Part 1) 20 minutes
Edit Distance (Part 2) 16 minutes
Shingling: Transform Sequences into Itemsets 8 minutes
Course Summary 3 minutes

3 readings Total 30 minutes

Sequential Patterns in Text Data 10 minutes
Introduction to Module 4 Programming Assignment: Dealing with Sequences Real-World Data 10 minutes
Module 4 Optional Readings & Resources 10 minutes