Data Mining and the Analytics Workflow

This course explains the continuing relevance of Data Mining today, in the context of applying machine learning techniques to big data. It covers conceptual and practical details of powerful techniques such as Association Rules learning and the industry-standard CRISP-DM methodology for data mining workflows.
Course info
Level
Beginner
Updated
Oct 30, 2019
Duration
2h 54m
Table of contents
Course Overview
Understanding Data Mining
Using Data Mining to Find Patterns
Predictive Data Mining for Categorical Data
Predictive Data Mining for Continuous Data
Understanding and Implementing the CRISP-DM Methodology
Description
Course info
Level
Beginner
Updated
Oct 30, 2019
Duration
2h 54m
Description

Data Mining is an umbrella term used for techniques that find patterns in large datasets. Simply put, data mining is the application of machine learning techniques on big data. The popularity of the term Data Mining peaked some years ago, but in substance, data mining is perhaps more relevant today than it has ever been.

In this course, Data Mining and the Analytics Workflow you will gain the ability to formulate your use-case as a Data Mining problem, and then apply a classic process, the CRISP-DM methodology, to solve it.

First, you will learn how association rules learning works, and why it is considered a classic data mining application, predating the explosion in the popularity of ML. You will see the similarities and contrasts between association rules learning and recommender systems.

Next, you will discover how big data and machine learning both squarely lie within the ambit of data mining, even as more traditional data mining links to statistics and information retrieval continue to exist.

Finally, you will round out your knowledge by learning about an industry-standard process for building data mining applications, know as the CRISP-DM. This technique is about two decades old but has retained its relevance, and closely mirrors the classic machine learning workflow in wide use today.

When you’re finished with this course, you will have the skills and knowledge to design and implement the right data mining solution, one that applies machine learning on big data, for your use-case.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi. My name is Janani Ravi, and welcome to this course on Data Mining and the Analytics Workflow. A little about myself: I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. Data mining is an umbrella term used for techniques that find patterns in large datasets. Simply put, data mining is the application of machine learning techniques on big data. In this course you will gain the ability to formulate your use case as a data mining problem, and the apply a classic process, the CRISP-DM methodology to solve it. First, you will learn how association rules learning works and why it's considered a classic data mining application, predating the explosion in the popularity of machine learning. You'll see the similarity and contrast between association rules learning and recommender systems. Next, you will discover how big data and machine learning both squarely lie within the ambit of data mining, even as more traditional data mining links to statistics and information retrieval continue to exist. Finally, you'll round out your knowledge by learning about an industry standard process for building data mining applications, known as the CRISP-DM. This technique is about two decades old, but has retained its relevance and closely mirrors the classic machine learning workflow in wide use today. When you're finished with this course, you will have the skills and knowledge to design and implement the right data mining solution, one that applies machine learning on big data for your use case.