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.
Author Name: Janani Ravi
Author Description:
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. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  1min
• Understanding Data Mining
  30mins
• Using Data Mining to Find Patterns
  50mins
• Predictive Data Mining for Categorical Data
  32mins
• Predictive Data Mining for Continuous Data
  33mins
• Understanding and Implementing the CRISP-DM Methodology
  25mins