Data Mining and the Analytics Workflow

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.