Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.
- Prerequisites for data mining
- Data mining using R, Python, Orange, and RapidMiner
- Data reduction
- Data clustering
- Anomaly detection
- Association analysis
- Regression analysis
- Sequence mining
- Text mining
Skill Level Beginner
- [Instructor] Data is all around us, and it's increasing at an explosive rate. The challenge is how to make sense of this overwhelming resource and get meaningful, usable information out of it. Data mining can help you do just that. I'm Barton Poulson, and I've been teaching people how to work productively with data, online and in person, for over 20 years. Without a doubt though, the last few years and the progress in data mining have made it the most exciting of all. In this course, I'm going to give you a map of the field of data mining.
We'll explore some of the important principles that make data mining what it is, and how it can be applied. More importantly, we'll get hands-on work with some of the most important techniques in data mining, such as text mining, clustering, classification and association analysis, using some of the most critical tools, such as R, Python, RapidMiner, and others. In addition, I'll give you opportunities to work on each of these tasks and with each of these programs and languages, so you can get an insider view on data mining. I'm excited to introduce you to data mining and to help you start making sense of the data deluge, so let's get started.
Transitioning from Data Warehousing to Big Datawith Alan Simon1h 50m Intermediate
Manage Your Organization's Big Data Programwith Alan Simon1h 11m Intermediate
2. Data Reduction
5. Anomaly Detection
6. Association Analysis
7. Regression Analysis
8. Sequential Patterns
9. Text Mining
Next steps1m 18s
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