Join Barton Poulson for an in-depth discussion in this video Who should watch this course, part of Data Science Foundations: Data Mining.
- [Instructor] This course is designed for anyone who needs to or wants to make sense of masses of data. If you're in marketing, medical research, policy analysis, information science, or data science, or if you just wanna make sense out of the data deluge, then this is a great course for you. If you have an interest in finding the hidden structure of data so you can get new insights, find new audiences, or get the competitive edge, then data mining is for you. We use a number of tools in this course, such as R, Python, RapidMiner, and a few others.
And it's helpful if you have some experience with those, especially R and Python. That allows us to jump right into the data mining with them. It's also helpful if you have some familiarity with data mining concepts like association analysis and clustering, so you understand what they're supposed to do and how they work. That allows us to jump straight into the hands-on demonstrations and allows you to get the greatest benefit out of this course as soon as possible.
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
Transitioning from Data Warehousing to Big Datawith Alan Simon1h 50m Intermediate
Big Data Foundations: Program Managementwith 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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