Join Barton Poulson for an in-depth discussion in this video Exercise files, part of Data Science Foundations: Data Mining.
- [Instructor] If you have access to the exercise files in this course, you can download them to your desktop as I've done here. When you click on this, you'll see that there's a separate folder for each of the chapters that has exercises. And within each of the folders, you'll find that script, and you'll also find any data that's used for that script. For Python, I'm using IPython Notebooks. You'll need to have that ready. And then for the last two, I use a variety of different programs, including Orange, and RapidMinner, as well as Knime, and BigML, and even Bash.
If you don't have access to the exercise files, that's okay. You can still follow along by watching how I use the files.
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
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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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