Join Barton Poulson for an in-depth discussion in this video Anomaly detection in Python, part of Data Science Foundations: Data Mining.
- [Narrator] When we open up the…IPython Notebook in Jupyter, the first thing…we're going to do, as with the others,…is install packages.…Again, you may have them installed already.…Doesn't hurt to do this, it just checks,…installs them if needed, and we're good there.…But even if you already have them installed,…you still need to import them, which…we're going to do right here, along with a…parameter that allows us to change…font size for this particular example.…The red warning here is that…Matplotlib is taking a moment to build the font cache.…It's probably done by now.…We're going to load the data.…
If you're using a Macintosh, and…you have it saved on the desktop like I do,…you can run this first command and…save the data into a object called df.…If you're using Windows, use this path, but…change the part that says bart to your own username.…And then, either one you use…is going to create the data frame in df,…and let's take a look at the first few lines.…What we have here are states, and…then we have their Google searches, and…
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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