Join Barton Poulson for an in-depth discussion in this video Text mining goals, part of Data Science Foundations: Data Mining.
- [Narrator] Text Mining is one of the most important forms…of Data Mining that you could use…and what makes it really different is that…this time, we're using Unstructured data.…It's not numerical data that's in rows…and columns or in lists,…it's just a blob of text,…and there are a few different reasons…you might wanna do this.…Now as a researcher,…one of my favorite is Assessing Authorship in documents,…seeing who wrote it…or seeing for changes in voice over time.…Another one is Clustering Groups of Respondents.…Clustering's a very big topic…in a huge number of fields…and using natural text can be a great way…of getting more insight into clusters.…
Finally, another very common use…is Sentiment Analysis in Social Media…and in news articles…and in blog posts…and telling whether people are basically saying…positive or negative things about something.…Without having to read through all of it,…you can get some of this…very basic information with Text Mining.…Now let me say a little more…about each of these.…First one is Comparing Voices.…
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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