Course Outline
Introduction
- Data mining as the analysis step of the KDD process ("Knowledge Discovery in Databases")
- Subfield of computer science
- Discovering patterns in large data sets
Sources of methods
- Artificial intelligence
- Machine learning
- Statistics
- Database systems
What is involved?
- Database and data management aspects
- Data pre-processing
- Model and inference considerations
- Interestingness metrics
- Complexity considerations
- Post-processing of discovered structures
- Visualization
- Online updating
Data mining main tasks
- Automatic or semi-automatic analysis of large quantities of data
- Extracting previously unknown interesting patterns
- groups of data records (cluster analysis)
- unusual records (anomaly detection)
- dependencies (association rule mining)
Data mining
- Anomaly detection (Outlier/change/deviation detection)
- Association rule learning (Dependency modeling)
- Clustering
- Classification
- Regression
- Summarization
Use and applications
- Able Danger
- Behavioral analytics
- Business analytics
- Cross Industry Standard Process for Data Mining
- Customer analytics
- Data mining in agriculture
- Data mining in meteorology
- Educational data mining
- Human genetic clustering
- Inference attack
- Java Data Mining
- Open-source intelligence
- Path analysis (computing)
- Reactive business intelligence
Data dredging, data fishing, data snooping
Requirements
Fair knowledge about relational data structures, SQL
Testimonials (9)
how the trainor shows his knowledge in the subject he's teachign
john ernesto ii fernandez - Philippine AXA Life Insurance Corporation
Course - Data Vault: Building a Scalable Data Warehouse
The content, as I found it very interesting and think it would help me in my final year at University.
Krishan - NBrown Group
Course - From Data to Decision with Big Data and Predictive Analytics
Very tailored to needs.
Yashan Wang
Course - Data Mining with R
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
Course - Data Mining & Machine Learning with R
I enjoyed the good real world examples, reviews of existing reports.
Ronald Parrish
Course - Data Visualization
Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra
Marija Hornis Dmitrovic - Marija Hornis
Course - Data Science for Big Data Analytics
I learned a lot - not only in theoretical knowledge but I also applied that knowledge during the training and therefore I really understood what process mining is and how it works. Thanks a lot!
Julia Dörre - Techniker Krankenkasse
Course - Process Mining
I feel more confident with coding now. I've never done it before but now I understand that it's not rocket science and I can do it when necessary.
Anna - Birmingham City University
Course - Foundation R
Very useful in because it helps me understand what we can do with the data in our context. It will also help me