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 (8)
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
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
I enjoyed the good real world examples, reviews of existing reports.
Ronald Parrish
Course - Data Visualization
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
Course - Data Mining & Machine Learning with R
Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra
Marija Hornis Dmitrovic - Marija Hornis
Course - Data Science for Big Data Analytics
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