Data Science Foundations: Data Mining

Course details

All data science begins with good data. Data mining is a framework for collecting, searching, and filtering raw data in a systematic matter, ensuring you have clean data from the start. It also helps you parse large data sets, and get at the most meaningful, useful information. This course, Data Science Foundations: Data Mining, is designed to provide a solid point of entry to all the tools, techniques, and tactical thinking behind data mining.

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.

Topics include:
- 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

Concepts

Introduction

welcome
who should watch this course
exercise files

Preliminaries

data mining prerequisites
algorithm prerequisites
software prerequisites

Data Reduction

goals of data reduction
data for data reduction
data reduction in r
data reduction in python
data reduction in orange
data reduction in rapidminer

Clustering

clustering goals
clustering data
clustering in r
clustering in python
clustering in bigml
clustering in orange

Classification

classification goals
classification data
classification in r
classification in python
classification in rapidminer
classification in knime

Anomaly Detection

anomaly detection goals
anomaly detection data
anomaly detection in r
anomaly detection in python
anomaly detection in bigml
anomaly detection in rapidminer

Association Analysis

association analysis goals
association analysis data
association analysis in r
association analysis in python
association analysis in orange
association analysis in rapidminer

Regression Analysis

regression analysis goals
regression analysis data
regression analysis in r
regression analysis in python
regression analysis in knime
regression analysis in rapidminer

Sequential Patterns

sequence mining goals
sequence mining algorithms
sequence mining in r
sequence mining in python
sequence mining in bigml - part 1
sequence mining in bigml - part 2

Text Mining

text mining goals
text mining algorithms
text mining in r
text mining in python
text mining in rapidminer

Conclusion

next steps