Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and
forecasting, and generally improve their performance through interaction with data. It is currently regarded as the key element of a more general process called
Knowledge Discovery that deals with extracting useful knowledge from raw data.
The knowledge discovery process includes data selection, cleaning, coding, using
different statistical and machine learning techniques, and visualization of the generated structures. The course will cover all these issues and will illustrate the whole
process by examples. Special emphasis will be give to the Machine Learning methods as they provide the real knowledge discovery tools. Important related
technologies, as data warehousing and on-line analytical processing (OLAP) will be also discussed. The students will use recent Data Mining software.
Very rarely is data easily accessible in a data science project. It’s more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. In these cases, the first step is to import the data and tidy the data using a software program. The steps that convert data from its raw form to the tidy form is called data wrangling.
This process is a critical step for any data mining person or scientist. Knowing how to wrangle and clean data will enable you to make critical insights that would otherwise be hidden.
| Required reading: | Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, 2005, ISBN: 0-12-088407-0. |
| Weka 3 : | Data Mining System with Free Open Source Machine Learning Software in Java. Available at http://www.cs.waikato.ac.nz/~ml/weka/index.html |
Instructors
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FAQs
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Requirements
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Features
- To introduce students to the basic concepts and techniques of Data Mining.
- To develop skills of using recent data mining software for solving practical problems.
- To gain experience of doing independent study and research.
- Apply the concepts and techniques of Data mining on data sets
- Preprocess and clean data for use in data mining
- Discover interesting patterns from large amounts of data
- Learn to process and convert raw data into formats needed for analysis.
- Web scraping
- Importing data into one format from different file formats
- Learn how to tidy data to better facilitate analysis
- String processing with regular expressions (regex)
- How to work with dates and times as file formats, and text mining
Target audiences
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