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 (author Barton Poulson).
Learn how to use Microsoft Excel to perform basic data mining and analysis. Core data-mining concepts is introduced and the algorithms Microsoft offers for data mining right out of the box (author Ron Davis).
By the end of the course, you will have the knowledge you need to perform basic data analysis and reporting, and unlock opportunities to accelerate your career in this exciting field (author Lavanya Vijayan).
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization.
This course introduces practical data mining through the use of a powerful tool called Weka.
Weka is a collection of machine learning algorithms for data mining tasks and contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications.
Six modules - Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.