Data Mining 101 - A Practitioner's Course
BI and Analytics Managers, Business & Data Analyst (IT and non-IT), Data Analyst, Database Administrators, Project Leaders, Systems Analyst
Data mining is the process of discovering interesting knowledge from large amounts of data. It is an interdisciplinary field with contributions from many areas, such as statistics, machine learning, information retrieval, pattern recognition and bioinformatics. Data mining is widely used in many domains, such as retail, finance, telecommunication and social media.
This course provides an overview of various data mining techniques with examples of how they are used in various organizations such as retail, finance, biotechnology and social media. Case studies are used to allow participants to work through several data mining issues using the techniques described and to recognize opportunities to apply data mining within their organization.
Note: This course uses a visually oriented, open source software package to process the data. The class is not intended to be a programming class. Instead, the software is used to examine the impact of different data mining decisions.
· Identify the data mining options available to solve the business question.
· Plan for common data challenges.
· Apply data mining techniques relevant to the business question.
This course will be accessible to students without prior training in quantitative research methods. However, students with a background in basic descriptive and inferential statistics will, most likely, get more out of the course.
Introduction to Data Mining
- Descriptive and Predictive
- Models and Algorithms
- Regression vs. Classification
- Supervised/Unsupervised Learning
- Integrating Data Sets
- Data Reduction
- Inconsistencies, Missing Data & Outliers
Data Mining Methods
- Association Rules
- Decision Trees
- Neural Networks
- Text Mining
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