How Intelligent Automation Is Reshaping the Future of Digital Business Operations
Introduction As organizations accelerate their digital transformation journey, Intelligent Automation...Read More
Master the fundamentals and prepare to
explore the intricacies
This course will present data mining concepts and techniques. Students will
understand the importance of data mining in today’s data explosive business
environment. The course will cover the different data mining methods, models,
and tools. In addition real-world data mining applications in various domains will
be covered and the latest data mining tools will be used on real-life data sets to
illustrate the course concepts.
1: Identify appropriate data mining algorithms to solve real world problems
2: Compare and evaluate different data mining techniques like classification,
prediction, clustering, and association rule mining
3: Identify machine learning techniques suitable for a given problem
4: Apply dimensionality reduction techniques and evaluate the effectiveness
of the machine learning algorithms.
- Cross Industry Standard Process for Data Mining (CRISP) - Data Understanding (Types)
Data Preparation (Cleaning & Transformation)
Data Mining Application (1) (Data Preparation and Cleaning)
Data Mining Application (2) (Exploratory Analysis)
Data Mining Models (overview) - Linear Regression
Logistic Regression - Time Series Analysis and Forecasting (1)
Time Series Analysis and Forecasting (2) Midterm Exam Period
Classification Models: Decision Trees
Unsupervised Data Mining Models: Association
Unsupervised Data Mining Models: Clustering
Text Mining
Responsible Data Mining
Projects Presentations
Finalizing Course for Final Exam (review Week)
— Kader Ali
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