At the end of this course, the students; 1) will be able to explain the theory of the basic concepts of data mining 2) will be able to prepare the data warehouse from operational database(s) 3) will be able to explain the data mining tasks 4) will be able to explain the components of data mining 5) will be able to explain the relationship between decision processes and business intelligence 6) will be able to differentiate data mining methods from the other analytical methods and business intelligence solutions 7) will be able to prepare data mining projects according to the CRISP-DM and the other data mining methodologies 8) will be able to identify the proper data mining method according to the data and the goals 9) will be able to explain the statistical infrastructure of data mining 10) will be able to apply Linear Regression Analysis, Logistic Regression Analysis, K-nearest neighbourhood, K-means Clustering, Hierarchical Clustering, Decision Trees and Association Rules methods via data mining tools and softwares. Ability to use (freeware) data mining softwares 11) will be able to apply and manage non-complicated data mining projects 12) will be able to apply and manage non-complicated data mining projects
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
This course explores the definition of data mining, survey of data mining applications, techniques and models, Data mining steps: define goal, data cleaning, data selection and preprocessing; data reduction and data transformation, select data mining algorithm, model assessment, interpretation, and exploration of data mining algorithms: decision trees, clustering, memory based methods, artificial neural networks.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Introduction to data mining
2nd Week
Data mining tasks
3rd Week
Data mining tasks
4th Week
Components of data mining
5th Week
Components of data mining
6th Week
Comparison of data mining and the other analytical methods
7th Week
Comparison of data mining and the other analytical methods
8th Week
Midterm
9th Week
Decision processes, business intelligence and data mining
10th Week
Methodology of data mining
11th Week
Classification of data mining methods
12th Week
Statistical infrastructure of data mining
13th Week
Data mining methods and their applications
14th Week
Data mining methods and their applications
RECOMENDED OR REQUIRED READING
Hand, David, Mannila, Heikki and Smyth, Padhraic. Principles of Data Mining. MIT Press, London, 2001.
Hastie, Trevor, Tibshirani, Robert and Friedman, Jerome. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second Edition). Springer Series in Statistics, 2009.
Witten, Ian H. and Frank, Eibe. Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), The Morgan Kaufmann Series in Data Management Systems), 2005. Anlatım,Sorun/Problem Çözme,Eğitim-Uygulama,Sunum,Deney
