Subject: Subject Sylbus: Data Analysis - 046193

Data Analysis - 046193
Credit
Points
3.0
 
Given In
Semester
a
 
  Lecture Exercise Laboratory Project or
Seminar
House
Work
Weekly
Hours
2 1      

Determination of the grade according to progress during the semester.


Prerequisites: ( Signals and Systems 044130
and Introduction to Probability H 104034 )


Of Statistical Inference
Parametric and Non-Parametrics Estimation, Hypothesis Testing. Data Preprocessing. Feature Selection. Dimensionality Reduction: Pca, Svd, Nonlinear Extensions. Distance and Similarity Measures. Clustering Algorithms. Frequency and Association Mining. Outlier Analysis. Representative Applications. Introduction to Data Mining Methods and Unsupervised

Learning Outcomes
Upon Completing the Course, Students Will Be Able to:
1. Explain the Basic Issues of Data Analysis.
2. Explain and Implement Statistical Methods for Parameter Estimation and Hypothesis Testing.
3. Explain and Implement Basic Approaches for Feature Selection.
4. Explain and Implement Algorithms for Data Dimensionality Reduction.
5. Explain and Implement Algorithms for Frequency and Correlation.
6. Explain and Implement Algorithms for Data Clustering.
7. Explain and Implement Algorithms for Outlier Detection.




System of hours to the semesters
Semester Previous Semester information 01/2017 2017/2018 Winter Semester


Textbooks
PublishedPublisherAuthorsBook
2015 c aggrawaldata mining the textbook, springer
2011morgan kaufmanni.h wittens e. frank and m.a. hall.data mining practical machine learning tools and techniques, 3rd
2011morgan kaufmanj. han, m kamber and j. pei,data mining concepts and techniques
2011cambridgen. japkowicz and m. shah,evaluating learning algorithms,
2004springerl. wassermanall of statistics

Created in 20/02/2018 Time 17:26:38