Data Analysis  046193





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 NonParametrics 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.
TextbooksPublished  Publisher  Authors  Book 

2015   c aggrawal  data mining the textbook, springer 
2011  morgan kaufmann  i.h wittens e. frank and m.a. hall.  data mining practical machine learning tools and techniques, 3rd 
2011  morgan kaufman  j. han, m kamber and j. pei,  data mining concepts and techniques 
2011  cambridge  n. japkowicz and m. shah,  evaluating learning algorithms, 
2004  springer  l. wasserman  all of statistics 
Created in 20/02/2018 Time 17:26:38