Data Mining

Classes are focused on teaching students advanced data mining and text mining techniques, including advanced methods of big data analysis. As a result of dynamic development of this interdisciplinary research a lot of effective algorithms were developed allowing automatically discovery of knowledge from databases, including unstructured data available in internet. This knowledge may be used in decision processes in business (including enterprises), science and technology.

The basic goal of these classes is to show students the theoretical and practical knowledge from advanced data mining techniques. During the classes theoretical basis of data mining techniques are presented as well as methods of verification of selected models. Also practical aspects of the applications of such methods in business are discussed. In laboratories students are able to realise the full process of data mining, starting with data preparation, variable selection, modelling, and ending with practical evaluation of results. In addition, unstructured data sets are analysed with text mining techniques. This subject is part of the Certificate: Data Scientist with SAS on master studies: Data Analysis - Big Data.

Literatura podstawowa:

Géron Aurélien (2018), Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Chollet, Allaire (2017), Deep Learning with Python, Manning Publications

Zheng Alice (2018), Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, O?Reilly

D.T.Larose, Discovering Knowledge in Data: An Introduction to Data Mining, Wiley, New York 2005;

D.T.Larose, Data Mining Methods and Models, Wiley, New York 2006;

I.Goodfellow, Y.Bengio, A.Courville, (2016). Deep learning (Vol. 1, p. 2). Cambridge: MIT press;

I.H.Witten, H.Ian, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition, Morgan Kaufmann, New York 2005;

R.Matignon, Data Mining Using SAS Enterprise Miner, Wiley, Hoboken, NJ, 2007;

R. Feldman, J. Sanger, The text mining handbook, Cambridge;

M. W. Berry, J. Kogan, Text Mining: Applications and Theory, Wiley;

S.M. Weiss, N. Indurkhya, T. Zhang, F. Damerau, Text Mining: Predictive Methods for Analyzing Unstructured Information, Springer;

S. Haykin, Neural Networks and Learning Machines, Pearson, New Jersey 2009;

F. Provost, T. Fawcett, Data Science for Business: What you need to know about data mining and data-analytic thinking, O?Reilly, USA, 2013;

P. Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge University Press, Cambridge, 2012;

N. Japkowicz, M. Shah, Evaluating Learning Algorithms: A Classification Perspective, Cambridge University Press, New York, USA, 2011.

Literatura uzupełniająca:

K.J.Cios, W.Pedrycz, R.W.Swiniarski, L.A.Kurgan, Data Mining: A Knowledge Discovery Approach, Springer Science, Business Media, New York, 2007;

Z.Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992.

Kryteria oceniania:

egzamin testowy: 50.00%

projekty: 50.00%

Classes are focused on teaching students advanced data mining and text mining techniques, including advanced methods of big data analysis. As a result of dynamic development of this interdisciplinary research a lot of effective algorithms were developed allowing automatically discovery of knowledge from databases, including unstructured data available in internet. This knowledge may be used in decision processes in business (including enterprises), science and technology.

The basic goal of these classes is to show students the theoretical and practical knowledge from advanced data mining techniques. During the classes theoretical basis of data mining techniques are presented as well as methods of verification of selected models. Also practical aspects of the applications of such methods in business are discussed. In laboratories students are able to realise the full process of data mining, starting with data preparation, variable selection, modelling, and ending with practical evaluation of results. In addition, unstructured data sets are analysed with text mining techniques. This subject is part of the Certificate: Data Scientist with SAS on master studies: Data Analysis - Big Data.

Literatura podstawowa:

Géron Aurélien (2018), Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Chollet, Allaire (2017), Deep Learning with Python, Manning Publications

Zheng Alice (2018), Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, O?Reilly

D.T.Larose, Discovering Knowledge in Data: An Introduction to Data Mining, Wiley, New York 2005;

D.T.Larose, Data Mining Methods and Models, Wiley, New York 2006;

I.Goodfellow, Y.Bengio, A.Courville, (2016). Deep learning (Vol. 1, p. 2). Cambridge: MIT press;

I.H.Witten, H.Ian, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition, Morgan Kaufmann, New York 2005;

R.Matignon, Data Mining Using SAS Enterprise Miner, Wiley, Hoboken, NJ, 2007;

R. Feldman, J. Sanger, The text mining handbook, Cambridge;

M. W. Berry, J. Kogan, Text Mining: Applications and Theory, Wiley;

S.M. Weiss, N. Indurkhya, T. Zhang, F. Damerau, Text Mining: Predictive Methods for Analyzing Unstructured Information, Springer;

S. Haykin, Neural Networks and Learning Machines, Pearson, New Jersey 2009;

F. Provost, T. Fawcett, Data Science for Business: What you need to know about data mining and data-analytic thinking, O?Reilly, USA, 2013;

P. Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge University Press, Cambridge, 2012;

N. Japkowicz, M. Shah, Evaluating Learning Algorithms: A Classification Perspective, Cambridge University Press, New York, USA, 2011.

Literatura uzupełniająca:

K.J.Cios, W.Pedrycz, R.W.Swiniarski, L.A.Kurgan, Data Mining: A Knowledge Discovery Approach, Springer Science, Business Media, New York, 2007;

Z.Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992.

Kryteria oceniania:

egzamin testowy: 50.00%

projekty: 50.00%

Classes are focused on teaching students advanced data mining and text mining techniques, including advanced methods of big data analysis. As a result of dynamic development of this interdisciplinary research a lot of effective algorithms were developed allowing automatically discovery of knowledge from databases, including unstructured data available in internet. This knowledge may be used in decision processes in business (including enterprises), science and technology.

The basic goal of these classes is to show students the theoretical and practical knowledge from advanced data mining techniques. During the classes theoretical basis of data mining techniques are presented as well as methods of verification of selected models. Also practical aspects of the applications of such methods in business are discussed. In laboratories students are able to realise the full process of data mining, starting with data preparation, variable selection, modelling, and ending with practical evaluation of results. In addition, unstructured data sets are analysed with text mining techniques. This subject is part of the Certificate: Data Scientist with SAS on master studies: Data Analysis - Big Data.

Literatura podstawowa:

D.T.Larose, Discovering Knowledge in Data: An Introduction to Data Mining, Wiley, New York 2005;

D.T.Larose, Data Mining Methods and Models, Wiley, New York 2006;

J.Koronacki, J.Ćwik, Statystyczne systemy uczące się, WN-T, Warszawa 2005;

I.Goodfellow, Y.Bengio, A.Courville, (2016). Deep learning (Vol. 1, p. 2). Cambridge: MIT press;

I.H.Witten, H.Ian, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition, Morgan Kaufmann, New York 2005;

R.Matignon, Data Mining Using SAS Enterprise Miner, Wiley, Hoboken, NJ, 2007;

R. Feldman, J. Sanger, The text mining handbook, Cambridge;

M. W. Berry, J. Kogan, Text Mining: Applications and Theory, Wiley;

S.M. Weiss, N. Indurkhya, T. Zhang, F. Damerau, Text Mining: Predictive Methods for Analyzing Unstructured Information, Springer;

S. Haykin, Neural Networks and Learning Machines, Pearson, New Jersey 2009;

F. Provost, T. Fawcett, Data Science for Business: What you need to know about data mining and data-analytic thinking, O?Reilly, USA, 2013;

P. Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge University Press, Cambridge, 2012;

N. Japkowicz, M. Shah, Evaluating Learning Algorithms: A Classification Perspective, Cambridge University Press, New York, USA, 2011.

Literatura uzupełniająca:

K.J.Cios, W.Pedrycz, R.W.Swiniarski, L.A.Kurgan, Data Mining: A Knowledge Discovery Approach, Springer Science, Business Media, New York, 2007;

Z.Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992.

Lasek, M., & Pęczkowski, M. (2013). Enterprise Miner: wykorzystywanie narzędzi Data Mining w systemie SAS. Wydawnictwa Uniwersytetu Warszawskiego.

Kryteria oceniania:

egzamin testowy: 50.00%

referaty/eseje: 50.00%

Classes are focused on teaching students advanced data mining and text mining techniques, including advanced methods of big data analysis. As a result of dynamic development of this interdisciplinary research a lot of effective algorithms were developed allowing automatically discovery of knowledge from databases, including unstructured data available in internet. This knowledge may be used in decision processes in business (including enterprises), science and technology.

The basic goal of these classes is to show students the theoretical and practical knowledge from advanced data mining techniques. During the classes theoretical basis of data mining techniques are presented as well as methods of verification of selected models. Also practical aspects of the applications of such methods in business are discussed. In laboratories students are able to realise the full process of data mining, starting with data preparation, variable selection, modelling, and ending with practical evaluation of results. In addition, unstructured data sets are analysed with text mining techniques. This subject is part of the Certificate: Data Scientist with SAS on master studies: Data Analysis - Big Data.

Literatura podstawowa:

D.T.Larose, Discovering Knowledge in Data: An Introduction to Data Mining, Wiley, New York 2005;

D.T.Larose, Data Mining Methods and Models, Wiley, New York 2006;

J.Koronacki, J.Ćwik, Statystyczne systemy uczące się, WN-T, Warszawa 2005;

I.Goodfellow, Y.Bengio, A.Courville, (2016). Deep learning (Vol. 1, p. 2). Cambridge: MIT press;

I.H.Witten, H.Ian, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition, Morgan Kaufmann, New York 2005;

R.Matignon, Data Mining Using SAS Enterprise Miner, Wiley, Hoboken, NJ, 2007;

R. Feldman, J. Sanger, The text mining handbook, Cambridge;

M. W. Berry, J. Kogan, Text Mining: Applications and Theory, Wiley;

S.M. Weiss, N. Indurkhya, T. Zhang, F. Damerau, Text Mining: Predictive Methods for Analyzing Unstructured Information, Springer;

S. Haykin, Neural Networks and Learning Machines, Pearson, New Jersey 2009;

F. Provost, T. Fawcett, Data Science for Business: What you need to know about data mining and data-analytic thinking, O?Reilly, USA, 2013;

P. Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge University Press, Cambridge, 2012;

N. Japkowicz, M. Shah, Evaluating Learning Algorithms: A Classification Perspective, Cambridge University Press, New York, USA, 2011.

Literatura uzupełniająca:

K.J.Cios, W.Pedrycz, R.W.Swiniarski, L.A.Kurgan, Data Mining: A Knowledge Discovery Approach, Springer Science, Business Media, New York, 2007;

Z.Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992.

Lasek, M., & Pęczkowski, M. (2013). Enterprise Miner: wykorzystywanie narzędzi Data Mining w systemie SAS. Wydawnictwa Uniwersytetu Warszawskiego.

Kryteria oceniania:

egzamin testowy: 50.00%

referaty/eseje: 50.00%