Outras informações

  • Reforça-se neste momento que o Programa teve o seu início em junho de 2016 e, portanto, não possui ainda nenhum discente formado.
  • Apesar de ter iniciado suas atividades em 2016, o PPCIC obteve conceito BOM em todos as dimensões: Proposta do Programa; Corpo Docente; Corpo Discente; Produção Intelectual; e Inserção Social, o que demonstra o amadurecimento do Programa.
  • Conforme indicado no final da avaliação da quadrienal 2013-2016, a linha de “Métodos Baseados em Dados” foi renomeada para “Algoritmos, Otimização e Modelagem Computacional”, dando maior clareza e foco à linha.
  • Os projetos de pesquisa foram revistos para refletir da melhor forma possível as pesquisas realizadas no programa, formando-se uma organização de quatro projetos por linha de pesquisa.


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Francine Berman. Got Data?: A Guide to Data Preservation in the Information Age. Commun. ACM, 51(12):50–56, December 2008.

Christopher Bishop. Pattern Recognition and Machine Learning. Springer, New York, October 2007.

Christine L. Borgman. Big Data, Little Data, No Data: Scholarship in the Networked World. The MIT Press, Cambridge, Massachusetts, January 2015.

Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms. The MIT Press, Cambridge, Mass, 3rd edition, July 2009.

Peter Dalgaard. Introductory Statistics with R. Springer, New York, 2nd edition, August 2008.

Sanjoy Dasgupta, Christos Papadimitriou, and Umesh Vazirani. Algorithms. McGraw-Hill Education, Boston, 1 edition, September 2006.

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Vasant Dhar. Data science and prediction. Communications of the ACM, 56(12):64–73, 2013.
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Ramez Elmasri and Shamkant B. Navathe. Fundamentals of Database Systems. Pearson/Addison Wesley, Boston, 5th edition, March 2006.

Ronen Feldman and James Sanger. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 1 edition, December 2006.

Peter Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, Cambridge ; New York, 1 edition, November 2012.

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Hilary Glasman-Deal. Science Research Writing for Non-Native Speakers of English. Imperial College Press, London ; Hackensack, NJ, 1 edition, December 2009.

Georg Hager and Gerhard Wellein. Introduction to High Performance Computing for Scientists and Engineers. CRC Press, Boca Raton, FL, 1 edition, July 2010.

Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann, Waltham, Mass., 3 edition, July 2011.

Aboul-Ella Hassanien, Ahmad Taher Azar, V aclav Sn asel, Janusz Kacprzyk, and Jemal H. Abawajy, editors. Big Data in Complex Systems: Challenges and Opportunities. Springer, New York, 2015 edition, January 2015.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2nd ed. 2009. corr. 7th printing 2013 edition, April 2011.

Simon O. Haykin. Neural Networks and Learning Machines. Prentice Hall, New York, 3 edition, November 2008.

John L. Hennessy and David A. Patterson. Computer Architecture: A Quantitative Approach. Morgan Kaufmann Publishers, Waltham, MA, September 2011.

Tony Hey, Stewart Tansley, and Kristin Tolle, editors. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, Redmond , Washington, 1 edition, October 2009.

Adam Jacobs. The Pathologies of Big Data. Commun. ACM, 52(8):36–44, August 2009.

HV Jagadish, Johannes Gehrke, Alexandros Labrinidis, Yannis Papakonstantinou, Jignesh M Patel, Raghu Ramakrishnan, and Cyrus Shahabi. Big data and its technical challenges. Communications of the ACM, 57(7):86– 94, 2014.

Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning: with Applications in R. Springer, 1st ed. 2013. corr. 4th printing 2014 edition, August 2013.

Matthew L. Jockers. Text Analysis with R for Students of Literature. Springer, New York, July 2014.

Anne Kao and Steve R. Poteet. Natural Language Processing and Text Mining. Springer London, 1 edition,March 2007.

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Peter Lake and Robert Drake. Information Systems Management in the Big Data Era. Springer, New York, NY, 2014 edition, January 2015.

Brett Lantz. Machine Learning with R. Packt Publishing, Birmingham, October 2013.

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David Lazer, Ryan Kennedy, Gary King, and Alessandro Vespignani. Big data. The parable of Google Flu: traps in big data analysis. Science (New York, N.Y.), 343(6176):1203–1205, March 2014.

Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, softcover reprint of hardcover 2nd ed. 2011 edition, August 2013.

Sandya Mannarswamy. Data Science: Learn the What, Where, and How of Data Science. Apress, 2015 edition, June 2015.

Christopher Manning and Hinrich Schuetze. Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge, Mass, 1 edition, June 1999.
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Gary Miner, John Elder, IV, Andrew Fast, Thomas Hill, Robert Nisbet, and Dursun Delen. Practical Text Mining and Statistical Analysis for Nonstructured Text Data Applications. Academic Press, 1 edition, January 2012.

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Mahmoud Parsian. Data Algorithms: Recipes for Scaling Up with Hadoop and Spark. O’Reilly Media, Sebastopol, 1 edition, July 2015.

Pethuru Raj, Anupama Raman, Dhivya Nagaraj, and Siddhartha Duggirala. High-Performance Big-Data Analytics: Computing Systems and Approaches. Springer, S.l., 2015 edition, August 2015.

Raghu Ramakrishnan and Johannes Gehrke. Database Management Systems. McGraw-Hill, Boston, Mass., 3rd edition, August 2002.

Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills. Advanced Analytics with Spark: Patterns for Learning from Data at Scale. O’Reilly Media, Beijing, 1 edition, April 2015.

Robert Sedgewick and Kevin Wayne. Algorithms. Addison-Wesley Professional, Upper Saddle River, NJ, 4th edition, March 2011.

Abraham Silberschatz, Henry Korth, and S. Sudarshan. Database System Concepts. McGraw-Hil Science/Engineering/Math, New York, 6 edition, January 2010.

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