It is reinforced at this point that the Program started in June 2016 and, therefore, only had its first graduates at the end of 2018. It should be noted that in 2019 the Program started to flow and the expectation is that the average training of graduates from the last two years (2019-2020) is compatible with the average of grade 4 Programs.

Despite having started its activities at the end of 2016, the PPCIC obtained a BOM concept in all dimensions: Program Proposal; Faculty; Student Body; Intellectual Production; and Social Insertion, which demonstrates the maturity of the Program.

As indicated at the end of the evaluation of the 2013-2016 quadrennial, the “Data Based Methods” line was renamed “Machine Learning and Optimization”, giving the line greater clarity and focus.

The research projects were revised to reflect the research carried out in the program in the best possible way, forming an organization between three to four projects per research line.


1. Charu Aggarwal and ChengXiang Zhai, editors. Mining Text Data. Springer, 2012 edition, February 2012.
2. Vijay Srinivas Agneeswaran. Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives. Pearson FT Press, Upper Saddle River, 1 edition, May 2014.
3. Mokhtar S. Bazaraa, John J. Jarvis, and Hanif D. Sherali. Linear Programming and Network Flows. Wiley, Hoboken, N.J, 4 edition, December 2009.
4. Francine Berman. Got Data? A Guide to Data Preservation in the Information Age. Commun. ACM, 51(12):50–56, December 2008.
5. Christopher Bishop. Pattern Recognition and Machine Learning. Springer, New York, October 2007.
6. Christine L. Borgman. Big Data, Little Data, No Data: Scholarship in the Networked World. The MIT Press, Cambridge, Massachusetts, January 2015.
7. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms. The MIT Press, Cambridge, Mass, 3rd edition, July 2009.
8. Peter Dalgaard. Introductory Statistics with R. Springer, New York, 2nd edition, August 2008.
9. Sanjoy Dasgupta, Christos Papadimitriou, and Umesh Vazirani. Algorithms. McGraw-Hill Education, Boston, 1 edition, September 2006.
10. J. Date. An Introduction to Database Systems. Pearson, Boston, Mass., 8 edition, August 2003.
11. Thomas H. Davenport and D. J. Patil. Data scientist: the sexiest job of the 21st century. Harvard Business Review, 90(10):70–76, 128, October 2012.
12. Jay L. Devore and Kenneth N. Berk. Modern Mathematical Statistics with Applications. Springer, New York; London, 2nd ed. 2012 edition, December 2011.
13. Vasant Dhar. Data science and prediction. Communications of the ACM, 56(12):64–73, 2013.
14. DSC. Data Science Central, 2015.
15. Victor Eijkhout. Introduction to High Performance Scientific Computing., Raleigh, N.C., January 2015.
16. Ramez Elmasri and Shamkant B. Navathe. Fundamentals of Database Systems. Pearson/Addison Wesley, Boston, 5th edition, March 2006.
17. Ronen Feldman and James Sanger. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 1 edition, December 2006.
18. 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.
19. Michael R. Garey and David S. Johnson. Computers and Intractability: A Guide to the Theory of NP- Completeness. W. H. Freeman, New York, first edition, January 1979.
20. Hilary Glasman-Deal. Science Research Writing for Non-Native Speakers of English. Imperial College Press, London; Hackensack, NJ, 1 edition, December 2009.
21. Georg Hager and Gerhard Wellein. Introduction to High Performance Computing for Scientists and Engineers. CRC Press, Boca Raton, FL, 1 edition, July 2010.
22. Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann, Waltham, Mass., 3 edition, July 2011.
23. 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.
24. 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.
25. Simon O. Haykin. Neural Networks and Learning Machines. Prentice Hall, New York, 3 edition, November 2008.
26. John L. Hennessy and David A. Patterson. Computer Architecture: A Quantitative Approach. Morgan Kaufmann Publishers, Waltham, MA, September 2011.
27. Tony Hey, Stewart Tansley, and Kristin Tolle, editors. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, Redmond, Washington, 1 edition, October 2009.
28. Adam Jacobs. The Pathologies of Big Data. Commun. ACM, 52(8):36–44, August 2009.
29. 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.
30. 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.
31. Matthew L. Jockers. Text Analysis with R for Students of Literature. Springer, New York, July 2014.
32. Anne Kao and Steve R. Poteet. Natural Language Processing and Text Mining. Springer London, 1 edition,March 2007.
33. Jon Kleinberg and E va Tardos. Algorithm Design. Pearson, Boston, 1 edition, March 2005.
34. Donald E. Knuth. The Art of Computer Programming. Addison-Wesley Professional, Amsterdam, March 2011.
35. Peter Lake and Robert Drake. Information Systems Management in the Big Data Era. Springer, New York, NY, 2014 edition, January 2015.
36. Brett Lantz. Machine Learning with R. Packt Publishing, Birmingham, October 2013.
37. Richard J. Larsen and Morris L. Marx. An Introduction to Mathematical Statistics and Its Applications. Prentice Hall, Upper Saddle River, N.J, 4 edition, December 2005.
38. 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.
39. Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, softcover reprint of hardcover 2nd ed. 2011 edition, August 2013.
40. Sandya Mannarswamy. Data Science: Learn the What, Where, and How of Data Science. Apress, 2015 edition, June 2015.
41. Christopher Manning and Hinrich Schuetze. Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge, Mass, 1 edition, June 1999.
42. MDS. Master’s in Data Science, 2015.
43. 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.
44. Hrushikesha Mohanty, Prachet Bhuyan, and Deepak Chenthati, editors. Big Data: A Primer. Springer, New York, NY, 2015 edition, July 2015.
45. Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge, MA, 1 edition, August 2012.
46. Nils J. Nilsson. Principles of Artificial Intelligence. Springer, Berlin, soft-cover reprint of the original 1st ed. 1982 edition, April 2013.
47. Ibrahim H. Osman and James P. Kelly, editors. Meta-Heuristics: Theory and Applications. Springer, Boston, 1996 edition, March 1996.
48. Mahmoud Parsian. Data Algorithms: Recipes for Scaling Up with Hadoop and Spark. O’Reilly Media, Sebastopol, 1 edition, July 2015.
49. 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.
50. Raghu Ramakrishnan and Johannes Gehrke. Database Management Systems. McGraw-Hill, Boston, Mass., 3rd edition, August 2002.
51. 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.
52. Robert Sedgewick and Kevin Wayne. Algorithms. Addison-Wesley Professional, Upper Saddle River, NJ, 4th edition, March 2011.
53. Abraham Silberschatz, Henry Korth, and S. Sudarshan. Database System Concepts. McGraw-Hil Science/Engineering/Math, New York, 6 edition, January 2010.
54. G. Srinivasa and Anil Kumar Muppalla. Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark. Springer, New York, NY, 2015 edition, February 2015.
55. William Stallings. Computer Organization and Architecture: International Edition. Pearson Education, 9 edition, March 2013.
56. Robert Stevens, Jun Zhao, and Carole Goble. Using provenance to manage knowledge of in silico experiments. Briefings in Bioinformatics, 8(3):183– 194, May 2007.
57. Andrew S. Tanenbaum and Todd Austin. Structured Computer Organization. Pearson, Boston, 6 edition, August 2012.
58. Ronald E Walpole. Probability & statistics for engineers & scientists. Prentice Hall, Boston, 2012.
59. Raul Wazlawick. Metodologia de Pesquisa para Ciência da Computação. Elsevier, 2 edition, September 2014.
60. Wayne L. Winston. Operations Research: Applications and Algorithms. Duxbury Press, Belmont, CA, 4 edition, July 2003.
61. Ian H. Witten, Eibe Frank, and Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington, MA, 3 edition, January 2011.
62. Laurence A. Wolsey. Integer Programming. Wiley-Interscience, New York, 1 edition, September 1998.
63. Alex Wright. Big Data Meets Big Science. Commun. ACM, 57(7):13–15, July 2014.
64. Mohammed J. Zaki and Wagner Meira Jr. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, May 2014.
65. Justin Zobel. Writing for Computer Science. Springer, New York, NY, 3rd ed. 2014 edition, February 2015. M. Tamer Ozsu and Patrick Valduriez. Principles of Distributed Database Systems. Springer, New York, 3rd ed. 2011 edition, March 2011.