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1. Stochastic Simulation and Monte Carlo Methods: Mathematical Foundations of Stochastic Simulation/Authors: Carl Graham, Denis Talay/2013

2. Beginning Python: From Novice to Professional/Magnus Lie Hetland/2008

3. Principles of Model Checking/Christel Baier/2008

4. Software Engineering - The Current Practice /2012

5. Introduction toSoftwareEngineering/ Ronald J. Leach/2016

6. Data Structures and Algorithms in C++/by Adam Drozdek /2012

7. Algorithms and theory of computation handbook/ Mikhail J Atallah/2010

8. Introduction to Numerical Analysis/1992

9. Applied linear statistical models/ John Neter/2004

10. Competitive Programmer's Handbook/ by Antti Laaksenon/2018

11. Applied liner regression: Computing Primer for Applied Linear Regression Using R/ Sanford Weisberg/2014

12. Data Classification: Algorithms and Applications / by Charu C. Aggarwal/2014

13. Data Structures and Algorithms in Java / by Michael T. Goodrich/2005

14. Data Structures and Algorithms Using C#/by Michael McMillan /2007

15. Data Structures & Algorithm Analysis in C++/by Mark A. Weiss / 2013

16. Data Structures and Algorithms in Java/by Michael T. Goodrich, Roberto Tamassia, et al/2014

17. Decision Making under Deep Uncertainty: From Theory to Practice/by Vincent A. W. J. Marchau, Warren E. Walker, et al. / 2019

18. Deep Learning for Natural Language Processing: Develop Deep Learning Models for your Natural Language Problems/ Author. Jason Brownlee/2017

19. Essentials of Stochastic Processes/ Rick Durrett/2011

20. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms /by John D. Keller/2015

21. Stochastic Processes: Theory for Applications/by Robert G. Gallagher/2014

22. Generalized Linear Models/by P. McCullough (Author), John A. Nelder/1989

23. Handbook of Graph Theory, Combinatorial Optimization, and Algorithms/by Krishnaiyan “KT” Thulasiraman/2016

24. Introduction to Probability/by Charles M. Grin stead/2012

25. Introduction to Probability and Statistics for Engineers and Scientists/Author: Sheldon Ross/2004

26. Intuitive Probability and Random Processes using MATLAB/by Steven Kay /2005

27. Probability Theory: The Logic of Science/by E. T. Jayne’s and G. Larry Brett horst / 2003

28. Linear Models with R /by Julian J. Faraway /2014

29. Machine Learning: A Probabilistic Perspective Textbook/ by Kevin P. Murphy/2012

30. Fundamentals of Probability and Statistics for Engineers /by T. T. Soong/2007

31. Handbook of Data Structures and Applications/by Dinesh P. Mehta (Editor)/2004

32. Modern Actuarial Risk Theory Using R/ Authors: Kaas, R., Goovaerts, M., Dhaene, J., Denuit, M/2009

33. A Modern Introduction to Probability and Statistics: Understanding Why and How/Authors: Dekking, F.M., Kraaikamp, C., Lopuhaä, H.P., Meester, And L.E/2005

34. Natural Language Processing in Action: Understanding, analyzing, and generating text with Python/by y Hobson Lane  (Author), Hannes Hapke  (Author), Cole Howard  (Author)/2019

35. Open Data Structures (in Java)/ Pat Morin/2013

36. Optimization Methods in Finance/ by Gérard Cornuéjols and Reha Tütüncü/2006

37. Optimization Models/ by Giuseppe C. Calafiore and Laurent El Ghaoui / 2014

38. Probability, Random Variables and Stochastic Processes/ By Athanasios Papoulis and S. Unnikrishna Pillai / 2002

39. Probability and mathematical statistics/Prasanna Sahoo/2013

40. Principles of Program Analysis/ by Flemming Nielson, Hanne R. Nielson, et al. /2005

41. Probability, Statistics, and Random Processes for Electrical Engineering /by Leon-Garcia Alberto / 2011

42. Probability and Statistics / by Michael J. Evans and Jeffrey S. Rosenthal/2009

43. Probability: Theory and Examples/Rick Durrett/2019

44. Relational data clustering algorithms with biomedical application/MOHAMMED A. KHALILIA/2014

45. Mathematical Statistics and Data Analysis /by John A. Rice / 2006

46. Mathematical Modeling and Statistical Methods for Risk Management/Henrik Hult and Filip Lindskog/2007

47. Schaum's Outline of Theory and Problems of Probability, Random Variables, and Random Processes/ Hwei P. Hsu/1997

48. Data Structures and Algorithms in Java /by Robert Lafore / 2002

49. Stochastic Calculus for Finance I: The Binomial Asset Pricing Model Solution of Exercise Problems/ Yan Zeng/ 2014

50. Simpler: Using R for Introductory Statistics/ John Verzani/2002

51. Understanding Complex Datasets: Data Mining with Matrix Decompositions / by David Skillicorn / 2007

52. SPSS for Intermediate Statistics: Use and Interpretation/by Nancy Leech, Karen Barrett, et al. / 2004

53. Stochastic Epidemic Models with Inference /by Tom Britton, Etienne Pardoux, et al. / 2019

54. Getting Started in Stock Analysis/by Michael C. Thomsett (Author)/2015

55. Student Solutions Manual to accompany Applied Linear Regression Models/ Michael H. Kutner/2004

56. Understanding Stocks / Michael Sincere/2004

57. Applied Statistics for Business and Economics/by Robert M. and Leekley / 2010

58. Fundamentals of Database Systems /by Ramez Elmasri and Shamkant B. Navathe /2010

59. Financing Social Protection/ by Michael Cichon (Author), Wolfgang Scholz (Author), Arthur van de Meerendonk (Author)/2005

60. Financial Institutions Management: A Risk Management Approach / by M. Cornett A. Saunders | 2007

61. Financial Production, Flows and Stocks in the System of National Accounts/2014

62. Catastrophe Modeling: A New Approach to Managing Risk /by Patricia Grossi and Howard Kunreuther / 2005

63. Stochastic Calculus and Financial Applications /by J. Michael Steele / 2000

64. Theory of Interest and Life Contingencies with Pension Applications: A Problem Solving Approach/by ASA Michael M. Parmenter/ 1999

65. Foundations of Computational Intelligence Volume 6: Data Mining/Editors: Abraham, A., And Hassanien/2009

66. A First Course in Probability /by Sheldon Ross / 2009

67. An Introduction to Market Risk Measurement /by Kevin Dowd /2002

68. Combinatorial Optimization: Theory and Algorithms/by Korte, Bernhard, Vygen, Jens/2006

69. Engineering Analysis with NX Advanced Simulation /by P. Goncharov (Author), I. Artamonov (Author), T. Khalitov (Author)/2014

70. Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk with Implementation in R and Matlab/by Jon Danielsson / 2011

71. Numerical Methods for Stochastic Partial Differential Equations with White Noise /by Zhongqiang Zhang and George Em Karniadakis / 2017

72. Neural Network Methods in Natural Language Processing /by Yoav Goldberg  /2017

73. Parallel Processing and Parallel Algorithms: Theory and Computation/by Roosta, Seyed H/2000

74. Risk Management and Financial Institutions /by John C. Hull / 2015

75. Sparse Modeling: Theory, Algorithms, and Applications /by Irina Rish (Author), Genady Grabarnik (Author)/2014

76. Advanced and Multivariate Statistical Methods/by Craig A. Mertler and Rachel Vannatta Reinhart / 2016

77. An Introduction to Statistical Methods and Data Analysis/by R. Lyman Ott and Micheal T. Longnecker /2001

78. An Introduction to Statistical Methods and Data Analysis /by R. Lyman Ott and Micheal Longnecker / 2008

79. Statistics of Financial Markets: An Introduction/byFranke, Jürgen, Härdle, Wolfgang Karl, Hafner, Christian Matthias/2008

80. Statistical Data Analysis/by Glen Cowan/1998

81. Algorithmic and Programming: Training materials for Teachers/ Maria Christodoulou/2018

82. Probability and mathematical statistics/Sahoo, 2013

83. Mathematical Statistics with Applications/by Kandethody M. Ramachandran (Author), Chris P. Tsokos (Author)/2009

84. Quantitative Financial Economics: Stocks, Bonds and Foreign Exchange/By Keith Cuthbertson and Dirk Nitzsche /1996