1
Casella G, Berger RL. Statistical inference. Second edition. Delhi: : Cengage Learning 2017.
2
Abu-Mostafa YS, Magdon-Ismail M, Lin H-T. Learning from data: a short course. [United States]: : AMLBook.com 2012.
3
Grimmett G, Stirzaker D. Probability and random processes. Third edition. Oxford: : Oxford University Press 2001.
4
Mood AM, Graybill FA, Boes DC. Introduction to the theory of statistics. Third edition. [Auckland?]: : McGraw-Hill Book Company 1974.
5
Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: : Springer 2009.
6
Murphy KP. Machine learning: a probabilistic perspective. Cambridge, Mass: : MIT Press 2012.
7
Barber D. Bayesian reasoning and machine learning. Cambridge: : Cambridge University Press 2012.
8
Bishop CM. Pattern recognition and machine learning. Oxford: : Springer 2006.
9
Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction : with 200 full-color illustrations. New York: : Springer 2001.
10
Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge, Massachusetts: : The MIT Press 2016.
11
Raschka S. Python machine learning: unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics. Birmingham: : Packt Publishing 2015.
12
McKinney W. Python for data analysis. Farnham: : O’Reilly 2013. http://proxy.library.lincoln.ac.uk/login?url=http://www.dawsonera.com/depp/reader/protected/external/AbstractView/S9781449323622
13
Kevin Sheppard - Lecture Notes. https://www.kevinsheppard.com/Main_Page
14
Harrington P. Machine learning in action. Shelter Island, N.Y.: : Manning Publications 2012.
15
Source Code for the book: Machine Learning in Action published by Manning. https://github.com/pbharrin/machinelearninginaction
16
Zumel N, Mount J. Practical data science with R. Shelter Island, New York: : Manning 2014.
17
Nolan DA, Lang DT, editors. Data science in R: a case studies approach to computational reasoning and problem solving. Boca Raton, FL: : Chapman & Hall/CRC 2015.
18
Kabacoff R. R in action: data analysis and graphics with R. Second edition. Shelter Island, NY: : Manning 2015.
19
Wickham H. ggplot2: elegant graphics for data analysis. New York: : Springer 2009. https://www.vlebooks.com/vleweb/product/openreader?id=UniLincoln&isbn=9780387981413
20
Wickham H. Advanced R. Boca Raton, FL: : Chapman & Hall/CRC 2014.
21
Sarkar D. Lattice: multivariate data visualization with R. New York: : Springer 2008. https://www.vlebooks.com/vleweb/product/openreader?id=UniLincoln&isbn=9780387759692
22
Lantz B. Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications. Birmingham: : Packt Publishing Limited 2013. https://www.vlebooks.com/vleweb/product/openreader?id=UniLincoln&isbn=9781782162155
23
Wickham H. R packages. Sebastopol, California: : O’Reilly Media 2015.
24
Wickham H. Advanced R. Boca Raton, FL: : Chapman & Hall/CRC 2014.
25
Peng R. R Programming for Data Science. Morrisville: : Lulu.com 2016.
26
Martinez WL, Martinez AR. Computational statistics handbook with MATLAB. Third edition. Boca Raton: : Chapman & Hall/CRC 2016.
27
Kiusalaas J. Numerical methods in engineering with MATLAB. Third edition. Cambridge: : Cambridge University Press 2016.
28
Karau H, Konwinski A, Wendell P, et al. Learning Spark: lightning-fast big data analytics. Sebastopol, CA: : O’Reilly 2013. https://www.vlebooks.com/vleweb/product/openreader?id=UniLincoln&isbn=9781449359065
