7. References

1

L.L. Beck. System Software: An Introduction to Systems Programming. Addison-Wesley, 1997. ISBN 9780201423006. URL: https://books.google.ca/books?id=eaZ-zwEACAAJ.

2

L. Breiman. Classification and Regression Trees. CRC Press, 2017. ISBN 9781351460491. URL: https://books.google.ca/books?id=MGlQDwAAQBAJ.

3

P.G. Bryant and M.A. Smith. Practical Data Analysis: Case Studies in Business Statistics. Number v. 2 in Practical Data Analysis: Case Studies in Business Statistics. Irwin, 1995. ISBN 9780256158281. URL: https://books.google.ca/books?id=dproKgAACAAJ.

4

M.I.T.C. Data. Secondary Analysis of Electronic Health Records. Springer International Publishing, 2016. ISBN 9783319437422. URL: https://books.google.ca/books?id=qtlCDwAAQBAJ.

5

A.B. Downey. Think Python: How to Think Like a Computer Scientist. O'Reilly Media, 2015. ISBN 9781491939413. URL: https://books.google.ca/books?id=mZwbCwAAQBAJ.

6

C. Fehily. Python. Visual quickstart guide. Peachpit Press, 2002. ISBN 9780201748840. URL: https://books.google.ca/books?id=carqdIdfVlYC.

7

Kristen B. Gorman, Tony D. Williams, and William R. Fraser. Ecological sexual dimorphism and environmental variability within a community of antarctic penguins (genus pygoscelis). PLOS ONE, 9(3):1–14, 03 2014. doi:10.1371/journal.pone.0090081.

8

Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant. Array programming with NumPy. Nature, 585(7825):357–362, September 2020. doi:https://doi.org/10.1038/s41586-020-2649-2.

9

I. Kalb. Object-Oriented Python: Master OOP by Building Games and GUIs. No Starch Press, 2022. ISBN 9781718502062. URL: https://books.google.ca/books?id=CxZOEAAAQBAJ.

10

J.W.B. Lin, H. Aizenman, E.M.C. Espinel, K. Gunnerson, and J. Liu. An Introduction to Python Programming for Scientists and Engineers. Cambridge University Press, 2022. ISBN 9781108701129. URL: https://books.google.ca/books?id=w7htEAAAQBAJ.

11

R.J.A. Little and D.B. Rubin. Statistical Analysis with Missing Data. Wiley Series in Probability and Statistics. Wiley, 2019. ISBN 9780470526798. URL: https://books.google.ca/books?id=BemMDwAAQBAJ.

12

A. Martelli. Python in a Nutshell. In a Nutshell (o'Reilly) Series. O'Reilly, 2003. ISBN 9780596001889. URL: https://books.google.ca/books?id=6TEcaEzA8N0C.

13

A. Martelli, A.M. Ravenscroft, S. Holden, and P. McGuire. Python in a Nutshell. O'Reilly Media, 2023. ISBN 9781098113513. URL: https://books.google.ca/books?id=2WSmEAAAQBAJ.

14

E. Matthes. Python Crash Course: A Hands-On, Project-Based Introduction to Programming. No Starch Press, 2015. ISBN 9781593276034. URL: https://books.google.ca/books?id=RXoZCwAAQBAJ.

15

Christian Mayer. Python __contains__() magic method. https://blog.finxter.com/python-__contains__-magic-method/, 2023. [Online; accessed 01-August-2023].

16

Christian Mayer. Python __len__() magic method. https://blog.finxter.com/python-__len__-magic-method/, 2023. [Online; accessed 01-August-2023].

17

P. Mccaffrey. An Introduction to Healthcare Informatics: Building Data-Driven Tools. Elsevier Science, 2020. ISBN 9780128149164. URL: https://books.google.ca/books?id=U-LcDwAAQBAJ.

18

W. McKinney. Python for Data Analysis. O'Reilly Media, 2022. ISBN 9781098104009. URL: https://books.google.ca/books?id=EgKBEAAAQBAJ.

19

Wes McKinney and others. Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, volume 445, 51–56. Austin, TX, 2010.

20

D. Mertz. Text Processing in Python. Addison-Wesley, 2003. ISBN 9780321112545. URL: https://books.google.ca/books?id=GxKWdn7u4w8C.

21

K.K. Mohbey and B. Bakariya. An Introduction to Python Programming: A Practical Approach: Using Python to Solve Complex Problems with a Burst of Machine Learning (English Edition). BPB Publications, 2021. ISBN 9789391392062. URL: https://books.google.ca/books?id=tVc\_EAAAQBAJ.

22

S. Molin and K. Jee. Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization. Packt Publishing, 2021. ISBN 9781800565913. URL: https://books.google.ca/books?id=Eh4sEAAAQBAJ.

23

R. Nayak and N. Gupta. Python for Engineers and Scientists: Concepts and Applications. CRC Press, 2022. ISBN 9781000802160. URL: https://books.google.ca/books?id=\_PmXEAAAQBAJ.

24

A. Pajankar. Hands-on Matplotlib: Learn Plotting and Visualizations with Python 3. Apress, 2021. ISBN 9781484274095. URL: https://books.google.ca/books?id=kUCZzgEACAAJ.

25

Pankaj and Andrea Anderson. How to use the __str__() and __repr__() methods in python. https://www.digitalocean.com/community/tutorials/python-str-repr-functions, 2023. [Online; accessed 01-August-2023].

26

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.

27

L. Ramalho. Fluent Python. O'Reilly Media, 2022. ISBN 9781492056324. URL: https://books.google.ca/books?id=ICdnEAAAQBAJ.

28

Leodanis Pozo Ramos. Python's .__call__() method: creating callable instances. hhttps://realpython.com/python-callable-instances/, 2023. [Online; accessed 01-August-2023].

29

Nicholas Rini. Benefits of object-oriented programming in java. https://www.developer.com/java/oop-benefits/, 2023. [Online; accessed 01-August-2023].

30

J. Rioux. Data Analysis with Python and PySpark. Manning, 2022. ISBN 9781617297205. URL: https://books.google.ca/books?id=4ytfEAAAQBAJ.

31

A. Sweigart. Beyond the Basic Stuff with Python: Best Practices for Writing Clean Code. No Starch Press, 2020. ISBN 9781593279660. URL: https://books.google.ca/books?id=7GUKEAAAQBAJ.

32

R. van Hattem. Mastering Python: Write powerful and efficient code using the full range of Python's capabilities. Packt Publishing, 2022. ISBN 9781800202108. URL: https://books.google.ca/books?id=jehvEAAAQBAJ.

33

Michael L. Waskom. Seaborn: statistical data visualization. Journal of Open Source Software, 6(60):3021, 2021. URL: https://doi.org/10.21105/joss.03021, doi:10.21105/joss.03021.

34

B. Wilson. Machine Learning Engineering in Action. Manning, 2022. ISBN 9781617298714. URL: https://books.google.ca/books?id=gitnEAAAQBAJ.

35

Government of Alberta. Alberta regional dashboard & site selector. https://regionaldashboard.alberta.ca/, 2024. [Online; accessed 15-July-2024].

36

Matplotlib Developers. Matplotlib 3.9.1 documentation. https://matplotlib.org/stable/index.html, 2024.

37

Numba Developers. Numba documentation. https://numba.readthedocs.io/en/stable/index.html, 2023. [Online; accessed 01-August-2023].

38

NumPy Developers. Numpy documentation. https://numpy.org/doc/stable/index.html, 2023. [Online; accessed 01-August-2023].

39

Pandas Developers. Pandas documentation. https://pandas.pydata.org/docs/, 2023. [Online; accessed 01-August-2023].

40

pip developers. Commands - pip documentation v24.1.1. 2024. URL: https://pip.pypa.io/en/stable/cli/index.html (visited on 2024-07-05).

41

Python Software Foundation. Python 3.12.4 documentation. https://docs.python.org/, 2024. [Online; accessed 01-June-2024].

42

scikit-learn Developers. Scikit-learn user guide. https://scikit-learn.org/stable/user_guide.html, 2023. [Online; accessed 01-August-2023].