The Self-Taught Computer Scientist

Data Structures
194
Additional Resources
As you know, computer science is an enormous topic. My goal was to write a book you would finish 
and cover the subjects most likely to help you in your programming careers. That means, unfortu-
nately, I had to leave a lot of topics out of this book. In this section, I will briefly cover some of the 
things I left out of this book and give you resources to learn about them.
Although you learned about binary trees in this book, I did not cover other trees deeply. You may 
want to spend more time studying different types of trees such as binary search trees, AVL trees, and 
parse trees. I also did not cover every common sorting algorithm. If you are preparing for a technical 
interview or want to learn even more about sorting algorithms, you should consider studying
heapsort, selection sort, quicksort, counting sort, and radix sort.
You can learn about all these topics and more in 
Introduction to Algorithms by Thomas H. Cormen 
(MIT Press, 2010). Be warned, however, that this is not an easy book to read. Now that you under-
stand computer science basics, you will ideally find the content much easier to understand. 
Computer 
Science Illuminated by Nell Dale and John Lewis (Jones & Bartlett Learning, 2012) is an excellent 
choice for learning more about computer science subjects outside of data structures and algorithms.
Final Thoughts
Thanks for choosing to read my book. I hope you enjoyed reading it as much as I enjoyed writing it. If 
you have questions or comments, feel free to reach out to me in the Self- Taught Programmers Facebook 
group located at 
https://facebook.com/groups/selftaughtprogrammers
. You can also subscribe 
to my newsletter to stay up- to- date with the Self- Taught community at 
https://selftaught.blog
. 
Finally, you can keep in touch with me on social media. My handle is 
@coryalthoff
 on Instagram, 
Twitter, and Facebook. If you have a second to leave a review on Amazon, I will be eternally grateful. 
Each review helps sales enormously, which allows me to continue creating new educational material 
for self- taught programmers.
Take care!

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A
abstract data type, 84, 87, 127, 163
acyclic graph, 174, 187
add_adj
 method, 179
add_edge
 method, 180
add_vertex
 method, 179
adjacency list, 177, 187
adjacent vertices, 176, 187
algorithm
analyzing of, 4–8
average- case complexity of, 15
best- case complexity of, 15
big O notation within, 7, 10, 11, 13–14, 15, 17
binary search of, 27–32, 34
brute- force, 14
constant time complexity of, 8–9
cubic time complexity of, 13–14
defined, 3, 17
exponential time of, 14–15
iterative, 19
linear search, 25–26
linear time of, 10
logarithmic time of, 9
log- linear time of, 11
polynomial time of, 14
quadratic time of, 11–13
run time of, 4–5
search, 25
sorting, 53–54
space complexity of, 15–16
steps within, 5–7
tortoise- and- the- hare, 110–111
worst- case complexity of, 15
American Standard Code for Information Interchange 
(ASCII), 32–33, 35
anagram, 55–56, 61
AND
operator, 69
an_iterable
parameter, 96
append
 method, 105, 107, 116, 118
application programming interface (API), 141, 145
array
associative, 137, 144
combining two lists within, 94–95
within computer memory, 89
creating, 90–91
defined, 87, 99
finding duplicates within a list, 95–97
finding the intersection of lists within, 98–99
heterogeneous, 89, 100
heterogeneous variable- length, 87, 100
moving zeros within, 91–94
multidimensional, 88, 100
within Numerical Python (NumPy) package, 90
one- dimensional, 88, 100
overallocation of, 90
performance of, 88–90
run time of, 88–89
as static data structure, 85
storage example of, 87
unique integer index and, 88
variable- length, 87, 100
array
 class, 90–91
array
 module, 91
array.array
parameter, 91
associative array, 137, 144
average- case complexity, 15, 18
B
backtracking, 84
balancing a heap, 164–166, 171
base, 30, 34, 63
base address, 88, 100
Index

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