In this chapter, which concludes our introduction to Python programming, you immersed yourself in some computer science thinking that goes beyond programming fundamentals. You created recursive functions—i.e., functions that call themselves. These functions (or methods) typically divide a problem into two conceptual pieces—the base case and the recursion step. The latter is a slightly smaller version of the original problem and is performed by a recursive function call. You saw some popular recursion examples, including calculating factorials and Fibonacci numbers. We compared recursive and iterative problem-solving approaches.

We also introduced searching and sorting. We discussed two searching algorithms—linear search and binary search—and three sorting algorithms—selection sort, insertion sort and recursive merge sort. We introduced Big O notation, which helps you express the efficiency of algorithms, making it easy to compare different algorithms for solving the same problem.

We presented an animated visualization of the selection sort using Seaborn and Matplotlib. We used the Matplotlib FuncAnimation class to drive the animation. We recast our selection_sort function as a generator function to provide values for display in the animation.

In the next part of the book, we present a series of implementation case studies that use a mix of AI and big-data technologies. We explore natural language processing, data mining Twitter, IBM Watson and cognitive computing, supervised and unsupervised machine learning, and deep learning with convolutional neural networks and recurrent neural networks. We discuss big-data software and hardware infrastructure, including NoSQL databases, Hadoop and Spark with a major emphasis on performance. You’re about to learn some really cool stuff!