What this book covers

Chapter 1, The Hunt for Data, identifies core approaches in reading data from various external sources such as CSV, JSON, XML, HTML, MongoDB, and SQLite.

Chapter 2, Integrity and Inspection, explains the importance of cleaning data through recipes about trimming whitespaces, lexing, and regular expression matching.

Chapter 3, The Science of Words, introduces common string manipulation algorithms, including base conversions, substring matching, and computing the edit distance.

Chapter 4, Data Hashing, covers essential hashing functions such as MD5, SHA256, GeoHashing, and perceptual hashing.

Chapter 5, The Dance with Trees, establishes an understanding of the tree data structure through examples that include tree traversals, balancing trees, and Huffman coding.

Chapter 6, Graph Fundamentals, manifests rudimentary algorithms for graphical networks such as graph traversals, visualization, and maximal clique detection.

Chapter 7, Statistics and Analysis, begins the investigation of important data analysis techniques that encompass regression algorithms, Bayesian networks, and neural networks.

Chapter 8, Clustering and Classification, involves quintessential analysis methods that involve k-means clustering, hierarchical clustering, constructing decision trees, and implementing the k-Nearest Neighbors classifier.

Chapter 9, Parallel and Concurrent Design, introduces advanced topics in Haskell such as forking I/O actions, mapping over lists in parallel, and benchmarking performance.

Chapter 10, Real-time Data, incorporates streamed data interactions from Twitter, Internet Relay Chat (IRC), and sockets.

Chapter 11, Visualizing Data, deals with sundry approaches to plotting graphs, including line charts, bar graphs, scatter plots, and D3.js visualizations.

Chapter 12, Exporting and Presenting, concludes the book with an enumeration of algorithms for exporting data to CSV, JSON, HTML, MongoDB, and SQLite.