Table of Contents
Python for Data Science - A Hands-On Introduction by Yuli Vasiliev
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Book Summary
A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples.
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You’ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support.
You will discover Python’s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.
About the Author
Yuli Vasiliev is a programmer, freelance writer, and consultant, who has been working with databases for more than two decades. He specializes in open-source development, and is experienced in building data structures and models, as well as designing and implementing database backends for various applications using Oracle technologies, MySQL, and natural language processing. Vasiliev is the author of Natural Language Processing with spaCy (No Starch Press).
Product Details
- Publication date: August 2, 2022
- Paperback: 240 pages
- Time to Complete: 6h 3m
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Data Science: Fundamentals of Data Science, DataOps, Big Data, Data Science IDEs (Jupyter Notebook, JetBrains DataGrip, Google Colab, JetBrains DataSpell, SQL Server Management Studio, MySQL Workbench, Oracle SQL Developer, SQLiteStudio), Data Science Tools (SQL, Apache Arrow, Pandas, NumPy, Dask, Spark, Kafka); Data Science Programming Languages (Python Data Science, NumPy Data Science, R Data Science, Java Data Science, C++ Data Science, MATLAB Data Science, Scala Data Science, Julia Data Science, Excel Data Science (Excel is the most popular "programming language") - Google Sheets, SAS Data Science, C# Data Science, Golang Data Science, JavaScript Data Science, Kotlin Data Science, Ruby Data Science, Rust Data Science, Swift Data Science, TypeScript Data Science, Bash Data Science); Databases, Data, Augmentation, Analysis, Analytics, Archaeology, Cleansing, Collection, Compression, Corruption, Curation, Degradation, Editing (EmEditor), Data engineering, ETL/ ELT ( Extract- Transform- Load), Farming, Format management, Fusion, Integration, Integrity, Lake, Library, Loss, Management, Migration, Mining, Pre-processing, Preservation, Protection (privacy), Recovery, Reduction, Retention, Quality, Science, Scraping, Scrubbing, Security, Stewardship, Storage, Validation, Warehouse, Wrangling/munging. ML-DL - MLOps. Data science history, Data Science Bibliography, Manning Data Science Series, Data science Glossary, Data science topics, Data science courses, Data science libraries, Data science frameworks, Data science GitHub, Data Science Awesome list. (navbar_datascience - see also navbar_python, navbar_numpy, navbar_data_engineering and navbar_database)
Data Structures: Array, Linked List, Stack, Queue, Binary Tree, Binary Search Tree, Heap, Hash Table, Graph, Trie, Skip List, Red-Black Tree, AVL Tree, B-Tree, B+ Tree, Splay Tree, Fibonacci Heap, Disjoint Set, Adjacency Matrix, Adjacency List, Circular Linked List, Doubly Linked List, Priority Queue, Dynamic Array, Bloom Filter, Segment Tree, Fenwick Tree, Cartesian Tree, Rope, Suffix Array, Suffix Tree, Ternary Search Tree, Radix Tree, Quadtree, Octree, KD Tree, Interval Tree, Sparse Table, Union-Find, Min-Max Heap, Binomial Heap, And-Or Graph, Bit Array, Bitmask, Circular Buffer, Concurrent Data Structures, Content Addressable Memory, Deque, Directed Acyclic Graph (DAG), Edge List, Eulerian Path and Circuit, Expression Tree, Huffman Tree, Immutable Data Structure, Indexable Skip List, Inverted Index, Judy Array, K-ary Tree, Lattice, Linked Hash Map, Linked Hash Set, List, Matrix, Merkle Tree, Multimap, Multiset, Nested Data Structure, Object Pool, Pairing Heap, Persistent Data Structure, Quad-edge, Queue (Double-ended), R-Tree, Radix Sort Tree, Range Tree, Record, Ring Buffer, Scene Graph, Scapegoat Tree, Soft Heap, Sparse Matrix, Spatial Index, Stack (Min/Max), Suffix Automaton, Threaded Binary Tree, Treap, Triple Store, Turing Machine, Unrolled Linked List, Van Emde Boas Tree, Vector, VList, Weak Heap, Weight-balanced Tree, X-fast Trie, Y-fast Trie, Z-order, Zero-suppressed Decision Diagram, Zigzag Tree
Data Structures Fundamentals - Algorithms Fundamentals, Algorithms, Data Types; Primitive Types (Boolean data type, Character (computing), Floating-point arithmetic, Single-precision floating-point format - Double-precision floating-point format, IEEE 754, Category:Floating point types, Fixed-point arithmetic, Integer (computer science), Reference (computer science), Pointer (computer programming), Enumerated type, Date Time);
Composite Types or Non-Primitive Types: Array data structure, String (computer science) (Array of characters), Record (computer science) (also called Struct (C programming language)), Union type (Tagged union, also called Variant type, Variant record, Discriminated union, or Disjoint union);
Abstract Data Types: Container (data structure), List (abstract data type), Tuple, Associative array (also called Map, Multimap, Set (abstract data type), Multiset (abstract data type) (also called Multiset (bag)), Stack (abstract data type), Queue (abstract data type), (e.g. Priority queue), Double-ended queue, Graph (data structure) (e.g. Tree (data structure), Heap (data structure))
Data Structures and Algorithms, Data Structures Syntax, Data Structures and OOP - Data Structures and Design Patterns, Data Structures Best Practices, Data Structures and Containerization, Data Structures and IDEs (IntelliSense), Data Structures and Development Tools, Data Structures and Compilers, Data Structures and Data Science - Data Structures and DataOps, Machine Learning Data Structures - Data Structures and MLOps, Deep Learning Data Structures, Functional Data Structures, Data Structures and Concurrency - Data Structures and Parallel Programming, Data Structure Libraries, Data Structures History, Data Structures Bibliography (Grokking Data Structures), Data Structures Courses, Data Structures Glossary, Data Structures Topics, Data Structures Research, Data Structures GitHub, Written in Data Structures, Data Structures Popularity, Data Structures Awesome. (navbar_data_structures - see also navbar_cpp_containers, navbar_math_algorithms, navbar_data_algorithms, navbar_design_patterns, navbar_software_architecture)
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