[eBook] [PDF] Intro to Python for Computer Science and Data Science Learning to Program with AI, Big Data and The Cloud, Global Edition By Paul Deitel
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[eBook] [PDF] Intro to Python for Computer Science and Data Science Learning to Program with AI, Big Data and The Cloud, Global Edition By Paul Deitel

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882
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50.46 MB
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About this ebook
The Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud" by Paul Deitel and Harvey Deitel follows a unique, modular architecture. The book divides its core material into traditional Computer Science (CS) topics, integrated introductory Data Science (DS) takeaways, and deep-dive Advanced Data Science/AI case studies. [1, 2]
The book is structured into the following main topics and chapters: [1, 2]
1. Computer Science Fundamentals (Chapters 1–11)
These chapters cover traditional introductory programming and computer science principles, with a strong focus on software engineering and code clarity: [1, 2]
  • Introduction to Computers and Python: Hardware/software, data hierarchy, the Python Standard Library, data-science libraries, and a hands-on test drive using IPython and Jupyter Notebooks.
  • Introduction to Python Programming: Variables, type systems, assignment statements, arithmetic expressions, basic string formatting (print), input extraction, and fundamental decision-making via if statements.
  • Control Statements and Program Development: Control flows (algorithms, if...else, while, for loops), sequence iteration, nested control flows, and Sentinel-controlled loops.
  • Functions: Defining custom modules, parameter passing, localized scoping rules, and foundational functional-style programming structures.
  • Sequences (Lists and Tuples): Storing ordered data collections, sorting sequences, searching items, and subsetting using slicing mechanisms.
  • Dictionaries and Sets: Mapping key-value pairs, unique set operations, and localized string tokenization.
  • Array-Oriented Programming with NumPy: High-performance multidimensional processing using NumPy arrays.
  • Strings: Advanced text manipulation, tokenization via regular expressions (regex), parsing formats, and localized data cleanup.
  • Files and Exceptions: Reading and writing text formats, serialization via JSON and CSV, structural error boundary definitions, and try/except/finally paradigms.
  • Object-Oriented Programming (OOP): Creating custom classes, custom attributes, instance methods, properties, and overriding underlying dunder methods.
  • Computer Science Thinking: Algorithmic complexity analysis (Big O notation), recursion patterns, and building custom standard data structures (linked lists, stacks, queues). [1, 2, 3, 4]
2. Integrated "Intro to Data Science" Modules
Embedded at the end of Chapters 1–10 are targeted data-science units. These bridge early programming concepts directly to data analytics with minimal friction: [1, 2, 3]
  • Basic Statistics: Computing central tendency values (mean, median, mode) and dispersion measures (variance, standard deviation).
  • Simulation and Visualization: Using random number generations alongside 2D/3D static, dynamic, or interactive visual tools (via Matplotlib and Seaborn).
  • Data Exploration: Leveraging the pandas library (Series and DataFrames) for structured data manipulation and data wrangling.
  • Time-Series and Regression: Analyzing trends across specific time constraints and executing simple linear regressions. [1, 2]
3. Advanced AI, Big Data, and Cloud Case Studies (Chapters 12–17)
The final third of the textbook moves into specialized, fully implemented case studies utilizing production-grade datasets and industry-standard open-source libraries: [1, 2]
  • Natural Language Processing (NLP): Analyzing text, sentiment classification, and linguistic pattern parsing via NLTK, TextBlob, and spaCy.
  • Data Mining Twitter: Streaming live social platform feeds, extracting geometric location metrics, and sentiment trends via Tweepy.
  • IBM Watson: Accessing enterprise-grade cloud cognitive computing resources via specialized developer APIs.
  • Machine Learning: Building predictive modeling pipelines, feature transformations, classification, and clustering via Scikit-learn.
  • Deep Learning & Computer Vision: Executing neural network architectures, image recognition, and convolutional training optimizations via Keras.
  • Big Data (Hadoop, Spark, and NoSQL): Working with multi-node infrastructure frameworks for distributed clusters, map-reduction scripts, and unstructured MongoDB query structures.
  • Internet of Things (IoT): Real-time sensor device communication and telemetry ingestion pipelines via web platforms like PubNub.

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eBook-PDF-Intro-to-Python-for-Computer-Science-and-Data-Science-Learning-to-Program-with-AI-Big-Data-and-The-Cloud-Global-Edition-By-Paul-Deitel.pdf 50.46 MB

Topics

Advanced AI Big Data and Cloud Case Studies Array-Oriented Programming with NumPy Basic Statistics Computer Science Fundamentals Computer Science Thinking Computers and Python Control Statements and Program Development Data Mining Twitter Deep Learning & Computer Vision Dictionaries and Sets Files and Exceptions IBM Watson Introduction to Python Programming Machine Learning Natural Language Processing NLP Object-Oriented Programming (OOP) Simulation and Visualization Time-Series and Regression
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