My Bookshelf

"In Sha Allah one day I will finish reading all of these books and use the knowledge to contribute to the world!"

All books are collected from the internet for personal learning purposes and categorized by using AI.

Problem Solving

Beginner

  • Language: C/C++
  • Focus: Core data structures and standard problems.
  • Why It's Good: Detailed explanations with dry-run tables and complexity analysis.
  • Use Case: Great to strengthen understanding of DSA after your first course.

1. Data Structures and Algorithms Made Easy

by Narasimha Karumanchi

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  • Language: Python
  • Focus: Illustrated explanations of core algorithms (e.g., binary search, sorting, recursion, graph traversal).
  • Why It's Good: Perfect for absolute beginners. Uses visual aids to explain complex topics.
  • Use Case: Read before or alongside your first programming course.

2. Grokking Algorithms

by Aditya Bhargava

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  • Language: Pseudocode
  • Focus: Algorithm design techniques (Divide & Conquer, Greedy, Dynamic Programming).
  • Why It's Good: Blends theory with practical examples.
  • Use Case: Best when transitioning from structured programming to algorithmic thinking.

3. Introduction to the Design and Analysis of Algorithms

by Anany Levitin

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  • Language: Python
  • Focus: Problem-solving through hands-on examples.
  • Why It's Good: Builds the mental model for competitive programming.
  • Use Case: Use with online judges like HackerRank, Kattis.

4. Algorithmic Thinking: A Problem-Based Introduction

by Daniel Zingaro

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  • Language: JavaScript (Concepts apply to all languages)
  • Focus: Demystifies concepts like recursion, hash tables, trees, big-O.
  • Why It's Good: Gentle introduction with real-world metaphors.
  • Use Case: Best for non-CS or self-taught learners.

5. A Common-Sense Guide to Data Structures and Algorithms

by Jay Wengrow

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Intermediate

  • Language: Java (easy to adapt)
  • Focus: 189 programming interview questions.
  • Why It's Good: Covers arrays, strings, trees, graphs, bit manipulation, and system design.
  • Use Case: Essential prep for tech job interviews.

1. Cracking the Coding Interview

by Gayle Laakmann McDowell

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  • Language: Python, Java, C++
  • Focus: Interview problems with in-depth explanations.
  • Why It's Good: Divides problems by type, with space/time trade-offs.
  • Use Case: Use after mastering basics to tackle medium–hard interview questions.

2. Elements of Programming Interviews (EPI)

by Adnan Aziz et al.

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  • Language: C++ (conceptual explanations are language-independent)
  • Focus: CP strategies, complexity, graph algorithms, DP.
  • Why It's Good: Ideal for those moving from DSA to contests.
  • Use Case: Follow alongside Codeforces or AtCoder contests.

3. Guide to Competitive Programming

by Antti Laaksonen

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  • Language: C++ with STL
  • Focus: Problems solved in ACM ICPC/IOI style.
  • Why It's Good: Offers problem sets and techniques from real competitions.
  • Use Case: Practice with UVA, SPOJ, Codeforces archives.

4. Competitive Programming 3 (CP3)

by Steven & Felix Halim

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  • Language: C++
  • Focus: Lightweight but wide coverage of CP topics.
  • Why It's Good: Direct, concise, problem-first explanations.
  • Use Case: Follow before CP3 for Codeforces/AtCoder prep.

5. Competitive Programmer’s Handbook

by Antti Laaksonen

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  • Language: Python
  • Focus: Academic introduction with object-oriented design.
  • Why It's Good: Best for those building solid DSA background in Python.
  • Use Case: Use it for university DSA courses or project-based learning.

6. Data Structures and Algorithms in Python

by Goodrich, Tamassia

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Advanced

  • Language: Pseudocode
  • Focus: In-depth theory of every major algorithm.
  • Why It's Good: Comprehensive; used in top universities.
  • Use Case: Read topic-wise when you need depth (e.g., DP, graphs, segment trees).

1. Introduction to Algorithms (CLRS)

by Cormen, Leiserson, Rivest, Stein

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  • Language: Pseudocode
  • Focus: Focus on algorithm design paradigms and problem-solving.
  • Why It's Good: Builds deep understanding of “why” behind algorithms.
  • Use Case: Best for academic preparation or Olympiad-level understanding.

2. Algorithm Design

by Jon Kleinberg, Éva Tardos

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  • Language: Java
  • Focus: Graphs, strings, sorting, and data structures with real applications.
  • Why It's Good: Practical + visual content with Princeton lectures online.
  • Use Case: Ideal if you prefer real-world applications.

3. Algorithms

by Robert Sedgewick, Kevin Wayne

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  • Language: Pseudocode/C++
  • Focus: Real-world heuristics and strategies, not just pure algorithms.
  • Why It's Good: Includes “Hitchhiker’s Guide to Algorithms” – a reference to known problems and their techniques.
  • Use Case: Use for projects, real problem-solving, and contests.

4. The Algorithm Design Manual

by Steven S. Skiena

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  • Language: C
  • Focus: Thinking like a programmer, not just coding.
  • Why It's Good: Develops critical problem-solving habits.
  • Use Case: Excellent for sharpening logical/analytical thinking.

5. Programming Pearls

by Jon Bentley

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  • Language: Pseudocode
  • Focus: Higher-order structures like interval trees, suffix trees, persistent DS.
  • Why It's Good: For those preparing for research or advanced CP.
  • Use Case: Use after CLRS for theory-heavy domains or competitive edge.

6. Advanced Data Structures

by Peter Brass

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AI & Machine Learning

Beginner

  • Language: Python
  • Focus: Applied machine learning using scikit-learn, TensorFlow, and Keras.
  • Why It's Good: Hands-on book with clear explanations, model evaluation techniques, and ML workflows.
  • Use Case: Ideal for beginners wanting to learn coding-based ML workflows and concepts together.

1. Python Machine Learning

by Sebastian Raschka & Vahid Mirjalili

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  • Language: Python
  • Focus: Practical ML & DL with real datasets using sklearn, Keras, and TF.
  • Why It's Good: Covers end-to-end projects and concepts like decision trees, SVMs, and deep learning in one book.
  • Use Case: Perfect for transitioning from ML theory to actual project development.

2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

by Aurélien Géron

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  • Language: Simplified English, no heavy code
  • Focus: Basic ML concepts without math or code complexity.
  • Why It's Good: Very accessible for someone starting from scratch.
  • Use Case: Use it as a warm-up before diving into technical books.

3. Machine Learning for Absolute Beginners

by Oliver Theobald

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  • Language: English (non-technical)
  • Focus: Societal impact, history, and philosophy of AI.
  • Why It's Good: Gives a grounded and skeptical view of modern AI’s limitations and power.
  • Use Case: Great for understanding the AI hype and what's real, before going deep.

4. Artificial Intelligence: A Guide for Thinking Humans

by Melanie Mitchell

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  • Language: Plain English with illustrations and some pseudocode/math
  • Focus: Foundational machine learning concepts explained intuitively and visually.
  • Why It's Good: Uses hand-drawn style diagrams and crystal-clear explanations — even the most complex ML ideas are broken into digestible chunks.
  • Use Case: Great for self-learners without a math-heavy background.

5. The StatQuest Illustrated Guide to Machine Learning

by Josh Starmer

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  • Language: Math (linear algebra, calculus, probability)
  • Focus: Foundational math for understanding ML algorithms.
  • Why It's Good: Bridges the gap between math and its application in ML.
  • Use Case: Use before/while learning advanced ML or Deep Learning.

6. Mathematics for Machine Learning

by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

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Intermediate

  • Language: Concise English with math and pseudocode
  • Focus: A compact but broad overview of core ML concepts and models.
  • Why It's Good: Covers everything from basics to advanced topics (SVMs, neural nets, ensemble learning, etc.) in just ~100 pages.
  • Use Case: Acts as a crash course before interviews, exams, or hackathons..

1. The Hundred-Page Machine Learning Book

by Andriy Burkov

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  • Language: Python (scikit-learn)
  • Focus: Applied ML basics, data preparation, and evaluation.
  • Why It's Good: Teaches the mindset of ML problem-solving using the sklearn ecosystem.
  • Use Case: Ideal for those who want a practical coding introduction with theory.

2. Introduction to Machine Learning with Python

by Andreas Müller & Sarah Guido

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  • Language: Python-based, practical code with simple English
  • Focus: How to structure ML problems in real life, from data prep to model tuning.
  • Why It's Good: Very hands-on and implementation-driven — shows what top Kagglers actually do.
  • Use Case: Ideal for learners ready to move beyond theory into real ML projects or Kaggle competitions.

3. Approaching (Almost) Any Machine Learning Problem

by Abhishek Thakur (Kaggle Grandmaster)

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  • Language: Mathematical formalism with explanations
  • Focus: Probabilistic models, graphical models, Bayesian ML.
  • Why It's Good: Strong focus on mathematical foundations and inference-based ML.
  • Use Case: Best for graduate students, math-lovers, and those doing research or theory-heavy work.

4. Pattern Recognition and Machine Learning

by Christopher Bishop

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  • Language: Simple, plain English
  • Focus: How to design and build successful ML systems.
  • Why It's Good: Not about algorithms — it’s about how to think like an ML engineer.
  • Use Case: Read alongside your ML projects to improve decision-making.

5. Machine Learning Yearning

by Andrew Ng

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  • Language: Math-heavy (probability, graphical models)
  • Focus: Bayesian inference, probabilistic graphical models, EM, variational Bayes.
  • Why It's Good: Emphasizes Bayesian thinking, which is key in modern probabilistic ML.
  • Use Case: Read alongside Bishop’s book or if you're diving into Bayesian deep learning, NLP, or causal inference.

6. Bayesian Reasoning and Machine Learning

by David Barber

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  • Language: R-based code, practical style
  • Focus: Solving real-world problems with ML tools.
  • Why It's Good: Teaches via case studies (spam filtering, stock prediction, recommendation systems).
  • Use Case: Not great for deep ML theory — more for hands-on exploration and hacking.

7. Machine Learning for Hackers

by Drew Conway & John Myles White

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  • Language: Python, Keras-based
  • Focus: Practical deep learning using Keras and TensorFlow.
  • Why It's Good: Explains DL intuitively with clean code examples.
  • Use Case: Perfect first book on deep learning.

8. Deep Learning with Python

by François Chollet (creator of Keras)

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  • Language: Python with PyTorch code
  • Focus: Deep learning principles implemented using PyTorch.
  • Why It's Good: Shows how to build models from scratch (CNNs, RNNs, NLP tasks).
  • Use Case: Read this if you plan to work with PyTorch, especially in research.

9. Deep Learning with PyTorch

by Eli Stevens, Luca Antiga, Thomas Viehmann

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Advanced

  • Language: Math (statistics-heavy)
  • Focus: Advanced statistical learning techniques – boosting, SVMs, ensemble methods.
  • Why It's Good: Deeper and more rigorous than ISLR, essential for theory-based understanding.
  • Use Case: After ISLR or when building a strong research/theory background.

1. The Elements of Statistical Learning (ESL)

by Trevor Hastie, Robert Tibshirani, Jerome Friedman

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  • Language: Math-heavy, with some Python
  • Focus: Foundations of neural networks, CNNs, RNNs, optimization.
  • Why It's Good: Written by deep learning pioneers; covers the theory behind DL.
  • Use Case: Use when getting serious about Deep Learning research.

2. Deep Learning

by Ian Goodfellow, Yoshua Bengio, & Aaron Courville

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  • Language: Math, Python
  • Focus: Probabilistic methods – Bayesian learning, graphical models.
  • Why It's Good: Excellent for learning uncertainty modeling in ML.
  • Use Case: Use when exploring Bayesian or probabilistic ML systems.

3. Probabilistic Machine Learning: An Introduction

by Kevin P. Murphy

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  • Language: Pseudocode + math
  • Focus: Learning from interaction, Q-learning, policy gradients.
  • Why It's Good: It’s the go-to book for RL theory and practice.
  • Use Case: Use when entering RL or game AI after basic ML.

4. Reinforcement Learning: An Introduction

by Richard S. Sutton & Andrew G. Barto

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  • Language: Python + math
  • Focus: How neural nets work from scratch.
  • Why It's Good: Explains the backpropagation algorithm and network design intuitively.
  • Use Case: Read before diving into TensorFlow or PyTorch.

5. Neural Networks and Deep Learning

by Michael Nielsen

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  • Language: Pseudocode + math
  • Focus: Classical AI – search, planning, reasoning, NLP.
  • Why It's Good: The most complete AI textbook used in top universities.
  • Use Case: Best for exploring symbolic AI and foundational principles.

6. Artificial Intelligence: A Modern Approach

by Stuart Russell & Peter Norvig

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Python

Beginner

  • Language: Python
  • Focus: Web APIs, files, and data handling
  • Why It's Good: Friendly intro with real-world use cases
  • Use Case: For absolute beginners to Python programming

Python for Everybody: Exploring Data in Python 3

by Charles Severance

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  • Language: Python
  • Focus: Voice control and speech recognition
  • Why It's Good: Builds interactive Python projects
  • Use Case: Ideal for creating assistive apps or smart tools

Make Python Talk

by Mark H. Liu

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  • Language: Python
  • Focus: Game logic and interactive programming
  • Why It's Good: Fun and educational for logic building
  • Use Case: Beginners interested in coding through games

Invent Your Own Computer Games with Python (4th Edition)

by Al Sweigart

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Intermediate

  • Language: Python
  • Focus: Writing concise and expressive code
  • Why It's Good: Improves fluency with clever Python patterns
  • Use Case: Python developers aiming to write clean, compact code

Python One-Liners

by Christian Mayer

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  • Language: Python
  • Focus: Fun projects using sensors, visuals, math, and sound
  • Why It's Good: Hands-on creative problem solving
  • Use Case: Hobbyists and intermediate learners seeking real-world apps

Python Playground

by Mahesh Venkitachalam

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  • Language: Python
  • Focus: Math + code = visualization + modeling
  • Why It's Good: Combines math understanding with Python application
  • Use Case: Great for students and STEM learners

Doing Math with Python

by Amit Saha

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Advanced

  • Language: Python
  • Focus: Comprehensive Python including classes and GUIs
  • Why It's Good: Uses Head First's visual approach for deeper understanding
  • Use Case: Intermediate users ready to learn advanced OOP and GUI

Head First Python

by Paul Barry

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  • Language: Python
  • Focus: Challenging and creative Python projects
  • Why It's Good: Improves deep problem-solving skills through unique tasks
  • Use Case: Experienced coders who enjoy problem-solving and puzzles

Impractical Python Projects

by Lee Vaughan

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  • Language: Python
  • Focus: Real-world data science workflows and projects
  • Why It's Good: Project-based learning for aspiring data scientists
  • Use Case: Intermediate/advanced learners aiming to enter data science

Data Science Projects with Python

by Stephen Klosterman

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Programming Foundation

C

  • Language: C
  • Focus: Procedural programming, syntax, data types
  • Why It's Good: Simple and comprehensive for beginners
  • Use Case: Ideal first book to learn C

Let Us C

by Yashavant Kanetkar

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  • Language: C
  • Focus: Real-world applications and practical examples
  • Why It's Good: Easy-to-follow guide with clear explanations
  • Use Case: Absolute beginners learning C step-by-step

C Programming Absolute Beginner's Guide

by Greg Perry & Dean Miller

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  • Language: C
  • Focus: Core language design, functions, pointers
  • Why It's Good: Written by C’s creators, it's authoritative
  • Use Case: Best for grasping C’s core logic and syntax deeply

The C Programming Language (2nd Edition)

by Brian W. Kernighan & Dennis M. Ritchie

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  • Language: C
  • Focus: Projects and hands-on examples
  • Why It's Good: Visual and engaging teaching style
  • Use Case: Great for learners who prefer visual learning

Head First C

by David Griffiths & Dawn Griffiths

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C++

  • Language: C++
  • Focus: Syntax, templates, STL
  • Why It's Good: One of the most recommended textbooks for C++
  • Use Case: Core programming and OOP learning

C++ Primer (5th Edition)

by Lippman, Lajoie, Moo

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  • Language: C++
  • Focus: OOP principles, design practices
  • Why It's Good: Written by the creator of C++
  • Use Case: Learning good programming habits with modern C++

Programming: Principles and Practice Using C++

by Bjarne Stroustrup

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  • Language: C++
  • Focus: Introduction to C++ with humor and clarity
  • Why It's Good: Simplifies concepts for absolute beginners
  • Use Case: C++ learners who enjoy relaxed learning

C++ For Dummies (5th Edition)

by Stephen R. Davis

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Java

  • Language: Java
  • Focus: Object-oriented programming
  • Why It's Good: Engaging visual style and beginner-friendly
  • Use Case: Newcomers to Java and OOP

Head First Java (2nd Edition)

by Kathy Sierra & Bert Bates

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  • Language: Java
  • Focus: Core Java and syntax
  • Why It's Good: Well-structured for self-study
  • Use Case: New learners preparing for job interviews

Java: A Beginner's Guide

by Herbert Schildt

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  • Language: Java
  • Focus: Java best practices
  • Why It's Good: Clean and effective Java programming guide
  • Use Case: Intermediate learners building professional apps

Effective Java (3rd Edition)

by Joshua Bloch

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  • Language: Java
  • Focus: Java fundamentals, APIs, GUI basics
  • Why It's Good: Covers practical enterprise development features
  • Use Case: For serious Java learners and backend devs

Core Java Volume I – Fundamentals

by Cay S. Horstmann

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  • Language: Java
  • Focus: All-in-one reference with hundreds of examples
  • Why It's Good: Most comprehensive Java reference available
  • Use Case: Anyone seeking to master Java in-depth

Java: The Complete Reference (11th Edition)

by Herbert Schildt

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Python

  • Language: Python
  • Focus: Automating real-life tasks
  • Why It's Good: Very practical with hands-on exercises
  • Use Case: Beginners who want to automate everyday problems

Automate the Boring Stuff with Python

by Al Sweigart

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  • Language: Python
  • Focus: Foundational programming + projects
  • Why It's Good: Great for self-taught coders
  • Use Case: Beginners transitioning to real-world projects

Python Crash Course

by Eric Matthes

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  • Language: Python
  • Focus: Problem solving + computer science logic
  • Why It's Good: Builds algorithmic thinking
  • Use Case: CS students and beginners wanting theory + practice

Think Python (2nd Edition)

by Allen B. Downey

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  • Language: Python
  • Focus: Writing Pythonic code and mastering the language
  • Why It's Good: Teaches clean, expressive programming
  • Use Case: Intermediate to advanced Python developers

Fluent Python (2nd Edition)

by Luciano Ramalho

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Blockchain

Beginner

  • Language: Non-technical
  • Focus: Basic blockchain principles
  • Why It's Good: Simple explanation with diagrams
  • Use Case: Starting point for non-programmers

Blockchain Basics: A Non-Technical Introduction in 25 Steps

by Daniel Drescher

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  • Language: English
  • Focus: Bitcoin and blockchain concepts
  • Why It's Good: Great mix of business and tech explanations
  • Use Case: Learn crypto basics and terminology

The Basics of Bitcoins and Blockchains

by Antony Lewis

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  • Language: Python
  • Focus: Bitcoin mechanics and transactions
  • Why It's Good: From beginner to semi-technical depth
  • Use Case: For developers entering the Bitcoin world

Mastering Bitcoin (First Half)

by Andreas M. Antonopoulos

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  • Language: English
  • Focus: Cryptography and blockchain fundamentals
  • Why It's Good: Clear and academic with online support
  • Use Case: Great for structured learning

Bitcoin and Cryptocurrency Technologies

by Arvind Narayanan et al.

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Intermediate

  • Language: Python
  • Focus: Bitcoin internals
  • Why It's Good: In-depth, highly respected
  • Use Case: Developers working with Bitcoin

Mastering Bitcoin

by Andreas M. Antonopoulos

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  • Language: English
  • Focus: Societal and business impacts of blockchain
  • Why It's Good: Visionary and strategic
  • Use Case: Understand blockchain beyond coding

Blockchain Revolution

by Don Tapscott & Alex Tapscott

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  • Language: English
  • Focus: Blockchain mechanics and architecture
  • Why It's Good: Covers cryptography, consensus, and smart contracts
  • Use Case: Developers and architects learning blockchain systems

Mastering Blockchain (2nd Edition)

by Imran Bashir

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  • Language: Solidity, Ethereum
  • Focus: Ethereum platform and smart contracts
  • Why It's Good: Technical and practical Ethereum resource
  • Use Case: For developers diving into Ethereum

Mastering Ethereum

by Andreas M. Antonopoulos & Gavin Wood

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  • Language: Python
  • Focus: Blockchain projects in Python
  • Why It's Good: Great for practical learners
  • Use Case: Build real blockchain systems in Python

Hands-On Blockchain for Python Developers

by Arjuna Sky Kok

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Advanced

  • Language: Python & Tools
  • Focus: Blockchain projects and simulations
  • Why It's Good: Real-world implementation style
  • Use Case: Engineers implementing real blockchain workflows

Blockchain in Action

by Bina Ramamurthy

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  • Language: Python
  • Focus: Bitcoin protocol coding
  • Why It's Good: Shows how to build Bitcoin from scratch
  • Use Case: For learners interested in protocol-level implementation

Programming Bitcoin

by Jimmy Song

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  • Language: Architecture
  • Focus: Blockchain solution architecture
  • Why It's Good: Maps blockchain to enterprise needs
  • Use Case: Architects and professionals designing systems

Architecting Blockchain Solutions

by Joseph Holbrook

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CSE Fundamentals

Object-Oriented Programming (OOP)

  • Language: C++
  • Focus: Concepts of classes, inheritance, polymorphism
  • Why It's Good: Comprehensive coverage of C++ OOP concepts
  • Use Case: C++ learners needing strong OOP foundation

Object-Oriented Programming in C++

by Robert Lafore

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  • Language: Java
  • Focus: Advanced Java OOP techniques and patterns
  • Why It's Good: Comprehensive and deep dive into Java OOP
  • Use Case: Intermediate Java programmers

Thinking in Java (4th Edition)

by Bruce Eckel

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  • Language: Language-agnostic
  • Focus: OOP principles and analysis
  • Why It's Good: Engaging, visual explanation of OOP thinking
  • Use Case: Understanding how to design with OOP

Head First Object-Oriented Analysis and Design

by Brett McLaughlin, Gary Pollice, David West

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  • Language: Java
  • Focus: UML, patterns, and Java OOP practices
  • Why It's Good: Bridges OOP concepts with software engineering
  • Use Case: For learners transitioning into real-world software design

Object-Oriented Software Engineering Using UML, Patterns, and Java

by Bernd Bruegge and Allen H. Dutoit

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Database Management Systems (DBMS)

  • Language: SQL
  • Focus: Relational models, design, and queries
  • Why It's Good: Highly adopted in global DBMS courses
  • Use Case: Comprehensive textbook for database fundamentals

Database System Concepts (6th Edition)

by Silberschatz, Korth, Sudarshan

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  • Language: SQL
  • Focus: ER modeling, normalization, and DBMS architecture
  • Why It's Good: Balanced with theory and practice
  • Use Case: Core text for undergrad database courses

Fundamentals of Database Systems

by Elmasri and Navathe

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  • Language: SQL
  • Focus: Query processing, optimization, storage and indexing
  • Why It's Good: Balanced and approachable technical depth
  • Use Case: Ideal for practical database design and execution

Database Management Systems (3rd Edition)

by Raghu Ramakrishnan and Johannes Gehrke

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  • Language: SQL
  • Focus: Step-by-step SQL queries, joins, subqueries, transactions
  • Why It's Good: Practical and accessible for beginners
  • Use Case: Getting hands-on with SQL without prior experience

Learning SQL

by Alan Beaulieu

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Operating Systems (OS)

  • Language: Pseudocode, C-style examples
  • Focus: Processes, memory, file systems, synchronization, virtualization
  • Why It's Good: Most-used OS textbook globally
  • Use Case: Undergrad OS courses and technical interviews

Operating System Concepts

by Silberschatz, Galvin, Gagne

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  • Language: Pseudocode, Java/C
  • Focus: OS design, architecture, distributed systems
  • Why It's Good: Deep theoretical foundation and real-world examples
  • Use Case: Academic courses or deep OS exploration

Modern Operating Systems

by Andrew S. Tanenbaum

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  • Language: C
  • Focus: Virtual memory, file systems, concurrency
  • Why It's Good: Free, accessible, and broken into manageable parts
  • Use Case: Beginner to intermediate OS learners

Operating Systems: Three Easy Pieces (OSTEP)

by Remzi & Andrea Arpaci-Dusseau

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  • Language: Assembly, C, theory
  • Focus: Case studies, OS architecture, embedded and real-time systems
  • Why It's Good: Rich in systems-level detail and applications
  • Use Case: Advanced OS courses or self-learners

Operating Systems: Internals and Design Principles

by William Stallings

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Information System Design (ISD)

  • Focus: Strategic value of IS in organizations
  • Why It's Good: Business-oriented tech thinking
  • Use Case: MIS students or management + tech crossover

Information Systems: A Manager’s Guide to Harnessing Technology

by John Gallaugher

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  • Focus: Condensed intro to SDLC, requirements gathering
  • Why It's Good: Suitable for one-semester courses
  • Use Case: Quick yet thorough intro to ISD

Essentials of Systems Analysis and Design

by Valacich & George

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  • Focus: Human factors, system usability, and modeling
  • Why It's Good: Holistic design with strong visuals
  • Use Case: Courses emphasizing people-centric ISD

System Analysis & Design

by Kendall & Kendall

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  • Focus: Engineering principles for software design, testing, maintenance
  • Why It's Good: Global SE reference across universities
  • Use Case: Core textbook for Software Engineering

Software Engineering (10th Edition)

by Ian Sommerville

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  • Focus: Process models, agile, testing, real-world applications
  • Why It's Good: Highly practical and used in industry-aligned courses
  • Use Case: Ideal for project-heavy SE courses

Software Engineering: A Practitioner’s Approach

by Roger Pressman

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  • Focus: Strategic enterprise systems planning
  • Why It's Good: High-level business + architecture bridge
  • Use Case: For senior-level IS strategy or enterprise architecture courses

Enterprise Architecture as Strategy

by Jeanne Ross, Peter Weill

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  • Focus: System lifecycle, SDLC phases, modeling, documentation
  • Why It's Good: Industry-standard, project-based approach
  • Use Case: Beginners in ISD or business systems

Systems Analysis and Design (12th Edition)

by Scott Tilley & Harry J. Rosenblatt

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  • Focus: Structured, OO, and agile methods
  • Why It's Good: Explains methods clearly with real-world examples
  • Use Case: Systems modeling and team-based design projects

Modern Systems Analysis and Design

by Hoffer, George, Valacich

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  • Focus: Use cases, user stories, diagrams, and implementation
  • Why It's Good: Incorporates iterative and Agile systems design
  • Use Case: Courses on dynamic or modern ISD environments

Systems Analysis and Design in a Changing World

by Satzinger, Jackson, Burd

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Cybersecurity

Beginner

  • Focus: Simple overview of cyber threats, attacks, and safety tips
  • Why It's Good: Easy to read and great entry-level content
  • Use Case: First exposure to cybersecurity concepts

Cybersecurity for Beginners

by Raef Meeuwisse

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  • Focus: Fundamentals of security and network defense
  • Why It's Good: Simple language, broad coverage
  • Use Case: Basic awareness and practice for beginners

Computer Security Basics

by Rick Lehtinen & G.T. Gangemi Sr

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  • Focus: Basics of penetration testing using hands-on tools
  • Why It's Good: Clear steps and real-world labs
  • Use Case: Beginners entering ethical hacking

The Basics of Hacking and Penetration Testing

by Patrick Engebretson

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  • Focus: C programming and memory exploitation techniques
  • Why It's Good: Combines coding and hacking fundamentals
  • Use Case: Beginners with some coding background

Hacking: The Art of Exploitation (2nd Edition)

by Jon Erickson

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  • Focus: Core network security principles
  • Why It's Good: Written by a networking authority
  • Use Case: Learners bridging networking and security

Network Security Essentials

by William Stallings

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Intermediate

  • Focus: Blue team defensive techniques
  • Why It's Good: Quick command references for defenders
  • Use Case: Blue team members or incident responders

Blue Team Field Manual (BTFM)

by Alan J. White & Ben Clark

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  • Focus: Web app vulnerabilities, testing, and exploits
  • Why It's Good: Deep dive into web hacking
  • Use Case: Intermediate pentesters and bug bounty hunters

The Web Application Hacker’s Handbook

by Dafydd Stuttard & Marcus Pinto

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  • Focus: Malware analysis using disassemblers and debuggers
  • Why It's Good: Practical, hands-on malware investigation
  • Use Case: Analysts working with malware in real environments

Practical Malware Analysis

by Michael Sikorski & Andrew Honig

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  • Focus: Cryptography systems and algorithms
  • Why It's Good: Covers both theory and real applications
  • Use Case: Security engineers and CS students

Applied Cryptography

by Bruce Schneier

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  • Focus: Security practices in Linux environments
  • Why It's Good: Written with ethical hackers in mind
  • Use Case: Red teamers and Linux users

Linux Basics for Hackers

by OccupyTheWeb

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Advanced

  • Focus: Red team and advanced penetration scenarios
  • Why It's Good: Full simulation-style attack guides
  • Use Case: Experienced ethical hackers

The Hacker Playbook 3

by Peter Kim

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  • Focus: Offensive Python scripts for hacking
  • Why It's Good: Real-world Python use for black hat tasks
  • Use Case: Developers in offensive security

Black Hat Python

by Justin Seitz

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  • Focus: Memory forensics using Volatility
  • Why It's Good: In-depth, practical memory inspection
  • Use Case: Forensic investigators and incident responders

The Art of Memory Forensics

by Michael Hale Ligh et al

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  • Focus: Advanced network and system hacking
  • Why It's Good: Penetration testing beyond common techniques
  • Use Case: Experts targeting hardened systems

Advanced Penetration Testing

by Wil Allsopp

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  • Focus: Secure system design principles
  • Why It's Good: Comprehensive and academic-level
  • Use Case: Security architects, policy makers

Security Engineering (3rd Edition)

by Ross Anderson

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Data Science

Beginner

  • Focus: Data wrangling with Pandas, NumPy, IPython
  • Why It's Good: Real-world data analysis examples
  • Use Case: Beginners using Python for structured data

Python for Data Analysis

by Wes McKinney

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  • Focus: Data science fundamentals from the ground up
  • Why It's Good: Emphasizes building everything from scratch
  • Use Case: Learners who want to code data science logic manually

Data Science from Scratch

by Joel Grus

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  • Focus: Statistical thinking for data analysis
  • Why It's Good: Practical intro to stats in Python
  • Use Case: Statistics primer for data-driven work

Think Stats

by Allen B. Downey

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  • Focus: Philosophy and workflow of doing data science
  • Why It's Good: Concise and thoughtful perspective
  • Use Case: Getting oriented with real-world data challenges

The Art of Data Science

by Roger D. Peng & Elizabeth Matsui

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  • Focus: Communicating data insights effectively
  • Why It's Good: Strong on visual storytelling
  • Use Case: Analysts and business professionals

Storytelling with Data

by Cole Nussbaumer Knaflic

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Intermediate

  • Focus: Statistics for data science through real-world examples
  • Why It's Good: Free textbook with clean R/Python integration
  • Use Case: Learning statistical modeling in applied settings

ISLP (Introduction to Statistical Learning)

by Gareth James et al.

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  • Focus: Stats techniques tailored for data science
  • Why It's Good: Excellent overview of practical statistical tools
  • Use Case: Data analysts and ML learners

Practical Statistics for Data Scientists

by Peter Bruce et al.

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  • Focus: Machine learning with Python, Keras, TensorFlow
  • Why It's Good: Clear and practical ML workflows
  • Use Case: Intermediate data scientists and ML engineers

Hands-On Machine Learning

by Aurélien Géron

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  • Focus: Data preparation and transformation for ML
  • Why It's Good: Practical guide to boosting model performance
  • Use Case: ML practitioners focused on feature pipelines

Feature Engineering for Machine Learning

by Alice Zheng

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  • Focus: Using data science for decision-making
  • Why It's Good: Business-focused explanation of data science logic
  • Use Case: Business leaders and aspiring data strategists

Data Science for Business

by Foster Provost & Tom Fawcett

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Advanced

  • Focus: Statistical theory and learning algorithms
  • Why It's Good: Comprehensive theoretical foundation
  • Use Case: Researchers and data science grad students

The Elements of Statistical Learning

by Hastie, Tibshirani, Friedman

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  • Focus: Deep learning concepts and architecture
  • Why It's Good: Written by field pioneers
  • Use Case: Deep learning students and researchers

Deep Learning

by Ian Goodfellow, Yoshua Bengio, Aaron Courville

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  • Focus: Design patterns for building large-scale data apps
  • Why It's Good: Explains tradeoffs and architecture clearly
  • Use Case: Advanced developers and data engineers

Designing Data-Intensive Applications

by Martin Kleppmann

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  • Focus: Natural language processing using Python
  • Why It's Good: Practical guide using NLTK library
  • Use Case: NLP developers and language modelers

NLTK

by Steven Bird, Ewan Klein, and Edward Loper

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  • Focus: Full-stack web apps for data science
  • Why It's Good: Combines modern APIs and Python deployment
  • Use Case: Python devs building data apps with FastAPI

Building Data Science Applications with FastAPI

by François Voron

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DevOps

Beginner

  • Focus: DevOps through an engaging fictional story
  • Why It's Good: Provides insight into IT and DevOps culture
  • Use Case: Beginners understanding DevOps mindset and workflow

The Phoenix Project

by Gene Kim, Kevin Behr, George Spafford

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  • Focus: DevOps practices, automation, lean management
  • Why It's Good: Comprehensive guide from industry leaders
  • Use Case: Foundation for learning modern DevOps techniques

The DevOps Handbook

by Gene Kim, Jez Humble, Patrick Debois, John Willis

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  • Focus: Key DevOps principles and real-world application
  • Why It's Good: Hands-on perspective with practical examples
  • Use Case: Beginners getting familiar with DevOps lifecycle

Learning DevOps

by Mikael Krief

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  • Focus: Performance metrics and high-performing teams
  • Why It's Good: Research-backed and concise framework
  • Use Case: Leaders and teams aiming to adopt DevOps best practices

Accelerate

by Nicole Forsgren, Jez Humble, Gene Kim

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  • Focus: Introduction to AWS cloud computing services
  • Why It's Good: Hands-on and beginner-friendly AWS guide
  • Use Case: Beginners getting started with cloud platforms

Learning Amazon Web Services (AWS)

by Mark Wilkins

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  • Focus: Core principles of cloud technology and architecture
  • Why It's Good: Conceptual clarity without vendor bias
  • Use Case: Foundation for understanding cloud in DevOps

Cloud Computing: Concepts, Technology & Architecture

by Thomas Erl

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  • Focus: AWS certification concepts and best practices
  • Why It's Good: Aligned with certification requirements
  • Use Case: Preparing for AWS Solutions Architect certification

AWS Certified Solutions Architect Study Guide (3rd Ed.)

by Ben Piper, David Clinton

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  • Focus: Building production-ready, resilient systems
  • Why It's Good: Focused on real-world stability practices
  • Use Case: Developers and ops engineers designing robust systems

Release It!

by Michael T. Nygard

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Intermediate

  • Focus: Container fundamentals and Docker architecture
  • Why It's Good: In-depth, example-driven Docker reference
  • Use Case: Intermediate users mastering containerization

Docker Deep Dive

by Nigel Poulton

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  • Focus: Kubernetes orchestration, services, and clusters
  • Why It's Good: Hands-on deployment and debugging tips
  • Use Case: Running and scaling containerized apps

Kubernetes: Up and Running (3rd Edition)

by Kelsey Hightower, Brendan Burns, Joe Beda

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  • Focus: Infrastructure automation using Terraform
  • Why It's Good: Real-world cloud automation use cases
  • Use Case: Managing cloud infra as code

Terraform: Up & Running

by Yevgeniy Brikman

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  • Focus: End-to-end continuous delivery pipeline
  • Why It's Good: Covers practices, tools, testing, integration
  • Use Case: Intermediate teams improving delivery speed

Continuous Delivery

by Jez Humble, David Farley

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  • Focus: Architecture on Google Cloud Platform
  • Why It's Good: Real-world GCP examples and design patterns
  • Use Case: GCP developers and cloud architects

Google Cloud Platform for Architects

by Vitthal Srinivasan, Valliappa Lakshmanan

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  • Focus: DevOps culture with Kubernetes tooling
  • Why It's Good: Mixes philosophy and Kubernetes operations
  • Use Case: Scaling DevOps with cloud-native tools

Cloud Native DevOps with Kubernetes

by John Arundel, Justin Domingus

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  • Focus: Automating deployment and configuration
  • Why It's Good: Step-by-step infrastructure provisioning
  • Use Case: DevOps engineers using Ansible for automation

Ansible for DevOps

by Jeff Geerling

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Advanced

  • Focus: Principles of reliability and system operation at scale
  • Why It's Good: Written by Google engineers with real-world practices
  • Use Case: Advanced SRE and DevOps engineers managing critical systems

Site Reliability Engineering

by Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Richard Murphy

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  • Focus: Practical SRE examples and implementations
  • Why It's Good: Companion to SRE book with more playbooks and scenarios
  • Use Case: SRE teams refining their reliability practices

The Site Reliability Workbook

by Betsy Beyer, Niall Richard Murphy, David K. Rensin, Kent Kawahara, Stephen Thorne

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  • Focus: Monitoring and metrics with Prometheus
  • Why It's Good: Detailed guide with production-level examples
  • Use Case: Building robust observability systems

Prometheus: Up & Running

by Brian Brazil

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  • Focus: Infrastructure as code strategies and automation
  • Why It's Good: Thorough coverage of IaC concepts and tools
  • Use Case: Advanced DevOps automation and infrastructure modeling

Infrastructure as Code (2nd Edition)

by Kief Morris

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Full-Stack Development

Beginner

  • Language: HTML & CSS
  • Focus: HTML and CSS layout, styling, structure
  • Why It's Good: Visually rich and beginner-friendly
  • Use Case: Absolute beginners learning web structure and style

HTML and CSS: Design and Build Websites

by Jon Duckett

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  • Language: JavaScript
  • Focus: JavaScript and jQuery for dynamic web interaction
  • Why It's Good: Companion to Duckett’s HTML/CSS book
  • Use Case: Beginners adding interactivity to websites

JavaScript and JQuery: Interactive Front-End Web Development

by Jon Duckett

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  • Language: PHP/MySQL
  • Focus: Intro to PHP and MySQL for web development
  • Why It's Good: Beginner-friendly PHP and backend logic
  • Use Case: Building dynamic web apps with PHP/MySQL

PHP & MySQL: Novice to Ninja (6th Edition)

by Tom Butler & Kevin Yank

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  • Language: JavaScript
  • Focus: Core JavaScript fundamentals
  • Why It's Good: Modern best practices and clarity
  • Use Case: Beginners with some programming background

Eloquent JavaScript (3rd Edition)

by Marijn Haverbeke

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  • Language: Full Stack (PHP, MySQL, JavaScript)
  • Focus: Full web stack with PHP, MySQL, JavaScript, jQuery, HTML5, CSS
  • Why It's Good: All-in-one book for frontend + backend fundamentals
  • Use Case: Beginners wanting full-stack overview in one book

Learning PHP, MySQL & JavaScript (5th Edition)

by Robin Nixon

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Intermediate

  • Language: JavaScript (React)
  • Focus: Real-world full-stack React projects
  • Why It's Good: Practical, hands-on React app examples
  • Use Case: Intermediate developers building full React apps

Full-Stack React Projects

by Shama Hoque

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  • Language: JavaScript (Node.js)
  • Focus: Scalable design with Node.js patterns
  • Why It's Good: Introduces patterns, anti-patterns, and refactoring
  • Use Case: Writing maintainable Node.js backends

Node.js Design Patterns

by Mario Casciaro & Luciano Mammino

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  • Language: JavaScript (React)
  • Focus: Deep understanding of JavaScript
  • Why It's Good: Challenges common JS misconceptions
  • Use Case: JavaScript developers improving fluency

You Don’t Know JS (book series)

by Kyle Simpson

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  • Language: REST/HTTP (Conceptual)
  • Focus: REST API principles and best practices
  • Why It's Good: Concise, practical REST API guidance
  • Use Case: Developers designing clean REST APIs

REST API Design Rulebook

by Mark Masse

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  • Language: JavaScript (React)
  • Focus: Practical React development with real examples
  • Why It's Good: Straightforward and beginner-friendly for React
  • Use Case: Learning React with focus on real app features

The Road to React

by Robin Wieruch

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Advanced

  • Language: Multi-language Concepts (Java, SQL, etc.)
  • Focus: Distributed systems and data-centric architecture
  • Why It's Good: Real-world design tradeoffs and scalability patterns
  • Use Case: Designing robust backend systems

Designing Data-Intensive Applications

by Martin Kleppmann

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  • Language: Conceptual (Architecture)
  • Focus: Software architecture tradeoffs and decisions
  • Why It's Good: Detailed breakdown of hard-to-model system concerns
  • Use Case: Architects tackling complexity in large systems

Software Architecture: The Hard Parts

by Neal Ford, Mark Richards

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  • Language: General Programming Concepts
  • Focus: Practical programming principles and long-term thinking
  • Why It's Good: Insightful, timeless developer advice
  • Use Case: All-level devs refining professional practices

The Pragmatic Programmer

by Andrew Hunt & David Thomas

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  • Language: JavaScript (React), GraphQL
  • Focus: Modern full-stack development using serverless tools
  • Why It's Good: Covers React, AWS, and GraphQL in practice
  • Use Case: Building scalable apps without server management

Full-Stack Serverless

by Nader Dabit

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  • Language: General (Java, REST, JSON)
  • Focus: Microservices design, architecture, and implementation
  • Why It's Good: In-depth principles for distributed systems
  • Use Case: Teams moving from monoliths to microservices

Building Microservices (2nd Edition)

by Sam Newman

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  • Language: General (Java, C#, etc.)
  • Focus: Clean architecture and SOLID principles
  • Why It's Good: Time-tested architectural guidance
  • Use Case: Developers structuring maintainable software systems

Clean Architecture

by Robert C. Martin (Uncle Bob)

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Git & GitHub