⏱️ Big-O Analysis

A foundational concept in computer science, these books teach readers to evaluate algorithm efficiency by understanding how performance scales with input size.

Data Structures and Algorithms Made Easy

by Narasimha Karumanchi • 2011

Every algorithm problem has multiple solutions. Find the fastest one, step by step.

4.10

1k+

⏱️ Algorithm Analysis 🗃️ Data Structures ✍️ Algorithm Design 📊 Complexity Theory 💼 Coding Interviews

Data Structures and Algorithms Made Easy Summary

Data Structures and Algorithms Made Easy 91%

Every algorithm problem has multiple solutions. Find the fastest one, step by step.

by Narasimha Karumanchi 2011

4.10

1k+ ratings

⏱️ Algorithm Analysis 🗃️ Data Structures ✍️ Algorithm Design 📊 Complexity Theory 💼 Coding Interviews

Cracking the Coding Interview

by Gayle Laakmann McDowell • 2020

189 interview problems that teach you to think out loud, because code follows from thinking.

4.33

476

🧑‍💻 Coding Interviews 🧠 Algorithm Problems 🗂️ Data Structures 🧑‍🏫 New Engineers ⏱️ Big O Notation

Cracking the Coding Interview 90%

189 interview problems that teach you to think out loud, because code follows from thinking.

by Gayle Laakmann McDowell 2020

4.33

476 ratings

🧑‍💻 Coding Interviews 🧠 Algorithm Problems 🗂️ Data Structures 🧑‍🏫 New Engineers ⏱️ Big O Notation

Algorithm Design

by Jon Kleinberg • 2005

Design algorithms that run fast; recognize the problems that never will; and learn the workarounds.

4.17

664

🧮 Algorithm Design ⏱️ Computational Complexity 🕸️ Graph Theory 🧩 Dynamic Programming 🚫 Np Completeness

Algorithm Design 90%

Design algorithms that run fast; recognize the problems that never will; and learn the workarounds.

by Jon Kleinberg 2005

4.17

664 ratings

🧮 Algorithm Design ⏱️ Computational Complexity 🕸️ Graph Theory 🧩 Dynamic Programming 🚫 Np Completeness

Algorithms

by Panos Louridas • 2020

Twenty steps to find one item in a million, and the hidden logic that runs our world.

4.03

302

📐 Algorithm Design 💻 Computing Primers 🕸️ Graph Theory 🔍 Search Algorithms 📊 Data Sorting

Algorithms 90%

Twenty steps to find one item in a million, and the hidden logic that runs our world.

by Panos Louridas 2020

4.03

302 ratings

📐 Algorithm Design 💻 Computing Primers 🕸️ Graph Theory 🔍 Search Algorithms 📊 Data Sorting

Introduction to Algorithms

by Thomas H. Cormen • 1989

Sorting a billion records can take days or seconds, depending on the algorithm, not the processor.

4.35

9k+

📘 Algorithms 🗂️ Data Structures 📊 Algorithm Analysis ✂️ Divide And Conquer 🎲 Randomized Algorithms

Introduction to Algorithms 90%

Sorting a billion records can take days or seconds, depending on the algorithm, not the processor.

by Thomas H. Cormen 1989

4.35

9k+ ratings

📘 Algorithms 🗂️ Data Structures 📊 Algorithm Analysis ✂️ Divide And Conquer 🎲 Randomized Algorithms

Data Structures and Algorithms

by Alfred V. Aho • 1983

Write code that scales: the classic mathematical toolkit for reasoning about speed before you run.

3.93

245

🧱 Data Structures ⏱️ Algorithm Analysis 🔨 Algorithm Design 💡 Language-Agnostic Programming 🔢 Sorting Algorithms

Data Structures and Algorithms 90%

Write code that scales: the classic mathematical toolkit for reasoning about speed before you run.

by Alfred V. Aho 1983

3.93

245 ratings

🧱 Data Structures ⏱️ Algorithm Analysis 🔨 Algorithm Design 💡 Language-Agnostic Programming 🔢 Sorting Algorithms

The Art of Computer Programming, Volume 1

by Donald Ervin Knuth • 1973

Algorithms are not just code: they are mathematical objects. To understand one, you must prove it.

4.38

2k+

🧮 Algorithm Design 🗂️ Data Structures 📈 Algorithm Analysis 📐 Mathematical Analysis 🔧 Low-Level Computing

The Art of Computer Programming, Volume 1 Summary

The Art of Computer Programming, Volume 1 90%

Algorithms are not just code: they are mathematical objects. To understand one, you must prove it.

by Donald Ervin Knuth 1973

4.38

2k+ ratings

🧮 Algorithm Design 🗂️ Data Structures 📈 Algorithm Analysis 📐 Mathematical Analysis 🔧 Low-Level Computing

Programming Interviews Exposed

by John Mongan • 2000

The data structures coding interviews reuse, and the method for talking through your work.

3.96

1k+

🖥️ Coding Interviews 💻 CS Fundamentals 📊 Data Structures 🔢 Algorithm Design 🚀 Code Optimization

Programming Interviews Exposed 90%

The data structures coding interviews reuse, and the method for talking through your work.

by John Mongan 2000

3.96

1k+ ratings

🖥️ Coding Interviews 💻 CS Fundamentals 📊 Data Structures 🔢 Algorithm Design 🚀 Code Optimization

A Common-Sense Guide to Data Structures and Algorithms

by Jay Wengrow • 2017

The algorithm knowledge that turns working code into fast code, explained without the math degree.

4.39

603

🧱 Data Structures ⏱️ Algorithm Analysis 🧠 Algorithmic Thinking 🔧 Practical Algorithms ⚡ Performance Optimization

A Common-Sense Guide to Data Structures and Algorithms Summary

A Common-Sense Guide to Data Structures and Algorithms 90%

The algorithm knowledge that turns working code into fast code, explained without the math degree.

by Jay Wengrow 2017

4.39

603 ratings

🧱 Data Structures ⏱️ Algorithm Analysis 🧠 Algorithmic Thinking 🔧 Practical Algorithms ⚡ Performance Optimization

Computer Science Distilled

by Wladston Ferreira Filho • 2017

The missing CS toolkit for self-taught coders: how to think in algorithms, not just syntax.

4.07

1k+

🧠 Computational Thinking 🧮 Algorithm Design 🗂️ Data Structures 📊 Complexity Analysis 🎲 Probability Theory

Computer Science Distilled 90%

The missing CS toolkit for self-taught coders: how to think in algorithms, not just syntax.

by Wladston Ferreira Filho 2017

4.07

1k+ ratings

🧠 Computational Thinking 🧮 Algorithm Design 🗂️ Data Structures 📊 Complexity Analysis 🎲 Probability Theory

Data Structures and Algorithms in Java

by Michael T. Goodrich • 1998

Data structures and their runtime analysis: how to write Java code that is efficient and dependable.

3.86

302

🧱 Data Structures ⚙️ Algorithms 🏗️ Algorithm Design 📈 Algorithm Analysis 🧩 Software Design

Data Structures and Algorithms in Java Summary

Data Structures and Algorithms in Java 89%

Data structures and their runtime analysis: how to write Java code that is efficient and dependable.

by Michael T. Goodrich 1998

3.86

302 ratings

🧱 Data Structures ⚙️ Algorithms 🏗️ Algorithm Design 📈 Algorithm Analysis 🧩 Software Design

Data Structures and Algorithms Made Easy in Java

by Narasimha Karumanchi • 2011

Data structures and algorithms, taught problem by problem in Java. Built for the interview.

4.17

471

🧱 Data Structures 🔧 Algorithm Design 💼 Coding Interviews ☕ Java Programming 📚 Cs Fundamentals

Data Structures and Algorithms Made Easy in Java Summary

Data Structures and Algorithms Made Easy in Java 89%

Data structures and algorithms, taught problem by problem in Java. Built for the interview.

by Narasimha Karumanchi 2011

4.17

471 ratings

🧱 Data Structures 🔧 Algorithm Design 💼 Coding Interviews ☕ Java Programming 📚 Cs Fundamentals

Introduction to Computation and Programming Using Python

by John V. Guttag • 2013

A computer calculates and remembers; everything beyond that is what you build with code.

4.22

500

🧑‍💻 Introductory Programming 🧠 Computational Thinking 🐍 Python For Beginners 📐 Algorithm Design 🗃️ Data Structures

Introduction to Computation and Programming Using Python Summary

Introduction to Computation and Programming Using Python 89%

A computer calculates and remembers; everything beyond that is what you build with code.

by John V. Guttag 2013

4.22

500 ratings

🧑‍💻 Introductory Programming 🧠 Computational Thinking 🐍 Python For Beginners 📐 Algorithm Design 🗃️ Data Structures

Grokking Algorithms An Illustrated Guide For Programmers and Other Curious People

by Aditya Y. Bhargava • 2015

Algorithms stripped of dense math: a hand-drawn guide to thinking in runtimes and data structures.

4.41

5k+

🔢 Algorithm Fundamentals 🧱 Data Structures 🔍 Sorting And Searching 🕸️ Graph Algorithms ✏️ Illustrated Guides

Grokking Algorithms An Illustrated Guide For Programmers and Other Curious People Summary

Grokking Algorithms An Illustrated Guide For Programmers and Other Curious People 89%

Algorithms stripped of dense math: a hand-drawn guide to thinking in runtimes and data structures.

by Aditya Y. Bhargava 2015

4.41

5k+ ratings

🔢 Algorithm Fundamentals 🧱 Data Structures 🔍 Sorting And Searching 🕸️ Graph Algorithms ✏️ Illustrated Guides

The Algorithm Design Manual

by Steven S. Skiena • 1997

The first step is modeling, not coding. A practical manual and its 75-problem catalog show how.

4.34

3k+

🧩 Algorithm Design 🗃️ Data Structures 🕸️ Graph Algorithms 🔢 Dynamic Programming 🛠️ Practical Algorithms

The Algorithm Design Manual 89%

The first step is modeling, not coding. A practical manual and its 75-problem catalog show how.

by Steven S. Skiena 1997

4.34

3k+ ratings

🧩 Algorithm Design 🗃️ Data Structures 🕸️ Graph Algorithms 🔢 Dynamic Programming 🛠️ Practical Algorithms

Data Structures and Problem Solving Using Java

by Mark Allen Weiss • 2009

Coding tricks lose to growth rates. The data structures and guarantees that survive at scale.

3.58

🏗️ Data Structures 🧠 Algorithmic Thinking 🕸️ Graph Algorithms 🔄 Recursion 🧱 Object Oriented Design

Data Structures and Problem Solving Using Java Summary

Data Structures and Problem Solving Using Java 89%

Coding tricks lose to growth rates. The data structures and guarantees that survive at scale.

by Mark Allen Weiss 2009

3.58

36 ratings

🏗️ Data Structures 🧠 Algorithmic Thinking 🕸️ Graph Algorithms 🔄 Recursion 🧱 Object Oriented Design

Python Programming

by John Zelle • 2003

A Python-first CS textbook that teaches what computation can do, not just how to code.

4.02

483

📘 Introductory Programming 🐍 Python Programming 🧮 Algorithm Fundamentals 🏗️ Object-Oriented Design 🔄 Software Development

Python Programming 88%

A Python-first CS textbook that teaches what computation can do, not just how to code.

by John Zelle 2003

4.02

483 ratings

📘 Introductory Programming 🐍 Python Programming 🧮 Algorithm Fundamentals 🏗️ Object-Oriented Design 🔄 Software Development

Once Upon an Algorithm

by Martin Erwig • 2017

What Hansel and Gretel, Sherlock Holmes, and Groundhog Day can teach you about algorithms.

3.63

265

🧠 Computational Thinking 📐 Algorithm Design 📖 Story-Driven Education 🔓 Accessible Algorithms 🗄️ Data Structures

Once Upon an Algorithm 88%

What Hansel and Gretel, Sherlock Holmes, and Groundhog Day can teach you about algorithms.

by Martin Erwig 2017

3.63

265 ratings

🧠 Computational Thinking 📐 Algorithm Design 📖 Story-Driven Education 🔓 Accessible Algorithms 🗄️ Data Structures

Think Like a Programmer

by V. Anton Spraul • 2012

A method for the part of programming no one teaches: turning ideas into code.

3.87

591

🧠 Algorithmic Thinking 📚 Programming Pedagogy ➗ Divide And Conquer 🗺️ Self-Directed Learning 📐 Planning And Design

Think Like a Programmer 88%

A method for the part of programming no one teaches: turning ideas into code.

by V. Anton Spraul 2012

3.87

591 ratings

🧠 Algorithmic Thinking 📚 Programming Pedagogy ➗ Divide And Conquer 🗺️ Self-Directed Learning 📐 Planning And Design

Weapons of Math Destruction

by Cathy O'Neil • 2016

How the algorithms that score your life lock in poverty, deepen racism, and erode democracy.

3.87

30k+

🧩 Algorithmic Bias 📊 Big Data Ethics 🔒 Data Privacy 👮 Predictive Policing ⚖️ Algorithmic Accountability

Weapons of Math Destruction 88%

How the algorithms that score your life lock in poverty, deepen racism, and erode democracy.

by Cathy O'Neil 2016

3.87

30k+ ratings

🧩 Algorithmic Bias 📊 Big Data Ethics 🔒 Data Privacy 👮 Predictive Policing ⚖️ Algorithmic Accountability

Hadoop

by Tom White • 2009

How to turn cheap servers into a petabyte-scale data platform, from HDFS to real-time streaming.

3.93

1k+

📊 Big Data 🖧 Distributed Systems 🐘 Hadoop Ecosystem ⚙️ Data Engineering 🔗 Data Pipelines

Hadoop 88%

How to turn cheap servers into a petabyte-scale data platform, from HDFS to real-time streaming.

by Tom White 2009

3.93

1k+ ratings

📊 Big Data 🖧 Distributed Systems 🐘 Hadoop Ecosystem ⚙️ Data Engineering 🔗 Data Pipelines

Mastering Regular Expressions

by Jeffrey E.F. Friedl • 1997

Understand the engine behind your regex: why some patterns crawl, and how to make them run.

4.16

2k+

🧩 Regular Expressions 📝 Text Processing 🔍 Pattern Matching ⚡ Code Optimization 🤖 Automata Theory

Mastering Regular Expressions 88%

Understand the engine behind your regex: why some patterns crawl, and how to make them run.

by Jeffrey E.F. Friedl 1997

4.16

2k+ ratings

🧩 Regular Expressions 📝 Text Processing 🔍 Pattern Matching ⚡ Code Optimization 🤖 Automata Theory

Numsense! Data Science for the Layman

by Annalyn Ng • 2017

Data science without the math: twelve techniques, what they do, and why intuition alone falls short.

4.14

622

🧠 Data Literacy 🤖 Machine Learning Basics 📈 Predictive Analytics 📊 Data-Driven Decisions 📘 Technical Primers

Numsense! Data Science for the Layman Summary

Numsense! Data Science for the Layman 88%

Data science without the math: twelve techniques, what they do, and why intuition alone falls short.

by Annalyn Ng 2017

4.14

622 ratings

🧠 Data Literacy 🤖 Machine Learning Basics 📈 Predictive Analytics 📊 Data-Driven Decisions 📘 Technical Primers

Thinking Better

by Marcus du Sautoy • 2021

Mathematical shortcuts that save time, solve problems, and show you what everyone else overlooks.

3.59

618

🧮 Mathematical Thinking 🧠 Mental Shortcuts 🔍 Pattern Recognition ⚙️ Optimization 👁️ Visual Thinking

Thinking Better 88%

Mathematical shortcuts that save time, solve problems, and show you what everyone else overlooks.

by Marcus du Sautoy 2021

3.59

618 ratings

🧮 Mathematical Thinking 🧠 Mental Shortcuts 🔍 Pattern Recognition ⚙️ Optimization 👁️ Visual Thinking

AI for Data Science

by Zacharias Voulgaris • 2018

The real AI toolkit goes beyond deep learning: optimization, fuzzy logic, ensembles.

4.55

📊 Data Science 🧠 Deep Learning ⚙️ Optimization Algorithms 🤝 Ensemble Methods 📈 Big Data Analytics

AI for Data Science 88%

The real AI toolkit goes beyond deep learning: optimization, fuzzy logic, ensembles.

by Zacharias Voulgaris 2018

4.55

20 ratings

📊 Data Science 🧠 Deep Learning ⚙️ Optimization Algorithms 🤝 Ensemble Methods 📈 Big Data Analytics

Graph Databases

by Ian Robinson • 2013

Relational databases fail on connected data. Graph databases traverse deep links in milliseconds.

3.64

437

🕸️ Graph Databases 🗄️ Nosql Databases 📐 Graph Theory 🔗 Network Analysis 🏗️ Data Architecture

Graph Databases 88%

Relational databases fail on connected data. Graph databases traverse deep links in milliseconds.

by Ian Robinson 2013

3.64

437 ratings

🕸️ Graph Databases 🗄️ Nosql Databases 📐 Graph Theory 🔗 Network Analysis 🏗️ Data Architecture

Algorithms to Live By

by Brian Christian • 2016

When to settle, when to quit, when to trust chance: computer science already found the answers.

4.12

35k+

🧠 Algorithmic Thinking 🔬 Decision Science 📊 Bayesian Reasoning 🧩 Heuristics 💭 Overthinking

Algorithms to Live By 87%

When to settle, when to quit, when to trust chance: computer science already found the answers.

by Brian Christian 2016

4.12

35k+ ratings

🧠 Algorithmic Thinking 🔬 Decision Science 📊 Bayesian Reasoning 🧩 Heuristics 💭 Overthinking

Object-oriented Programming with C++

by E. Balagurusamy • 1994

Classes, inheritance, templates: C++ tools for building software that scales without falling apart.

3.98

278

🧱 Object Orientation 💻 C++📐 Oop Principles 🧩 Generic Programming 💾 Memory Management

Object-oriented Programming with C++ Summary

Object-oriented Programming with C++ 87%

Classes, inheritance, templates: C++ tools for building software that scales without falling apart.

by E. Balagurusamy 1994

3.98

278 ratings

🧱 Object Orientation 💻 C++📐 Oop Principles 🧩 Generic Programming 💾 Memory Management

Python for Data Analysis

by Oscar Scratch • 2019

Python data analysis, taught through questions: NumPy, Pandas, and the bridge to machine learning.

3.50

🧮 Scientific Computing 🔄 Data Manipulation 📊 Data Visualization 🐍 Introductory Programming 📈 Beginner Data Science

Python for Data Analysis 87%

Python data analysis, taught through questions: NumPy, Pandas, and the bridge to machine learning.

by Oscar Scratch 2019

3.50

2 ratings

🧮 Scientific Computing 🔄 Data Manipulation 📊 Data Visualization 🐍 Introductory Programming 📈 Beginner Data Science

Object-Oriented Software Construction

by Bertrand Meyer • 1988

Before running code, define the contract: how agreements between objects produce reliable software.

4.04

271

🏗️ Object-Oriented Design 🏛️ Software Architecture ✅ Software Correctness ♻️ Code Reusability 🧱 Modular Design

Object-Oriented Software Construction Summary

Object-Oriented Software Construction 87%

Before running code, define the contract: how agreements between objects produce reliable software.

by Bertrand Meyer 1988

4.04

271 ratings

🏗️ Object-Oriented Design 🏛️ Software Architecture ✅ Software Correctness ♻️ Code Reusability 🧱 Modular Design

Game Programming Patterns

by Robert Nystrom • 2011

The programming patterns that make game code resilient to change, and the ones that don't.

4.49

2k+

🎮 Game Programming 🧩 Design Patterns 🏗️ Software Architecture ⚡ Performance Optimization 🔗 Decoupling

Game Programming Patterns 87%

The programming patterns that make game code resilient to change, and the ones that don't.

by Robert Nystrom 2011

4.49

2k+ ratings

🎮 Game Programming 🧩 Design Patterns 🏗️ Software Architecture ⚡ Performance Optimization 🔗 Decoupling

Algorithms + Data Structures = Programs

by Niklaus Wirth • 1975

Programs are just data structures shaped by algorithms. Master both, and the rest follows.

4.24

207

🔧 Software Engineering 🧮 Data Structures And Algorithms 📐 Structured Programming 🧩 Modular Design 🐛 Debugging Practices

Algorithms + Data Structures = Programs Summary

Algorithms + Data Structures = Programs 87%

Programs are just data structures shaped by algorithms. Master both, and the rest follows.

by Niklaus Wirth 1975

4.24

207 ratings

🔧 Software Engineering 🧮 Data Structures And Algorithms 📐 Structured Programming 🧩 Modular Design 🐛 Debugging Practices

Bursts

by Albert-László Barabási • 2010

Why your email bursts, your travels repeat, and history rhymes: one hidden law of priority.

3.30

1k+

🕸️ Complex Systems 🔗 Network Science 📉 Power Laws ✅ Task Prioritization 🔐 Data And Privacy

Bursts 87%

Why your email bursts, your travels repeat, and history rhymes: one hidden law of priority.

by Albert-László Barabási 2010

3.30

1k+ ratings

🕸️ Complex Systems 🔗 Network Science 📉 Power Laws ✅ Task Prioritization 🔐 Data And Privacy

Python Machine Learning Case Studies

by Danish Haroon • 2017

Five Python case studies in machine learning: from data cleanup to a scored model, step by step.

2.60

📊 Data Science 🤖 Machine Learning 🔍 Exploratory Data Analysis ⚙️ Feature Engineering 📐 Model Evaluation

Python Machine Learning Case Studies Summary

Python Machine Learning Case Studies 87%

Five Python case studies in machine learning: from data cleanup to a scored model, step by step.

by Danish Haroon 2017

2.60

5 ratings

📊 Data Science 🤖 Machine Learning 🔍 Exploratory Data Analysis ⚙️ Feature Engineering 📐 Model Evaluation

Everyday Chaos

by David Weinberger • 2019

A medical AI diagnoses illness but can't explain how. The age of demanding explanations is over.

3.78

202

🔬 Complexity Science 🔄 Systems Thinking 🤖 Machine Learning 💻 Digital Strategy 🔓 Open Platforms

Everyday Chaos 87%

A medical AI diagnoses illness but can't explain how. The age of demanding explanations is over.

by David Weinberger 2019

3.78

202 ratings

🔬 Complexity Science 🔄 Systems Thinking 🤖 Machine Learning 💻 Digital Strategy 🔓 Open Platforms

The Ethical Algorithm

by Michael Kearns • 2019

Algorithms don't do ethics by default. Making them fair takes math, and the math demands trade-offs.

4.10

671

⚖️ Algorithmic Bias 🔒 Data Privacy 🤖 Ai Ethics 🎲 Game Theory 🔬 Reproducibility Crisis

The Ethical Algorithm 87%

Algorithms don't do ethics by default. Making them fair takes math, and the math demands trade-offs.

by Michael Kearns 2019

4.10

671 ratings

⚖️ Algorithmic Bias 🔒 Data Privacy 🤖 Ai Ethics 🎲 Game Theory 🔬 Reproducibility Crisis

Shape

by Jordan Ellenberg • 2021

Geometry is not about triangles. It is the hidden structure of pandemics, search, and democracy.

3.73

2k+

Shape 87%

Geometry is not about triangles. It is the hidden structure of pandemics, search, and democracy.

by Jordan Ellenberg 2021

3.73

2k+ ratings

PYTHON FOR DATA ANALYSIS

by Ryshith Doyle • 2019

The systematic NumPy and Pandas walkthrough: import, clean, combine, and analyze data in Python.

3.67

🐍 Python Programming 📊 Data Analysis 🧹 Data Wrangling 🧱 Data Structures 🔢 Numerical Computing

PYTHON FOR DATA ANALYSIS 87%

The systematic NumPy and Pandas walkthrough: import, clean, combine, and analyze data in Python.

by Ryshith Doyle 2019

3.67

6 ratings

🐍 Python Programming 📊 Data Analysis 🧹 Data Wrangling 🧱 Data Structures 🔢 Numerical Computing

Writing High-Performance .NET Code

by Ben Watson • 2014

Stop guessing why .NET drags. Measure, master the GC, and fix the real bottlenecks.

4.30

291

💻 .Net Development ⚡ Performance Tuning 💾 Memory Management 🔄 Asynchronous Programming 📊 Code Profiling

Writing High-Performance .NET Code Summary

Writing High-Performance .NET Code 87%

Stop guessing why .NET drags. Measure, master the GC, and fix the real bottlenecks.

by Ben Watson 2014

4.30

291 ratings

💻 .Net Development ⚡ Performance Tuning 💾 Memory Management 🔄 Asynchronous Programming 📊 Code Profiling

Big Data

by Viktor Mayer-Schönberger • 2013

The era of sampling, clean data, and causal certainty is over. What replaces it is stranger.

3.69

9k+

💰 Data As Asset 📈 Predictive Analytics 🧹 Imperfect Data 🌐 Quantified World 🔗 Correlation Analysis

Big Data 87%

The era of sampling, clean data, and causal certainty is over. What replaces it is stranger.

by Viktor Mayer-Schönberger 2013

3.69

9k+ ratings

💰 Data As Asset 📈 Predictive Analytics 🧹 Imperfect Data 🌐 Quantified World 🔗 Correlation Analysis

Practical Statistics for Data Scientists

by Peter Bruce • 2020

The statistics data scientists actually use, from EDA to ensembles, with working R and Python code.

4.21

261

📊 Statistical Computing 🔍 Exploratory Data Analysis 📐 Statistical Inference 🔄 Resampling Methods 📈 Regression Modeling

Practical Statistics for Data Scientists Summary

Practical Statistics for Data Scientists 87%

The statistics data scientists actually use, from EDA to ensembles, with working R and Python code.

by Peter Bruce 2020

4.21

261 ratings

📊 Statistical Computing 🔍 Exploratory Data Analysis 📐 Statistical Inference 🔄 Resampling Methods 📈 Regression Modeling

Machine Learning for Dummies

by John Paul Mueller • 2016

Machine learning demystified: pick the right algorithm, train it well, and know when it works.

3.53

139

🧠 Machine Learning Basics 📊 Data Science Basics 🏷️ Supervised Learning 📈 Statistical Learning 🔮 Predictive Modeling

Machine Learning for Dummies 87%

Machine learning demystified: pick the right algorithm, train it well, and know when it works.

by John Paul Mueller 2016

3.53

139 ratings

🧠 Machine Learning Basics 📊 Data Science Basics 🏷️ Supervised Learning 📈 Statistical Learning 🔮 Predictive Modeling

Practical Statistics for Data Scientists

by Peter Bruce • 2017

The statistical methods data scientists use daily, from boxplots to bootstrap, minus the proofs.

4.02

545

🛠️ Practical Data Science 📈 Applied Statistics 🔍 Exploratory Data Analysis 🎓 Statistical Inference 🔄 Resampling Methods

Practical Statistics for Data Scientists 87%

The statistical methods data scientists use daily, from boxplots to bootstrap, minus the proofs.

by Peter Bruce 2017

4.02

545 ratings

🛠️ Practical Data Science 📈 Applied Statistics 🔍 Exploratory Data Analysis 🎓 Statistical Inference 🔄 Resampling Methods

Game Development Patterns and Best Practices

by John P. Doran • 2017

Design patterns that ship games: when to use each and how they prevent late-stage collapse.

3.90

🎮 Game Development 🧩 Design Patterns 🧹 Clean Code 🏗️ Software Architecture ⚡ Performance Optimization

Game Development Patterns and Best Practices Summary

Game Development Patterns and Best Practices 87%

Design patterns that ship games: when to use each and how they prevent late-stage collapse.

by John P. Doran 2017

3.90

20 ratings

🎮 Game Development 🧩 Design Patterns 🧹 Clean Code 🏗️ Software Architecture ⚡ Performance Optimization

Robin Hood Math

by Noah Giansiracusa • 2025

Algorithms price your insurance, rank your resume, curate your reality. The math that fights back.

3.91

185

🧮 Algorithmic Literacy 🛡️ Digital Self-Defense 📐 Applied Mathematics 📊 Bayesian Reasoning 📉 Everyday Statistics

Robin Hood Math 87%

Algorithms price your insurance, rank your resume, curate your reality. The math that fights back.

by Noah Giansiracusa 2025

3.91

185 ratings

🧮 Algorithmic Literacy 🛡️ Digital Self-Defense 📐 Applied Mathematics 📊 Bayesian Reasoning 📉 Everyday Statistics

Modern Software Engineering

by David Farley • 2021

Software is not a craft; it's a science. The discipline that turns teams into learning machines.

4.16

1k+

🔬 Scientific Engineering 🔄 Continuous Delivery 🔁 Feedback Loops 🧱 Modularity 🧪 Testability

Modern Software Engineering 87%

Software is not a craft; it's a science. The discipline that turns teams into learning machines.

by David Farley 2021

4.16

1k+ ratings

🔬 Scientific Engineering 🔄 Continuous Delivery 🔁 Feedback Loops 🧱 Modularity 🧪 Testability

The Elements of Statistical Learning

by Trevor Hastie • 2001

The unified mathematical engine under linear regression, trees, and neural nets.

4.43

2k+

📊 Statistical Learning 🎯 Supervised Learning ✅ Model Evaluation 🤝 Ensemble Learning ⚖️ Regularization Methods

The Elements of Statistical Learning Summary

The Elements of Statistical Learning 87%

The unified mathematical engine under linear regression, trees, and neural nets.

by Trevor Hastie 2001

4.43

2k+ ratings

📊 Statistical Learning 🎯 Supervised Learning ✅ Model Evaluation 🤝 Ensemble Learning ⚖️ Regularization Methods

The Mythical Man-Month

by Frederick P. Brooks Jr. • 1975

A late software project plus more programmers equals a later one. The essays that proved it.

4.01

15k+

🏗️ Software Project Management 🧱 Systems Design 🎨 Conceptual Design 👥 Team Dynamics 🔄 Incremental Development

The Mythical Man-Month 87%

A late software project plus more programmers equals a later one. The essays that proved it.

by Frederick P. Brooks Jr. 1975

4.01

15k+ ratings

🏗️ Software Project Management 🧱 Systems Design 🎨 Conceptual Design 👥 Team Dynamics 🔄 Incremental Development

Neural Networks, Fuzzy Logic And Genetic Algorithms

by S. Rajasekaran • 2004

Soft computing's three pillars each have blind spots. This is how they fill each other's gaps.

4.21

151

🖥️ Soft Computing 🧠 Neural Networks 🌫️ Fuzzy Logic 🧬 Genetic Algorithms 🤖 Machine Learning

Neural Networks, Fuzzy Logic And Genetic Algorithms Summary

Neural Networks, Fuzzy Logic And Genetic Algorithms 87%

Soft computing's three pillars each have blind spots. This is how they fill each other's gaps.

by S. Rajasekaran 2004

4.21

151 ratings

🖥️ Soft Computing 🧠 Neural Networks 🌫️ Fuzzy Logic 🧬 Genetic Algorithms 🤖 Machine Learning

Computational Thinking

by Peter J. Denning • 2019

It is a 4,500-year-old practice that built modern science, yet most confuse it with just coding.

3.63

304

💭 Computational Thinking 🕰️ History Of Computing 🎨 Design Thinking ⚙️ Software Engineering 🧠 Algorithmic Thinking

Computational Thinking 87%

It is a 4,500-year-old practice that built modern science, yet most confuse it with just coding.

by Peter J. Denning 2019

3.63

304 ratings

💭 Computational Thinking 🕰️ History Of Computing 🎨 Design Thinking ⚙️ Software Engineering 🧠 Algorithmic Thinking

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