Programming
Software development: programming languages, coding, libraries and frameworks, software engineering and architecture, source control, developer tools, databases, and infrastructure.
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DataScreenIQ datascreeniq.com
Real-time data quality firewall for pipelines and APIs. Screens rows in milliseconds for schema drift, null spikes, type mismatches, and data anomalies with PASS / WARN / BLOCK decisions.
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Data Mechanics datamechanics.co
A cloud-based platform deployed on Kubernetes making Apache Spark more developer-friendly and cost-effective.
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DataKitchen datakitchen.io
Open Source Data Observability for end-to-end Data Journey Observability, data profiling, anomaly detection, and auto-created data quality validation tests.
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Dataform dataform.co
An open-source framework and web based IDE to manage datasets and their dependencies. SQLX extends your existing SQL warehouse dialect to add features that support dependency management, testing, documentation and more.
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Crustdata crustdata.com
A real-time B2B data API for company and people intelligence, providing firmographics, headcount signals, job listings, web traffic, and funding events via REST API and webhooks for data enrichment pipelines.
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Couchbase couchbase.com
The highest performing NoSQL distributed database.
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Common Crawl commoncrawl.org
Open source repository of web crawl data.
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ClickHouse clickhouse.tech
Distributed columnar DBMS for OLAP. SQL.
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chDB chdb.io
Embedded ClickHouse — full ClickHouse SQL dialect, ~80 data formats, and 12+ source connectors (S3, Postgres, MongoDB, Kafka, Iceberg) in core. Python, Go, Rust, Node, Bun, Zig, and Ruby bindings.
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Bonobo bonobo-project.org
A data-processing toolkit for python 3.5+.
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Actian Ingres actian.com
commercially supported, open-source SQL relational database management system.
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Actian Versant actian.com
commercial object-oriented database management systems .
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Actian SQL for Hadoop actian.com
high performance interactive SQL access to all Hadoop data.
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Actian Vector actian.com
column-oriented analytic database.
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Arpe.io arpe.io
High-speed CLI tools for database export, import, replication and migration with parallel streaming to CSV, Parquet, JSON and cloud storage, supporting PostgreSQL, MySQL, Oracle, SQL Server and 80+ sources.
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LinkedIn Jobs Scraper apify.com
Crawlee-based actor extracting structured LinkedIn job listings at scale without API keys.
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Sorsa API api.sorsa.io
Real-time X (Twitter) data API providing tweets, profiles, search, communities and engagement metrics. Up to 50x cheaper than the official X API with 20 req/sec rate limit, JSON output.
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AI for Database aifordatabase.com
Agentic AI platform to connect any database (PostgreSQL, MySQL, MongoDB, etc.) and query in plain English; includes self-refreshing intelligent dashboards and action workflows triggered by data changes.
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Docker based Hadoop provisioning slideshare.net
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Programming Hive shop.oreilly.com
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Programming Pig shop.oreilly.com
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Parametarized Algorithms by Warsaw youtube.com
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CMU Theory youtube.com
Aims for a mathematical understanding of fundamental issues in Computer Science, and to use this understanding to produce better algorithms, protocols, and systems, as well as identify the inherent limitations of efficient computation.
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Gregory Valiant. The Modern Algorithmic Toolbox. Stanford web.stanford.edu
It covers hashing, dimension reduction, linear and convex programming, gradient descent and regression, sampling and estimation, compressive sensing, linear-algebraic techniques (principal components analysis, singular value decomposition, spectral techniques), and an intro to…
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Mary Wootters. Randomized Algorithms and Probabilistic Analysis. Stanford web.stanford.edu
Key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Emphasis is on theoretical foundations, though applications will be discussed in machine learning and data analysis, networking, and systems…
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Computability, Complexity & Algorithms. Georgia Institute of Technology udacity.com
It focuses on the big fundamental questions of computing, and how understanding the power and limitations of algorithms helps us develop the tools to make real-world computers smarter, faster and safer.
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Du & Ko. Design and Analysis of Approximation Algorithms u.pcloud.link
A technique-oriented approach provides a unified view. It includes detailed algorithms, proofs, analyses, examples, and applications from research papers.
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Vijay Vazirani. Approximation Algorithms u.pcloud.link
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Indyk & Daskalakis. Learning-augmented Algorithms. MIT stellar.mit.edu
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Oberguggenberger & Ostermann. Analysis for Computer Scientists: Foundations, Methods, and Algorithms springer.com
Presents an algorithmic approach to mathematical analysis, with a focus on modelling and on the applications of analysis.
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Makinson. Sets, Logic and Maths for Computing springer.com
It presents a careful selection of the material most needed by students in their first two years studying computer science.
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UW CSE505 18au Principles of PL sites.google.com
Techniques for thinking crisply about programming languages, write some fascinating programs, and discuss various design tradeoffs.
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Part I scottaaronson.com
& Part II - Part I: Presuppose only linear algebra and a bit of classical algorithms. Topics include quantum circuits, density matrices, entanglement entropy, Wiesner’s quantum money, QKD, quantum teleportation, the Bell inequality, interpretations of QM, the Shor 9-qubit code…
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Atallah & Blanton. Algorithms and Theory of Computation Handbook: Special Topics and Techniques routledge.com
A complete comprehensive encyclopediac handbook which surveys all related areas to theoretical computer science.
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Atallah & Blanton. Algorithms and Theory of Computation Handbook: General Concepts and Techniques routledge.com
A complete comprehensive encyclopediac handbook which surveys all related areas to theoretical computer science.
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Jeremy Kun. A Programmer's Introduction to Mathematics pimbook.org
Uses your familiarity with ideas from programming and software to teach mathematics.
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Madhu Sudan. Essential Coding Theory people.seas.harvard.edu
Some elements of Algorithmic tasks of encoding and decoding and its connections with error-correction; These codes are now tools in the design and analysis of algorithms, and also in many aspects of computational complexity. The focus is on constructions of algorithmic and…
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Papadimitriou. Computational Complexity pearson.com
Body of knowledge for studying the performance and limitations of computer algorithms. Among topics covered are: reductions and NP-completeness, cryptography and protocols, randomized algorithms, and approximability of optimization problems, circuit complexity, the structural…
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Knuth, Graham & Patashnik. Concrete Mathematics: A Foundation for Computer Science pearson.com
An expansion of the Mathematical Preliminaries section in Knuth's classic Art of Computer Programming, but the style of presentation is more leisurely, and individual topics are covered more deeply.
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Erik Demaine. Advanced Data Structures. MIT ocw.mit.edu
It covers major results and current directions of research in data structure.
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Demaine/Devadas/Lynch. Design and Analysis of algorithms. MIT ocw.mit.edu
A second course on algorithms and data structures. — added by Erik himself!
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Demaine/Ku/Soloman. Introduction to Algorithms. MIT ocw.mit.edu
A first course on basic algorithms and data structures. — added by Erik himself!
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MIT OCW ocw.mit.edu
, HTML book, Byford's playlist, Javascript book, Python book, Berkeley for self-study, and Berkeley 2024 - Fundamental principles of computer programming in Scheme, including recursion, abstraction, modularity, and programming language design and implementation.
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Kelner. Topics in Theoretical Computer Science: An Algorithmist's Toolkit. MIT ocw.mit.edu
It covers a collection of geometric techniques that apply broadly in modern algorithm design.
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Moore & Mertens. The Nature of Computation nature-of-computation.org
It spans complexity of mazes and games; optimization in theory and practice; randomized algorithms, interactive proofs, and pseudorandomness; Markov chains and phase transitions; and of quantum computing. It provides accessible explanations
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Penna. Algorithmic Game Theory ml2.inf.ethz.ch
The course discusses algorithmic aspects of game theory, such as a general introduction to game theory, auctions, mechanisms, the costs of a central control optimum versus those of an equilibrium under selfish agents, and algorithms and complexity of computing equilibria.
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Handbook of Theoretical Computer Science. Volume A: Algorithms and Complexity mitpress.mit.edu
A complete comprehensive encyclopediac handbook which surveys all related areas to theoretical computer science.
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Eck & Critchlow. Foundations of Computation math.hws.edu
for a one-semester course in theoretical computer science. It has no prerequisites other than introductory computer programming. It includes logic, sets, and function from discrete math, and automata, formal languages, and grammar from upper-level courses.
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Vaughan. CS260: Machine Learning Theory jennwv.com
A broad overview of the theoretical foundations underlying common machine learning algorithms.
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Lee. Randomized Algorithms and Probabilistic Analysis. Washington. homes.cs.washington.edu
Topics include Discrete probability, High-dimensional geometry and statistics, Information and entropy, and Markov chains and convergence to equilibrium.
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