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The data science reading list I wish I'd had from day one

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**A curated reading list for becoming a well-rounded data scientist.**

This is a personal collection of useful resources from my journey to becoming a unicorn data scientist: someone who can work across the full data science discipline, not just train models.

> This is not another list of tools and libraries. It contains articles, papers, and guides that explain difficult ideas unusually well and remain useful over time.

## Table of Contents

- [1. Engineering](#1-engineering)
  - [ML Engineering](#ml-engineering)
  - [Software Engineering](#software-engineering)
  - [Forward Deployed Engineering](#forward-deployed-engineering)
- [2. Statistics](#2-statistics)
  - [Probability](#probability)
  - [Inference](#inference)
  - [Experiment Design](#experiment-design)
- [3. Machine Learning](#3-machine-learning)
  - [ML Fundamentals](#ml-fundamentals)
  - [Search and Recommendations](#search-and-recommendations)
  - [Causal Inference](#causal-inference)
  - [Regression and Forecasting](#regression-and-forecasting)
  - [Deep Learning](#deep-learning)
- [4. Generative AI](#4-generative-ai)
  - [LLM Fundamentals](#llm-fundamentals)
  - [Agents](#agents)
  - [LLM System Design](#llm-system-design)
  - [Applied LLMs](#applied-llms)
  - [LLM Training and Inference](#llm-training-and-inference)
  - [Evals](#evals)
  - [Prompt Engineering](#prompt-engineering)
- [5. Data & Analytics](#5-data--analytics)
  - [Analytics](#analytics)
  - [Analytics Engineering](#analytics-engineering)
  - [Data Governance](#data-governance)
  - [Dashboards](#dashboards)
- [6. Working as a Data Scientist](#6-working-as-a-data-scientist)
  - [Management](#management)
  - [Ways of Working](#ways-of-working)
  - [Craft](#craft)
  - [Hiring](#hiring)
  - [Interviewing](#interviewing)
  - [Consulting](#consulting)
- [Contributing](#contributing)

---

## 1. Engineering

### ML Engineering

- [Rules of ML](https://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)
- [Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department](https://multithreaded.stitchfix.com/blog/2016/03/16/engineers-shouldnt-write-etl/)
- [Machine Learning: The High-Interest Credit Card of Technical Debt](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/43146.pdf)
  - [Follow-up post on ML technical debt](https://matthewmcateer.me/blog/machine-learning-technical-debt/)
- [Production-Level-Deep-Learning](https://github.com/alirezadir/Production-Level-Deep-Learning)
- [End-to-end Machine Learning with TFX on TensorFlow 2.x](https://towardsdatascience.com/end-to-end-machine-learning-with-tfx-on-tensorflow-2-x-6eda2fb5fe37)
- [Monitoring Machine Learning Models in Production](https://christophergs.com/machine%20learning/2020/03/14/how-to-monitor-machine-learning-models/)
- [ML engineering best practices](https://se-ml.github.io/practices/)
- [Google MLOps whitepaper](https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf)
- [MLOps Principles](https://ml-ops.org/content/mlops-principles)
- [Continuous delivery and automation pipelines in machine learning](https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)
- [No, you don't need MLOps](https://lakshmanok.medium.com/no-you-dont-need-mlops-5e1ce9fdaa4b)
- [Nomadic Infrastructure Design for AI Workloads](https://www.tigrisdata.com/blog/nomadic-compute/)

### Software Engineering

- [Python Design Patterns](https://python-patterns.guide/)
- [Awesome Prometheus Alerts](https://awesome-prometheus-alerts.grep.to/)
- [Refactoring Guru](https://refactoring.guru/design-patterns)
- [Choose boring technology](https://boringtechnology.club/)
- [The Beginner's Guide to Databases](https://technically.substack.com/p/the-beginners-guide-to-databases)
- [System Design 101](https://github.com/ByteByteGoHq/system-design-101)
- [How to do a code review](https://google.github.io/eng-practices/review/reviewer/)
- [Python dependency management is a dumpster fire](https://nielscautaerts.xyz/python-dependency-management-is-a-dumpster-fire.html)
- [Ways I Use Testing as a Data Scientist](https://www.peterbaumgartner.com/blog/testing-for-data-science/)

### Forward Deployed Engineering

- [The Forward Deployed Engineer Playbook: How to Structure, Run, and Scale an FDE Function in 2026](https://getperspective.ai/blog/the-forward-deployed-engineer-playbook-how-to-structure-run-and-scale-an-fde-function-in-2026)
- [The Forward Deployed Engineer Playbook Explained](https://fde.academy/blog/forward-deployed-engineer-playbook#why-fdes-need-a-playbook-not-just-skills)

## 2. Statistics

### Probability

- [Probability Distribution Explorer](http://bois.caltech.edu/distribution_explorer/)
- [Common probability distributions](https://medium.com/@srowen/common-probability-distributions-347e6b945ce4)
- [William Chen probability cheat sheet](https://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf)
- [KDE visualisation](https://mathisonian.github.io/kde/)
- [Modeling conversion rates and saving millions of dollars using Kaplan-Meier and gamma distributions](https://better.engineering/2019/07/29/modeling-conversion-rates-and-saving-millions-of-dollars-using-kaplan-meier-and-gamma-distributions/)
- [Robust Statistical Distances for Machine Learning](https://www.datadoghq.com/blog/engineering/robust-statistical-distances-for-machine-learning/)
- [How Not To Sort By Average Rating](https://www.evanmiller.org/how-not-to-sort-by-average-rating.html)

### Inference

- [Common statistical tests are linear models](https://lindeloev.github.io/tests-as-linear/)
- [On Average, You’re Using the Wrong Average: Geometric & Harmonic Means in Data Analysis](https://towardsdatascience.com/on-average-youre-using-the-wrong-average-geometric-harmonic-means-in-data-analysis-2a703e21ea0)
- [Stop aggregating away the signal in your data](https://stackoverflow.blog/2022/03/03/stop-aggregating-away-the-signal-in-your-data/)
- [Inferring Concept Drift Without Labeled Data](https://concept-drift.fastforwardlabs.com/)
- [The hacker's guide to uncertainty estimates](https://erikbern.com/2018/10/08/the-hackers-guide-to-uncertainty-estimates.html)
- [A One-Page Primer on: Statistical Power](https://www.carlislerainey.com/blog/2025-08-30-1p-statistical-power/)

### Experiment Design

- [Detecting Interference: An A/B Test of A/B Tests](https://engineering.linkedin.com/blog/2019/06/detecting-interference--an-a-b-test-of-a-b-tests)
- [Switchback Tests and Randomized Experimentation Under Network Effects at DoorDash](https://medium.com/@DoorDash/switchback-tests-and-randomized-experimentation-under-network-effects-at-doordash-f1d938ab7c2a)
- [Innovating Faster on Personalization Algorithms at Netflix Using Interleaving](https://netflixtechblog.com/interleaving-in-online-experiments-at-netflix-a04ee392ec55)
- [How Walmart Leverages CUPED and Reduces Experimentation Lifecycle](https://medium.com/walmartglobaltech/how-walmart-leverages-cuped-and-reduces-experimentation-lifecycle-eae2446c2ee4)

## 3. Machine Learning

### ML Fundamentals

- [The Illustrated Machine Learning website](https://illustrated-machine-learning.github.io/)
- [Feature Engineering A-Z](https://feaz-book.com/)
- [Applied Machine Learning for Tabular Data](https://aml4td.org/)
- [Interpretable ML Book](https://christophm.github.io/interpretable-ml-book/)
- [Machine Learning Visualized](https://ml-visualized.com/)
- [Bayesian Optimization](https://distill.pub/2020/bayesian-optimization/)

### Search and Recommendations

- [Google Recommendation Systems Crash Course](https://developers.google.com/machine-learning/recommendation)
- [Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf)
- [Deep density networks and uncertainty in recommender systems](https://arxiv.org/pdf/1711.02487.pdf)
- [Microsoft Recommenders](https://github.com/microsoft/recommenders)
- [aman.ai recsys](https://aman.ai/recsys/)
- [The Best Way to Use Text Embeddings Portably is With Parquet and Polars](https://minimaxir.com/2025/02/embeddings-parquet/)
- [AI Engineer 2025 - Improving RecSys & Search with LLM techniques](https://eugeneyan.com/speaking/aie-2025/)
- [Improving Recommendation Systems & Search in the Age of LLMs](https://eugeneyan.com/writing/recsys-llm/)

### Causal Inference

- [Bandit Algorithms](https://banditalgs.com/)
- [A Contextual-Bandit Approach to Personalized News Article Recommendation](https://arxiv.org/pdf/1003.0146.pdf)
- [Python Causality Handbook](https://matheusfacure.github.io/python-causality-handbook/)

### Regression and Forecasting

- [OLS Regression Explained Visually](http://setosa.io/ev/ordinary-least-squares-regression/)
- [How Instacart delivers on time (using quantile regression)](https://tech.instacart.com/how-instacart-delivers-on-time-using-quantile-regression-2383e2e03edb)
- [Our quest for robust time series forecasting at scale](http://www.unofficialgoogledatascience.com/2017/04/our-quest-for-robust-time-series.html)
- [Sorry ARIMA, but I’m Going Bayesian](https://multithreaded.stitchfix.com/blog/2016/04/21/forget-arima/)

### Deep Learning

- [A Recipe for Training Neural Networks](http://karpathy.github.io/2019/04/25/recipe/)
- [Numerically stable and computationally efficient log-sum-exp](http://www.nowozin.net/sebastian/blog/streaming-log-sum-exp-computation.html)
- [Interpreting loss curves](https://developers.google.com/machine-learning/testing-debugging/metrics/interpretic)
- [Visualizing the Loss Landscape of a Neural Network](https://mathformachines.com/posts/visualizing-the-loss-landscape/)
- [Google research tuning playbook](https://github.com/google-research/tuning_playbook)

## 4. Generative AI

### LLM Fundamentals

- [Understanding Large Language Models](https://magazine.sebastianraschka.com/p/understanding-large-language-models)
- [Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch](https://sebastianraschka.com/blog/2023/self-attention-from-scratch.html)
- [LLM101n - Andrej Kaparthy](https://github.com/karpathy/LLM101n)
- [Anti-hype LLM reading list](https://gist.github.com/veekaybee/be375ab33085102f9027853128dc5f0e)
- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)
- [Learning Deep Representations of Data Distributions](https://ma-lab-berkeley.github.io/deep-representation-learning-book/index.html)
- [Hugging Face LLM Course](https://huggingface.co/learn/llm-course/chapter1/1)
- [Transformers from Scratch](https://brandonrohrer.com/transformers.html)
- [Holding the LLM Stack in Your Head](https://thegustafson.com/series)
- [Transformer Explainer](https://poloclub.github.io/transformer-explainer/)
- [What is SwiGLU?](https://jcarlosroldan.com/post/348/what-is-swiglu)

### Agents

- [Zero to One: Learning Agentic Patterns](https://www.philschmid.de/agentic-pattern)
- [A practical guide to building agents - OpenAI](https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf)
- [Building effective agents - Anthropic](https://www.anthropic.com/engineering/building-effective-agents)
- [How to Build an Agent](https://ampcode.com/how-to-build-an-agent)
- [12 factor agents](https://github.com/humanlayer/12-factor-agents)
- [Lessons on building an AI data analyst](https://www.pedronasc.com/articles/lessons-building-ai-data-analyst)

### LLM System Design

- [Building a Generative AI Platform](https://huyenchip.com/2024/07/25/genai-platform.html)
- [Emerging Architectures for LLM Applications](https://a16z.com/emerging-architectures-for-llm-applications/)
- [rerankers](https://www.answer.ai/posts/2024-09-16-rerankers.html)
- [ML and LLM system design: 450 case studies to learn from](https://www.evidentlyai.com/ml-system-design)
- [Introducing Contextual Retrieval](https://www.anthropic.com/news/contextual-retrieval)
- [Vector Databases Are the Wrong Abstraction](https://www.timescale.com/blog/vector-databases-are-the-wrong-abstraction/)
- [RAG Is Simpler Than You Think](https://www.lighthousenewsletter.com/p/rag-is-simpler-than-you-think)

### Applied LLMs

- [Here’s how I use LLMs to help me write code](https://simonwillison.net/2025/Mar/11/using-llms-for-code/)
- [AI in organizations: Some tactics](https://www.oneusefulthing.org/p/ai-in-organizations-some-tactics)
- [Synthetic Consumers](https://drive.google.com/file/d/1LVylkrK4fFrHC02TeZ_auxOx5GwGQyqP/view)
- [LLMOps Is About People Too: The Human Element in AI Engineering](https://www.zenml.io/blog/llmops-is-about-people-too-the-human-element-in-ai-engineering)
- [Field Notes From Shipping Real Code With Claude](https://diwank.space/field-notes-from-shipping-real-code-with-claude)
- [Running a Software Factory Efficiently at Uber Scale](https://www.uber.com/gb/en/blog/efficient-software-factory/)

### LLM Training and Inference

- [GPU Glossary](https://modal.com/gpu-glossary)
- [LLM Inference Economics from First Principles](https://www.tensoreconomics.com/p/llm-inference-economics-from-first)
- [Basic facts about GPUs](https://damek.github.io/random/basic-facts-about-gpus/)
- [Making Deep Learning Go Brrrr From First Principles](https://horace.io/brrr_intro.html)
- [The Ultra-Scale Playbook](https://huggingface.co/spaces/nanotron/ultrascale-playbook)
- [Transformer Math 101](https://blog.eleuther.ai/transformer-math/)
- [A Visual Guide to Quantization](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-quantization)

### Evals

- [Creating a LLM-as-a-Judge That Drives Business Results](https://hamel.dev/blog/posts/llm-judge/)
- [Demystifying evals for AI agents](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents)

### Prompt Engineering

- [awesome-chatgpt-prompts](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts)
- [Prompt Engineering Guide](https://www.promptingguide.ai/)
- [Anthropic prompt engineering overview](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview)
- [Prompt Engineering Roadmap](https://roadmap.sh/prompt-engineering)
- [OpenAI Cookbook](https://cookbook.openai.com/)
- [Google Prompt Design Strategies](https://ai.google.dev/gemini-api/docs/prompting-strategies)

## 5. Data & Analytics

### Analytics

- [Data Analytics Design Patterns](https://cloud.google.com/architecture/reference-patterns/overview)
- [How AI will Disrupt BI As We Know It](https://roundup.getdbt.com/p/how-ai-will-disrupt-bi-as-we-know)
- [Coding for Economists](https://aeturrell.github.io/coding-for-economists/)
- [Practical advice for analysis of large, complex data sets](http://www.unofficialgoogledatascience.com/2016/10/practical-advice-for-analysis-of-large.html)
- [North star metrics](https://future.com/north-star-metrics/)
- [Choosing a Product Analytics Tool](https://sarahsnewsletter.substack.com/p/choosing-a-product-analytics-tool)

### Analytics Engineering

- [What is an analytics engineer?](https://blog.getdbt.com/what-is-an-analytics-engineer/)
- [The Modern Data Stack](https://blog.getdbt.com/future-of-the-modern-data-stack)
- [Emerging architectures for modern data infrastructure](https://future.a16z.com/emerging-architectures-modern-data-infrastructure/)
- [Highly Opinionated Integrations](https://davidsj.substack.com/p/highly-opinionated-integrations)
- [2022 ETL Buyer’s Guide: How to Pick the Right Tool for Your Analytics Stack](https://mode.com/blog/etl-buyers-guide)
- [Modern Data Stack in a Box with DuckDB](https://duckdb.org/2022/10/12/modern-data-stack-in-a-box.html)
- [Simple ML for Sheets](https://simplemlforsheets.com/index.html)
- [Data Pipeline Design Patterns](https://www.startdataengineering.com/post/design-patterns/)
- [The Analytics Development Lifecycle](https://www.getdbt.com/resources/guides/the-analytics-development-lifecycle)
- [The Rise of the Declarative Data Stack](https://www.rilldata.com/blog/the-rise-of-the-declarative-data-stack)

### Data Governance

- [DataHub: Popular metadata architectures explained](https://engineering.linkedin.com/blog/2020/datahub-popular-metadata-architectures-explained)
- [Biases in AI Systems](https://queue.acm.org/detail.cfm?id=3466134)
- [How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh](https://martinfowler.com/articles/data-monolith-to-mesh.html)

### Dashboards

- [Apple's Human Interface Guidelines for Charts](https://developer.apple.com/design/human-interface-guidelines/components/content/charts)
- [Better dashboards align with the scales of business decisions](https://observablehq.com/blog/align-dashboards-with-scales-of-business-decisions)

## 6. Working as a Data Scientist

### Management

- [Hashicorp manager charter](https://works.hashicorp.com/articles/manager-charter)
- [Open decision making](https://web.stanford.edu/~ouster/cgi-bin/decisions.php)
- [So You Want to Become a Data Science Manager?](https://medium.com/deliberate-data-science/so-you-want-become-a-data-science-manager-4ff9544e6827)
- [Mochary Method Curriculum](https://docs.google.com/document/d/18FiJbYn53fTtPmphfdCKT2TMWH-8Y2L-MLqDk-MFV4s/preview?pru=AAABhJk6izM*8NM-ASg_-AehSTTdRjwR3w)
- [The Great CEO Within](https://docs.google.com/document/d/1ZJZbv4J6FZ8Dnb0JuMhJxTnwl-dwqx5xl0s65DE3wO8/edit)

### Ways of Working

- [Agile analytics](https://locallyoptimistic.com/post/agile-analytics-p1/)
- [Modern data culture stack](https://humansofdata.atlan.com/2021/12/modern-data-culture-stack/)
- [Gitlab Data Team Handbook](https://about.gitlab.com/handbook/business-technology/data-team/)
- [Coordination Headwind](https://komoroske.com/slime-mold/)
- [The Art of Onboarding](https://locallyoptimistic.com/post/the-art-of-onboarding/)

### Craft

- [Good DS vs. Bad DS](https://ianwhitestone.work/good-ds-bad-ds)
- [Don't be Frupid](https://selix.net/notes/dont-be-frupid)
- [7 Mindsets That Are Slowing Down Your Career Growth](https://www.thecaringtechie.com/p/7-mindsets-that-are-slowing-down)
- [3 Critical Skills You Need to Grow Beyond Senior Levels in Engineering](https://www.thecaringtechie.com/p/3-critical-skills-you-need-to-grow)
- [Best Practice vs. Fit Practice](https://blog.alexewerlof.com/p/best-practice)
- [Cargo Culting](https://blog.alexewerlof.com/p/cargo-culting)
- [The Best Programmers I Know](https://endler.dev/2025/best-programmers/)

### Hiring

- [How to Consistently Hire Remarkable Data Scientists](https://firstround.com/review/how-to-consistently-hire-remarkable-data-scientists/)
- [How Coursera Competes Against Google and Facebook for the Best Talent](https://firstround.com/review/this-is-how-coursera-competes-against-google-and-facebook-for-the-best-talent/)
- [How to hire smarter than the market: a toy model](https://erikbern.com/2020/01/13/how-to-hire-smarter-than-the-market-a-toy-model.html)
- [Interviewing is a noisy prediction problem](https://erikbern.com/2018/05/02/interviewing-is-a-noisy-prediction-problem.html)
- [How to set compensation using commensense principles](https://erikbern.com/2020/06/08/how-to-set-compensation-using-commonsense-principles.html)
- [VP of Engineering hiring cheatsheet](https://notion.notion.site/VP-of-Engineering-hiring-cheatsheet-f7824eb3f60748bb997f9b9b14c073a5)
- [Quantifying the statistical skills needed to be a Google Data Scientist](https://www.unofficialgoogledatascience.com/2025/03/quantifying-statistical-skills-needed.html)

### Interviewing

- [The Data Science Interview Book](https://dipranjan.github.io/dsinterviewqns/intro.html)
- [Conor Dewey's list of data science interview resources](https://www.conordewey.com/blog/the-big-list-of-data-science-interview-resources/)
- [101 Data Science Interview Questions, Answers, and Key Concepts](https://blog.datasciencedojo.com/data-science-interview-questions/)
- [How I negotiated a $300,000 job offer in Silicon Valley](https://medium.com/@bayareabelletrist/how-i-negotiated-a-software-engineer-offer-in-silicon-valley-f11590f5c656)
- [How to Make Your Data Science Job Application Stand Out](https://www.dataquest.io/blog/how-to-data-science-job-application-stand-out/)
- [You’re probably answering these 5 common interview questions wrong](https://www.holistics.io/blog/judge-your-company-using-three-levels-data-analysis-data-career-decision-making/)
- [6 red flags I saw while doing 60+ technical interviews in 30 days](http://blog.interviewing.io/6-red-flags-i-saw-while-doing-60-technical-interviews-in-30-days)
- [Google interview warmup](https://grow.google/certificates/interview-warmup/)
- [Red Flags to Look Out for When Joining a Data Team](https://eugeneyan.com/writing/red-flags/)
- [Data Scientist Handbook](https://github.com/andresvourakis/data-scientist-handbook?tab=readme-ov-file)
- [A guide to passing the A/B test interview question in tech companies](https://www.reddit.com/r/datascience/comments/1fyrawz/a_guide_to_passing_the_ab_test_interview_question/)
- [Top 50 Large Language Model (LLM) Interview Questions](https://drive.google.com/file/d/1cUxKspEXgQ64s4OFEw0kabf_qNauOPiH/view)

### Consulting

- [10 Reads for Data Scientists Getting Started with Business Models](https://www.conordewey.com/blog/10-reads-for-data-scientists-getting-started-with-business-models/)
- [Smart companies try to commoditize their products’ complements](https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/)
- [How to find consulting clients](https://chrisachard.com/how-to-find-consulting-clients)
- [Doing Freelance Data Science Consulting in 2019](https://www.ethanrosenthal.com/2020/01/08/freelance-ds-consulting/)
- [Official Lean AI Company Playbook](https://henrythe9th.substack.com/p/official-lean-ai-company-playbook)

## Contributing

Found something special that deserves a place here? I’d love to hear about it.

You can either [open an issue](https://github.com/thisisandreeeee/unicorn-data-science/issues/new) to suggest it, or submit a pull request directly. If you choose to submit a PR, please read [CONTRIBUTING.md](./CONTRIBUTING.md) first.

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