llms.txt
# Let's Data Science
> Data science and AI learning platform with browser-based SQL and Python practice,
> interactive courses, guided coding projects, interview flashcards and AI news.
> Practice problems run in the browser; project applications run on the learner's
> own computer and may require installed tools and separate provider accounts.
The platform covers the full data science learning journey: structured courses
(SQL, Python, Statistics, Probability), hands-on practice with real company-style
datasets (AdTech, Banking, Fintech, Healthcare, Logistics, Retail, and 9 more),
guided applications with source code, rapid interview prep via flashcards, and AI/ML news games.
## Guided Coding Projects
- [LDS Projects](https://letsdatascience.com/projects): Build AI applications step by step using working source code, real app recordings and checks. Public overview pages explain prerequisites, estimated effort, costs and data handling. Free guides require an LDS account; open projects do not. API usage is separate from guide access.
- [Build Feed Lens with Jev](https://letsdatascience.com/projects/feed-lens): Eight-step JavaScript project to build a desktop Chrome extension that recommends what to read on LinkedIn. Practice and offline tests need no API key; live scoring uses your own TypeSafe account. LinkedIn is the only included platform integration. A fit score is not an accuracy or fact-check score. Contributed by Prateek Gaurav.
- [Feed Lens source code](https://github.com/lets-data-science/feed-lens): Public source and versioned starter/reference releases. Apache 2.0 applies to the project code; no API credentials are included.
- [Build AI Walkthrough Studio with Gemini](https://letsdatascience.com/projects/ai-walkthrough-studio): Six-step React, Gemini and FFmpeg project. Bring your own screen recording, edit a scene plan and export a captioned walkthrough locally. No sample video is bundled. Gemini analysis sends a silent video copy to Google; optional ElevenLabs narration sends the approved script to ElevenLabs. Voice is a deliberate final step. Contributed by Prateek Gaurav.
- [AI Walkthrough Studio source code](https://github.com/lets-data-science/ai-walkthrough-studio): Public source and versioned starter/reference releases under Apache 2.0. Manual editing and silent export work without an API key. Requires Node.js 22.13 or newer and FFmpeg/FFprobe.
## Practice Problems
- [All Problems](https://letsdatascience.com/problems): 1,625 SQL and Python practice problems graded in-browser (816 SQL, 809 Python)
- [AdTech SQL Problems](https://letsdatascience.com/problems?dataset=adtech_v1): Campaign, impression, click, and conversion analytics (Google, Meta, The Trade Desk)
- [Banking SQL Problems](https://letsdatascience.com/problems?dataset=banking_v1): Account, transaction, and loan analysis (JPMorgan, Goldman Sachs)
- [Fintech Trading Problems](https://letsdatascience.com/problems?dataset=fintech_trading_v1): Portfolio, order, and stock analysis (Robinhood, Schwab, Fidelity)
- [Health Claims Problems](https://letsdatascience.com/problems?dataset=health_claims_v1): Member, claim, and provider analysis (UnitedHealth, Cigna, Aetna)
- [Lodging Problems](https://letsdatascience.com/problems?dataset=lodging_v1): Property, booking, and review analysis (Airbnb, Booking.com, Marriott)
- [Logistics Problems](https://letsdatascience.com/problems?dataset=logistics_v1): Shipment, route, and warehouse optimization (FedEx, UPS, DHL)
- [Food Delivery Problems](https://letsdatascience.com/problems?dataset=marketplace_food_v1): Order, restaurant, and driver analytics (DoorDash, Uber Eats, Instacart)
- [Ride-Sharing Problems](https://letsdatascience.com/problems?dataset=marketplace_mobility_v1): Ride, driver, and passenger analysis (Uber, Lyft, Grab)
- [Payments Problems](https://letsdatascience.com/problems?dataset=payments_v1): Transaction, merchant, and dispute analysis (Stripe, PayPal, Square)
- [Real Estate Problems](https://letsdatascience.com/problems?dataset=real_estate_v1): Listing, agent, and transaction analysis (Zillow, Redfin, Compass)
- [Retail Problems](https://letsdatascience.com/problems?dataset=retail_v1): Product, order, and customer analysis (Amazon, Walmart, Shopify)
- [B2B SaaS Problems](https://letsdatascience.com/problems?dataset=saas_b2b_v1): Account, subscription, and usage analysis (Salesforce, HubSpot, Atlassian)
- [Social Network Problems](https://letsdatascience.com/problems?dataset=social_graph_v1): User, connection, and post analysis (LinkedIn, Twitter/X, Meta)
- [Streaming Media Problems](https://letsdatascience.com/problems?dataset=streaming_media_v1): User, content, and session analysis (Netflix, Spotify, YouTube)
- [Telecom Problems](https://letsdatascience.com/problems?dataset=telecom_isp_v1): Subscriber, plan, and network analysis (AT&T, Verizon, T-Mobile)
## Interactive Courses
The catalog has 13 active courses: 6 free courses and 7 Pro courses. Module 1 of every Pro course is free; Modules 2+ require a Pro subscription. All courses run code in-browser (Pyodide / sql.js / PGlite) with animated visualizations.
- [Learn Hub](https://letsdatascience.com/learn): All interactive courses
- [Full course syllabi (llms-full.txt)](https://letsdatascience.com/llms-full.txt): module-by-module concept outline of every course (topics + sections)
- [SQL Fundamentals](https://letsdatascience.com/learn/sql-fundamentals): SELECT, WHERE, GROUP BY, JOINs with animated visualizations (free)
- [SQL Mastery](https://letsdatascience.com/learn/sql-mastery): CASE, window functions (ROW_NUMBER, RANK, LAG/LEAD), CTEs, subqueries (free)
- [SQL Window Functions](https://letsdatascience.com/learn/sql-window-functions): OVER, PARTITION BY, ranking, ROWS/RANGE frames, LAG/LEAD, multi-dialect quirks, performance (Pro — Module 1 free)
- [Python Fundamentals](https://letsdatascience.com/learn/python-fundamentals): Variables, data structures, functions, OOP, error handling (free)
- [Pandas Fundamentals](https://letsdatascience.com/learn/pandas-fundamentals): DataFrames, selection, groupby, joins, reshaping, time series (free)
- [Statistics Foundations](https://letsdatascience.com/learn/statistics): Descriptive stats, distributions, spread & variability, skewness (free)
- [Probability for Data Scientists](https://letsdatascience.com/learn/probability): Conditional probability, random variables, distributions, Bayes, joint distributions, LLN & CLT (Pro — Module 1 free)
- [Machine Learning Fundamentals](https://letsdatascience.com/learn/ml-fundamentals): Linear & logistic regression, regularization, classification, metrics, cross-validation, sklearn pipelines (Pro — Module 1 free)
- [A/B Testing & Experimentation](https://letsdatascience.com/learn/ab-testing): Causal inference, hypotheses & OEC, power, randomization & SRM, CUPED, sequential testing, multi-armed bandits (Pro — Module 1 free)
- [LLM Foundations](https://letsdatascience.com/learn/llm-foundations): Tokenization, embeddings & RoPE, attention, the transformer block, training, inference/decoding, post-training (SFT/DPO/GRPO), interpretability (free)
- [Building RAG Systems & Vector Search](https://letsdatascience.com/learn/rag-vector-search): Retrieval, embeddings, chunking, hybrid search, vector indexes, reranking, grounding, citations, and RAG evaluation (Pro — Module 1 free)
- [Building AI Agents](https://letsdatascience.com/learn/building-ai-agents): Agent loops, tool use, structured outputs, planning, reflection, memory, multi-agent systems, evaluation, and guardrails (Pro — Module 1 free)
- [AI Evals: Test, Measure & Ship LLM Apps](https://letsdatascience.com/learn/ai-evals): Error analysis, deterministic and semantic scorers, LLM-as-judge alignment, confidence intervals, regression gates, and production evals (Pro — Module 1 free)
## Source-Led History Archive
The History Archive contains eight free, interactive research timelines. Each record identifies the people involved, explains the contribution and its limits, and links to original papers, books, standards, or archival sources.
- [History Archive](https://letsdatascience.com/learn/history): Directory for all eight research timelines
- [History of Artificial Intelligence](https://letsdatascience.com/learn/history/artificial-intelligence): Logic, cybernetics, expert systems, machine learning, foundation models, and AI governance
- [History of Machine Learning](https://letsdatascience.com/learn/history/machine-learning): Formal neurons, perceptrons, statistical learning, deep learning, transformers, and scientific models
- [History of Large Language Models](https://letsdatascience.com/learn/history/large-language-models): Statistical language models, neural sequence models, transformers, scaling, instruction tuning, and reasoning
- [History of RAG, AI Memory, and Context Engineering](https://letsdatascience.com/learn/history/rag-and-context-engineering): Information retrieval, dense search, grounded generation, long context, persistent memory, and agentic retrieval
- [History of Generative and Multimodal AI](https://letsdatascience.com/learn/history/generative-and-multimodal-ai): Synthesis, GANs, diffusion, vision-language systems, image, audio, and video generation
- [History of AI Agents](https://letsdatascience.com/learn/history/ai-agents): Planning, reinforcement learning, tool use, computer use, MCP, A2A, evaluation, and control
- [History of Databases and SQL](https://letsdatascience.com/learn/history/databases-and-sql): Early data processing, relational theory, SQL, distributed systems, warehouses, lakehouses, and vector search
- [History of Statistics and Probability](https://letsdatascience.com/learn/history/statistics-and-probability): Games of chance, population records, inference, Bayesian and frequentist methods, causality, and computation
## Quick Recall
- [Quick Recall Hub](https://letsdatascience.com/quick-recall): Interview flashcards and SQL exercises — free, no signup
- [Interview Flashcards](https://letsdatascience.com/quick-recall/setup): 496 cards across SQL, Python, Statistics, ML, and LLMs
- [SQL Recall](https://letsdatascience.com/quick-recall/sql-recall): 33 lessons + 71 interactive SQL exercises across 11 topics
## Blog
- [Blog](https://letsdatascience.com/blog): Deep-dive articles with interactive code playgrounds and audio versions
- [System Design](https://letsdatascience.com/blog/system-design): ML system architecture and design patterns
- [Machine Learning](https://letsdatascience.com/blog/machine-learning): Algorithms, evaluation, feature engineering
- [GenAI & LLMs](https://letsdatascience.com/blog/genai-llms): Language models, retrieval, agents, and evaluation
- [AI & Technology](https://letsdatascience.com/blog/ai-technology): Research-led explainers on AI infrastructure, energy, business, and policy
- [Career](https://letsdatascience.com/blog/career): Data science careers, interview preparation, and portfolios
- [Data](https://letsdatascience.com/blog/data): Analysis, visualization, and engineering
- [News](https://letsdatascience.com/blog/news): AI industry analysis and commentary
## AI & Data Science News
- [AI News Games](https://letsdatascience.com/news/games): Four free browser games for people who follow AI news — three daily editions at 6 AM ET plus a larger Monday crossword; no account is required to play
- [AI News Quiz](https://letsdatascience.com/news/games/quiz): Daily five-question quiz built from recent verified AI headlines and figures, with the exact source window and links back to each story
- [AI Word](https://letsdatascience.com/news/games/word): Daily five-letter deduction game tied to a major AI story, with six attempts and exact repeated-letter scoring
- [AI Mini Crossword](https://letsdatascience.com/news/games/mini-crossword): Daily connected mini crossword combining current AI news with familiar AI vocabulary
- [Weekly AI Crossword](https://letsdatascience.com/news/games/crossword): Larger Monday crossword built from the week's defining AI stories and evergreen concepts
- [Games Leaderboard](https://letsdatascience.com/news/games/leaderboard): Server-verified weekly play standings plus provisional monthly prize standings that prioritize consistent eligible participation and require human review
- [Editorial Standards](https://letsdatascience.com/editorial-standards): How automated news processing, sourcing, corrections, named review, and Search approval are separated
- [Corrections Policy](https://letsdatascience.com/corrections): How readers can report an error and how material corrections are documented
## Optional
- [Pricing](https://letsdatascience.com/pricing): Pro subscription — $9.99/mo or $79.99/yr (unlocks all Pro course modules)
- [Privacy Policy](https://letsdatascience.com/privacy): Data handling practices
- [Terms of Service](https://letsdatascience.com/terms): Usage terms
