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Data Science

Data Science Add us as a preferred source on Google

Telegram @datascienceiot
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🔄 Data last refreshed 1 month ago
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📣Sponsored postOne post to the channel with your link and creative. $380→ 📌Pinned postA sponsored post pinned to the top of the channel for the mo… $1,140/mo→ 📍Post + pin comboA sponsored post that also stays pinned for a set number of … $608→ 🔁Forward / repost packageI forward your channel's posts to mine through the month — d… $760/mo→ 🔗Channel-description linkYour link in my channel description for the month. $570/mo→ 🎁Giveaway postYou supply the prize — I run a giveaway (Telegram's native g… $304→ 📦Recurring placement (retainer)Several sponsored posts per month at a package rate — always… $1,520/mo→ 🤝Cross-channel shoutoutWe shout each other out / mutual-forward — a value swap betw… Swap→ 🎬UGC HireBranded content for your own channels & ads from $148→ 🎙️Podcast guest spotInvite Data Science onto your podcast (~45 min, remote) $148→ 📞1:1 ConsultPaid video call — advice, coaching or Q&A $75/hr→

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Subscribers
42.2K
🎁 Free analysisfor Data Science
🕵️Fake Followers CheckReal-audience % & bot risk→ 📊Engagement rateBenchmarked vs. peers→ 💰What they chargeFair sponsored-post price→

Known for

Linear Algebra for Computer Science 📓 Read @datascienceiot9.1K views Linear Algebra for Computer Science 📓 Read @datascienceiot Updated 2204-page PDF Mathematics ebook: "Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning" Find it here: https://cis.upenn.edu/~jean/gbooks/geomath.html5.2K views Updated 2204-page PDF Mathematics ebook: "Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning" Find it here: https://cis.upenn.edu/~jean/gbooks/geomath.html Understanding Machine Learning: From Theory to Algorithms — фундаментальная книга по математическим основам машинного обучения. Внутри: empirical risk minimization и переобучение PAC learning и uniform convergence VC dimension и No-Free-Lunch theorem bias–complexity tradeoff линейные модели, boosting и AdaBoost выбор модели и валидация выпуклая оптимизация, регуляризация и устойчивость Книга вы4.9K views Understanding Machine Learning: From Theory to Algorithms — фундаментальная книга по математическим основам машинного обучения. Внутри: empirical risk minimization и переобучение PAC learning и uniform convergence VC dimension и No-Free-Lunch theorem bias–complexity tradeoff линейные модели, boosting и AdaBoost выбор модели и валидация выпуклая оптимизация, регуляризация и устойчивость Книга вы Game Theory: http://arxiv.org/abs/1512.06808 ————— #GameTheory #Gamification #Mathematics #Statistics #Probability @datascienceiot4.6K views Game Theory: http://arxiv.org/abs/1512.06808 ————— #GameTheory #Gamification #Mathematics #Statistics #Probability @datascienceiot

📣 Post reach

4.1K
Avg views / post
9.8%
Reach vs followers

🔥 Top post: Linear Algebra for Computer Science 📓 Read @datascienceiot · 9.1K views

Recent posts

View on Telegram ↗
🔥 Top post Linear Algebra for Computer Science 📓 Read @datascienceiot 👁 9.1K
"Patterns, Predictions, and Actions: A Story About Machine Learning" 📓 Read @datascienceiot 👁 2.4K Сложный индустриальный ML, работа с генеративными подсказками, уровни дообучения и агентский Cotype — на E-CODE 2026 трек ML&DS снова впечатляет. Программа ещё пополняется. Но уже сейчас понятно, что успевшая заслужить высокоранговую слав… 👁 2.5K Andrej Karpathy just dropped 12-page PDF on "Graph Engineering" for multi-agentic systems the shift: Karpathy's loop runs 700 experiments and forgets all of them. A graph remembers forever here's the full system: step 1 → build one loop… 👁 3.2K Updated 2204-page PDF Mathematics ebook: "Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning" Find it here: https://cis.upenn.edu/~jean/gbooks/geomath.html 👁 5.2K Understanding Machine Learning: From Theory to Algorithms — фундаментальная книга по математическим основам машинного обучения. Внутри: empirical risk minimization и переобучение PAC learning и uniform convergence VC dimension и No-Free-… 👁 4.9K 🔥 Хочешь быстрее расти в IT? Хватит учиться в одиночку В IT прокачивается тот, кто каждый день видит сильные идеи, новые инструменты, реальные задачи, вакансии и разборы. Окружение решает больше, чем кажется. Собрал папки и каналы, где … 👁 3.8K Galois Theory of Algorithms Noson S. Yanofsky∗ 📓 Read @datascienceiot 👁 3.1K SQL и Python есть: что часто отсекает на собеседовании Классика вакансий на аналитика: SQL и Python на уровне «уверенно», статистика — «понимание методов A/B-тестирования и работы с большими выборками». Первые две галочки ставятся спокойн… 👁 3.2K Applied Causal Inference Powered by Machine Learning and AI 📓 Read @datascienceiot 👁 3.4K Game Theory: http://arxiv.org/abs/1512.06808 ————— #GameTheory #Gamification #Mathematics #Statistics #Probability @datascienceiot 👁 4.6K he Little Book of Generative AI Foundations: An Intuitive Mathematical Primer 📓 Read @datascienceiot 👁 4.4K

📊 Channel activity & format

Posting cadence
4.1 / week
Posts several times a week — consistently active for a campaign window.
Content format
Mostly images
An image/graphic-led channel — suits announcements, drops and visual promos. Recent: 0 video · 11 image.

💡 Facts

👁️Averages 4.1K views per post.
🏅Earned the 10K Subscribers for passing 10K subscribers.

🕵️ Fake follower check

Estimated
41/100
Low Credibility score
~69%
Mixed Real audience
Medium Fake-follower risk
Low Data confidence
  • Est. 69% real, active audience · Medium fake-follower risk.
  • Low engagement (~9.8% of followers engage each post) — a sign of an inflated or inactive audience.

Low confidence — few signals are public for this listing, so treat it as a rough screen. Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.

About

DS По всем вопросам- @haarrp @ai_machinelearning_big_data - machine learning @pythonl - Python @itchannels_telegram - 🔥 best it channels @ArtificialIntelligencedl - AI @pythonlbooks-📚 @programming_books_it -📚 Реестр РКН: https://clck.ru/3Fk3zS

📸 Gallery

"Patterns, Predictions, and Actions: A Story About Machine Learning" 📓 Read @datascienceiot Сложный индустриальный ML, работа с генеративными подсказками, уровни дообучения и агентский Cotype — на E-CODE 2026 трек ML&DS снова впечатляет. Программа ещё пополняется. Но уже сейчас понятно, что успевшая заслужить высокоранговую славу конференция Ozon Tech снова станет одним из ключевых событий в индустрии. Осталось только пройти модерацию, чтобы убедиться лично: https://ecode.ozon.tech/. Andrej Karpathy just dropped 12-page PDF on "Graph Engineering" for multi-agentic systems the shift: Karpathy's loop runs 700 experiments and forgets all of them. A graph remembers forever here's the full system: step 1 → build one loop: generate, critique, revise. 630 lines, 700 experiments in 48 hours step 2 → go parallel: agents in separate worktrees, same repo, different branches, no confl Updated 2204-page PDF Mathematics ebook: "Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning" Find it here: https://cis.upenn.edu/~jean/gbooks/geomath.html Understanding Machine Learning: From Theory to Algorithms — фундаментальная книга по математическим основам машинного обучения. Внутри: empirical risk minimization и переобучение PAC learning и uniform convergence VC dimension и No-Free-Lunch theorem bias–complexity tradeoff линейные модели, boosting и AdaBoost выбор модели и валидация выпуклая оптимизация, регуляризация и устойчивость Книга вы 🔥 Хочешь быстрее расти в IT? Хватит учиться в одиночку В IT прокачивается тот, кто каждый день видит сильные идеи, новые инструменты, реальные задачи, вакансии и разборы. Окружение решает больше, чем кажется. Собрал папки и каналы, где можно быстрее влиться в нужное направление, следить за трендами и не вариться в своём пузыре. AI: t.me/ai_machinelearning_big_data Python: t.me/pythonl Linux: t Galois Theory of Algorithms Noson S. Yanofsky∗ 📓 Read @datascienceiot Applied Causal Inference Powered by Machine Learning and AI 📓 Read @datascienceiot

🔀 Audience overlap

Estimated

Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

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