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Индия26 485 Образование12 528...

📈 Аналитический обзор Telegram-канала DataSpoof

Канал DataSpoof (@dataspoof) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 16 107 подписчиков, занимая 12 528 место в категории Образование и 26 485 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 16 107 подписчиков.

Согласно последним данным от 24 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -149, а за последние 24 часа — -10, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 7.90%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 0 просмотров. В течение первых суток публикация набирает 0 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 0.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как api, llm, pipeline, +9183182, engineer.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data”

Благодаря высокой частоте обновлений (последние данные получены 25 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.

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Подписчики

-1024 часа

-287 дней

-14930 день

1 273

Просмотры поста

Нет данных24 часа

Нет данных48 часов

7.90%

Коэффициент вовлеченности

Нет данных

Постов в день

Ads index

beta

Архив постов

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Data Science ML Roadmap.pdf3.72 KB

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Some of the links will not be working https://medium.com/coders-camp/230-machine-learning-projects-with-python-5d0c7abf8265

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217 Machine Learning Projects with Python Code.pdf1.66 MB

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The links of code is also in pdf

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How to build a website using chatgpt https://www.instagram.com/p/CqsnrcXPMYx/?igshid=YmMyMTA2M2Y=

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Data Engineering course 1. Master Python: https://lnkd.in/e5rCbvP8 2. Learn SQL: https://lnkd.in/efMKFkfX 3. Learn MySQL: https://lnkd.in/efk-Mi3c 4. Learn MongoDB: https://lnkd.in/eMKPWtqX 5. Dominate PySpark: https://lnkd.in/exwA2hKz 6. Learn Bash, Airflow & Kafka: https://lnkd.in/eyN6u2yd 7. Learn Git & GitHub: https://lnkd.in/eX_Q8s99 8. Learn CICD basics: https://lnkd.in/epKGivFY 9. Decode Data Warehousing: https://lnkd.in/eKnVbFAB 10. Learn DBT: : https://lnkd.in/eG9eaEuE 11. Learn Data Lakes: https://lnkd.in/eQ9xxAJT 12. Learn DataBricks: https://lnkd.in/ePZpCv86 13. Learn Azure Databricks: https://lnkd.in/eBij4akJ 14. Learn Snowflake: https://lnkd.in/erETmtFU 15. Learn Apache NiFi: http://bit.ly/43btwYy 16. Learn Debezium: http://bit.ly/3K6W5gL

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https://www.instagram.com/p/CqktXUrNeA_/?igshid=YmMyMTA2M2Y= Follow us on Instagram for more data science related contents and giveways

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List of popular ai tools

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Generative AI timeline Credit- David

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What are the various time series algorithms available for forecasting Source- Instagram www.instagram.com/dataspoof

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Various types of test used in statistics for data science T-test: used to test whether the means of two groups are significantly different from each other. ANOVA: used to test whether the means of three or more groups are significantly different from each other. Chi-squared test: used to test whether two categorical variables are independent or associated with each other. Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables. Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other. Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other. Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other. Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.

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How to get GpU class performance on your CPU LAPTOP

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What is online machine learning Online machine learning. Abhishek Singh Online machine learning Online machine learning is a type of machine learning that involves updating a model continuously based on new data points as they become available. In contrast to batch learning, where the model is trained on a fixed dataset, online learning adapts to new data incrementally and in real-time. Online learning is particularly useful in scenarios where data is constantly arriving and the model needs to be updated frequently to reflect the latest information. Examples include fraud detection, recommendation systems, and online advertising. In online learning, the model is initially trained on a small subset of the data, and as new data arrives, the model updates its parameters to incorporate the new information. The update process can be done using various algorithms, such as stochastic gradient descent or online gradient descent. Online learning has several advantages over batch learning, including the ability to adapt to changing data distributions, the ability to handle large datasets efficiently, and the ability to make real-time predictions. However, it also has some limitations, such as the need to carefully manage the learning rate to avoid overfitting, and the difficulty in handling non-stationary data streams.

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How to data preprocessing speed using polar library. Polar is a powerful data preprocessing library which support parallel processing.

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Join and share our telegram channel with your friends to learn data science, machine learning, big data and , deep learning

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Docker for data scientists

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Docker for Data Scientists (2).pdf1.77 MB

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Empower your web-app with API.pdf1.22 MB