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Data Science

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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

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📈 تحلیل کانال تلگرام Data Science

کانال Data Science (@datascienceiot) بازیگری فعال است. در حال حاضر جامعه شامل 41 791 مشترک است و جایگاه 3 224 را در دسته فناوری و برنامه‌ها و رتبه 15 220 را در منطقه روسيا دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 41 791 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 29 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -138 و در ۲۴ ساعت گذشته برابر -23 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 6.03% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 2.45% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 2 520 بازدید دریافت می‌کند. در اولین روز معمولاً 1 024 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 0 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند llm, агентов, api, октября, разработчиков تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
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/3...

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 30 ژوئن, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامه‌ها تبدیل کرده‌اند.

41 791
مشترکین
-2324 ساعت
-747 روز
-13830 روز
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Although this challenge effectively boils down to good communication, this can be difficult within a single organization—let alone across the multiple organizations and partnerships that IoT initiatives often require to be successful. In this future post, I’ll share some examples to demonstrate how seriously you should take even the little changes. I’ll also offer some strategies for effectively communicating and managing these changes

Top Challenges to Successful IoT Initiatives — And How to Overcome Them In the past few years that I’ve spent in the IoT industry, helping to develop, deploy, and improve IoT solutions involving millions of sensors, I’ve learned that often the biggest hurdles to success aren’t technological. A Gartner study last year found that only 26 percent of surveyed companies were successful with their IoT initiatives. This is an abysmal statistic, but it doesn’t surprise me given the many I’ve personally seen—many of which are purely operational or organizational. Although many issues associated with IoT deployments aren’t technological, they’re equally painful and difficult to overcome. And if every organization has to overcome these challenges in a void, a 74 percent failure rate is likely to continue. But here at Leverege, we believe in openly sharing the knowledge and insights that we’ve gained through our extensive experience, because it’s this open sharing of knowledge that will help us all to move forward together, collectively amplifying our human potential. So in this series, I’ll be exploring the issues we’ve seen, with examples taken from our experiences, and how you can overcome similar challenges as you pursue your own IoT initiatives. Whether you’re a solutions provider building new solutions for clients, or you’re one of the organizations integrating IoT technologies into your current systems and processes, I hope this series of posts will equip you with knowledge and strategies that will help you to succeed. If you’re relatively new to IoT and the various technologies and terminologies associated with it, I encourage you first to read the Intro to IoT eBook I wrote. This is a comprehensive (but simple!) introduction to and explanation of many important concepts that I’ll assume readers know as I explore various challenges. I’ll save the in-depth exploration for future posts, but here are just a few of the topics to come: You Need to Associate We’re increasingly using sensors and devices to allow us to represent physical objects and their attributes digitally. For example, in asset tracking applications, you’ll likely have a tracker that’s attached to a given asset you’d like to track. The asset is the thing we want to track, but the tracker is the thing that’s capturing and sending data. Therefore, you need to know that this tracker is on this asset, which we call “association” or “pairing.” For most asset tracking applications, someone needs to manually associate a tracker to a given asset, and when you introduce a manual step, there are bound to be problems. From labeling issues during manufacturing to operational issues like employees simply not associating trackers to the assets, I’ll share some of the challenges we’ve faced with IoT Association and how you can avoid or overcome them. Little Changes Are Big Changes in IoT Initiatives The Internet of Things is often called “a system of systems,” and with good reason: successful IoT initiatives usually involve a combination of hardware, software, and connectivity, which is then tied into business processes and operations. Because of the complexity and systemic codependence, a simple change in one part of the system—or subsystem—can effectively break the entire system of systems. Let’s say that you want to make some changes to the firmware on your sensor/device to help reduce the battery drain, so you reduce the number of messages sent from the sensor/device per day. Great! You’ve just added on a few months of battery life! But unbeknownst to you, your IoT system uses the number of messages from the sensors/devices to flag when a sensor/device may be defective. After making this change, you suddenly have countless sensors/devices being flagged by the system as defective, which at best hurts user confidence and at worst means that the system itself doesn’t work (e.g. if defective sensors/devices are automatically prevented from being used).

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Architecting an IoT Solution with Google Cloud & LoRa FREE WEBINAR ON IOT CLOUD & CONNECTIVITY Feb 28th, 2019 2:00 PM EST Are you building an IoT solution? With the vast number of connectivity and cloud platform options available, designing, building, and scaling an IoT project can be a daunting task. Our team has been using Google’s Cloud IoT Core as build large-scale IoT deployments while also collaborating with the Google team, giving us deep, under-the-hood knowledge. We’ll share what we've learned building and rapidly scaling enterprise IoT solutions within the Google IoT Core ecosystem. Sign up for our free webinar. Let's build the future! - Learn how to choose the right IoT cloud platform based on your specific needs - Discover what we find useful about the Google IoT Core ecosystem - Learn more about the different modes of connectivity that work best in industrial settings, with a focus on LoRa - Absorb best practices for using GCP to build IoT solutions quickly and at scale Link for register: https://www.leverege.com/webinars/gcp-webinar

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Data Science - آمار و تحلیل کانال تلگرام @datascienceiot