现已上线！2025 年 Telegram 研究 — 年度关键洞察 
DataSpoof
前往频道在 Telegram
Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data
显示更多📈 Telegram 频道 DataSpoof 的分析概览
频道 DataSpoof (@dataspoof) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 16 138 名订阅者，在 教育 类别中位列第 12 559，并在 印度 地区排名第 26 707 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 16 138 名订阅者。
根据 20 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -151，过去 24 小时变化为 0，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 7.89%。内容发布后 24 小时内通常能获得 N/A% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 0 次浏览，首日通常累积 0 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 0。
- 主题关注点： 内容集中在 api, llm, pipeline, +9183182, engineer 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Learn Data Science
https://dataspoof4081.graphy.com/membership
Artificial Intelligence
Machine Learning
Data Science
Deep learning
Computer vision
NLP
Big data”
凭借高频更新（最新数据采集于 21 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 教育 类别中的关键影响点。
16 138
订阅者
无数据24 小时
-397 天
-15130 天
帖子存档
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Update 1002 students already enrolled in seats. Now you can get this course in 399 inr
https://www.udemy.com/course/aws-certified-solutions-architect-associate-saa-c03-m/?couponCode=9AA1B1B8F7772FA461B7
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Get our AWS course at Rs 399
https://www.udemy.com/course/aws-certified-solutions-architect-associate-saa-c03-m/?couponCode=9AA1B1B8F7772FA461B7
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My Amazon SDE Interview experience for the reference of all freshers applying:
(FYI, Amazon just dropped their SDE-1 India University Graduate openings!)
⏳ The process:
1️⃣ 1 Online Assessment
2️⃣ 2 Coding rounds
3️⃣ 1 Coding + Leadership Principles round
💻 The interviews:
1️⃣ OA round:
7 basic code-debugging MCQs
2 DSA questions:
- LC 2265. Nodes Equal to Average of Subtree
- LC 68. Text Justification
1 (very lengthy) behavioral question form
- Solved all 7 debugging questions correctly.
- Solved first DSA problem in 10 mins.
- Partially solved second problem, failing few test cases.
- This round went average. But got the interview invite.
Being fast in contests and debugging would help in this round.
2️⃣ Coding round 1:
A BFS-based LeetCode hard problem.
- Quickly coded a BFS + hashmap solution.
- Interviewer had cross-questions but appeared satisfied overall.
If you can solve LC 127. Word Ladder, you’d be fine.
3️⃣ Coding round 2:
> Q1: Nodes at distance K in a binary tree
- Used BFS after creating parent pointers using HashMap.
> Q2: Connect ropes with minimum cost
- Implemented a greedy solution using a priority queue.
- Interviewer liked my speed but gave another problem.
> Q3: Max steps with reduced m
- Gave O(n) solution, then optimized using binary search to O(log n) and later to O(log(sqrt(m))).
Overall, pleasant interview with optimized solutions.
All of the above problems:
- LC 863. All Nodes Distance K in Binary Tree
- GFG. Connect n ropes with minimum cost
- Problem 3 not on the internet. Here’s a playground for it - https://lnkd.in/gsg2Pnmp
4️⃣ Coding + Managerial round:
- LC Hard; Smallest substring in ’s’ containing ’t’ as subsequence
- Came up with a sliding window approach.
- Took 30+ min to explain and code the approach.
- Interviewer was satisfied with my approach, but couldn’t finish coding completely.
- Overall, explained the concept but could have implemented faster.
If you have done LC 76. Minimum Window Substring, you got this one.
Behavioral Questions:
[1] Internship Discussion:
- Day-to-day responsibilities?
- Technologies you worked with, and why?
- Any accomplishments or key learnings?
[2] Amazon Leadership Principles:
- Time when you went above and beyond to meet a customer’s needs? (Customer Obsession)
- Time when you had to make a quick decision with limited information? (Bias for Action)
Decent answers in behavioral round as I had prepped for similar questions.
🎯 Result:
My interview result was positive and a few weeks later, I got the life-altering SDE-1 offer from Amazon
Credit- Harshit sharma
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Overfitting happens when a model learns too much detail from training data, including noise, rather than general patterns.
Result: The model performs well on training data but poorly on new, unseen data.
Symptoms: High accuracy on training data, low accuracy on test data.
Cause: Model is too complex (e.g., too many layers, features, or parameters).
Example: Memorizing answers for a specific test rather than understanding concepts.
Solution: Simplify the model, use regularization techniques, or gather more data.
Purpose of Avoiding Overfitting: Ensures the model can generalize and make accurate predictions on new data.
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We are launching a premium memberships for our followers
Currently it's contains AWS course for data scientist
Soon we will add following courses
1- Complete MLOPS
2- complete Data analyst
3- Complete ML engineer
4- Complete big data analyst
Support us by becoming a premium member and enjoys the benefits
https://dataspoof4081.graphy.com/membership
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Complete roadmap of azure data engineers
https://www.instagram.com/p/DBVKLOmTb0j/?igsh=MTFzdGpiYTQ5azNwZg==
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+1
Capgemini Training wrapup
If you are not connected me on LinkedIn you can connect there
https://www.linkedin.com/posts/abhishek-kumar-singh-8a6326148_datascience-machinelearning-ai-activity-7253045178930774016-EVT2?utm_source=share&utm_medium=member_android
