DataSpoof

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Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data

India26 595 Education12 546...

📈 Analytical overview of Telegram channel DataSpoof

Channel DataSpoof (@dataspoof) in the English language segment is an active participant. Currently, the community unites 16 139 subscribers, ranking 12 546 in the Education category and 26 595 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 16 139 subscribers.

According to the latest data from 21 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -143 over the last 30 days and by -2 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.89%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
Thematic interests: Content is focused on key topics such as api, llm, pipeline, +9183182, engineer.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data”

Thanks to the high frequency of updates (latest data received on 22 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

16 139

Subscribers

-224 hours

-327 days

-14330 days

1 273

Post views

No data24 hours

No data48 hours

7.89%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

16 139

Top 250 aws Q&A.pdf1.77 MB

16 139

Important Interview Question On Spark ========================================= 1. Difference between RDD & Dataframes 2. What are the challenges you face in spark? 3. What is difference between reduceByKey & groupByKey? 4. What is the difference between Persist and Cache? 5. What is the Advantage of a Parquet File? 6. What is a Broadcast Join ? 7. What is Difference between Coalesce and Repartition? 8. What are the roles and responsibility of driver in spark Architecture? 9. What is meant by Data Skewness? How is it deal? 10. What are the optimisation techniques used in Spark? 11. What is Difference Between Map and FlatMap? 12. What are accumulator and BroadCast Variables? 13. What is a OOM Issue, how to deal it? 14. what are tranformation in spark? Type of Transformation? 15. Tell me some action in spark that you used ? 16. What is the role of Catalyst Optimizer ? 17. what is the checkpointing? 18. Cache and persist 19. What do you understand by Lazy Evaluation ? 20. How to convert Rdd to Dataframe? 21. How to Dataframe to Dataset. 22. What makes Spark better than Hadoop? 23. How can you read a CSV file without using an external schema? 24. What is the difference between Narrow Transformation and Wide Transformation? 25. What are the different parameters that can be passed while Spark-submit? 26. What are Global Temp View and Temp View? 27. How can you add two new columns to a Data frame with some calculated values? 28. Avro Vs ORC, which one do you prefer? 29. What are the different types of joins in Spark? 30. Can you explain Anti join and Semi join? 31. What is the difference between Order By, Sort By, and Cluster By? 32. Data Frame vs Dataset in spark? 33. 4.What are the join strategies in Spark 34. What happens in Cluster deployment mode and Client deployment mode 35. What are the parameters you have used in spark-submit 36. How do you add a new column in Spark 37. How do you drop a column in Spark 38. What is difference between map and flatmap? 39. What is skew partitions? 40. What is DAG and Lineage in Spark? 41. What is the difference between RDD and Dataframe? 42. Where we can find the spark application logs. 43. What is the difference between reduceByKey and groupByKey? 44. what is spark optimization? 45. What are shared variables in spark 46. What is a broadcast variable 47. Why spark instead of Hive 48. what is cache 49. Tell me the steps to read a file in spark 50. How do you handle 10 GB file in spark, how do you optimize it

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Over 100 Essential Concepts For Data Scientists.pdf1.17 KB

16 139

Important_𝗦𝗰𝗶𝗸𝗶𝘁_𝗹𝗲𝗮𝗿𝗻_Operations.pdf1.10 KB

16 139

Training Details_data_science-1.pdf6.34 KB

16 139

Linux Essential Operations - CheatSheet.pdf1.09 KB

16 139

https://www.instagram.com/p/C2JX6tdvUAj/?igsh=MWk3MGpwdnJpbTd4cQ== Follow on Instagram for more data science content

16 139

Roadmap of learning tableau

16 139

Tableau-1.pdf8.87 MB

16 139

Heros of deep learning (CNN)- Yann lecun

16 139

All YouTube Resources for DSA 🔥🔥 Important topic-wise playlist on youtube [BEST]💯💯 DSA full course-https://lnkd.in/drfYia2j ARRAYS - https://lnkd.in/dG69DAEZ STRING - https://lnkd.in/deWr9svh Dynamic Programming-https://lnkd.in/dGpVEHg8 Recursion-https://lnkd.in/dv6XUNyP Heap-https://lnkd.in/dZBJdr2W SLIDING WINDOW -https://lnkd.in/dxXNKFgQ Binary Search-https://lnkd.in/dCQRSiXq Stack-https://lnkd.in/dYqeH7ft HASHING -https://lnkd.in/dM77crfV Binary Trees-https://lnkd.in/dFSXYYFt Graph-https://lnkd.in/dfmi74sU TRIE -https://lnkd.in/d3e-wm_J SEGMENT TREE -https://lnkd.in/dytGUaGB COMPETITIVE PROGRAMMING ->(https://lnkd.in/d3z6jKE4)

16 139

https://www.linkedin.com/posts/abhishek-kumar-singh-8a6326148_artificialintelligenceai-learncode-programmingmemes-activity-7148193868826431488-sg_l?utm_source=share&utm_medium=member_android Roadmap for Microservices

16 139

Top 10 Leetcode Articles to identify patterns in DSA 🔥 : 1) Sliding Window Pattern : https://lnkd.in/gPZDh9Er 2) Two Pointers Pattern : https://lnkd.in/gd7mrf_D 3) Substring Problems Pattern : https://lnkd.in/gkAipSuF 4) Dynamic Programming-1 Pattern : https://lnkd.in/gjkjTwHD 5) Dynamic Programming-2 Pattern : https://lnkd.in/gNKrm_5N 6) Binary Search Pattern : https://lnkd.in/gSwxE2WN 7) Tree Pattern : https://lnkd.in/gmPEVK6v 8) Graph Pattern : https://lnkd.in/gNTrMUKb

16 139

Statistics For Data Science !.pdf1.29 MB

16 139

Sql injection for data science

16 139

SQL injection CyberSecurity.pdf1.58 MB