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𝗧𝘂𝗿𝗻 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗜𝗻𝗱𝗶𝗮'𝘀 𝗟𝗮𝗿𝗴𝗲𝘀𝘁 𝗘𝘅𝗮𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗕𝗼𝗱𝘆! The National Testing Agency (NTA) is building a data-first Communications & Outreach function to give leadership decision-ready intelligence on sentiment, coverage and emerging issues during high-stakes examination cycles. We are inviting applications for the position of 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗰𝘂𝗺 𝗠𝗲𝗱𝗶𝗮 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 at our New Delhi Head Office. If you can pair analytical rigour with real-time media monitoring — reading a news cycle before it breaks — this role puts you at the analytics backbone of JEE (Main), NEET (UG), CUET, UGC-NET and other flagship examinations. 𝗧𝗵𝗲 𝗞𝗲𝘆 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗮𝗯𝗹𝗲𝘀: 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: Track and analyse social-media sentiment, reach, and engagement; build dashboards and branded intelligence reports with clear visualisation and narrative; measure campaign performance and surface A/B insights. 𝗠𝗲𝗱𝗶𝗮 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Execute daily monitoring across print, broadcast, digital, and social; compile bilingual daily digests; apply tagging frameworks and archive verified evidence. 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 & 𝗘𝘀𝗰𝗮𝗹𝗮𝘁𝗶𝗼𝗻: Analyse tone, reach, share of voice, and misinformation patterns; operate escalation protocols for high-risk stories, viral posts and misinformation; deliver exam-cycle coverage reports. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Prepare briefings ahead of DG media interactions and Parliament sessions — ensure leadership is never surprised by the news cycle. 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 & 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆: Improve monitoring infrastructure — dashboards, automated alerts, and a searchable repository of verified clippings, sentiment data, and past reports. 𝗪𝗵𝗼 𝗪𝗲 𝗔𝗿𝗲 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗙𝗼𝗿: 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻: Master's degree in Mass Communication, Journalism, Media Studies, Public Policy, Statistics, Data, or related field. Exceptional Bachelor's candidates may be considered. 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: Minimum 3 years of combined experience in data analytics, media monitoring, and communications insights. 𝗧𝗼𝗼𝗹𝘀: Advanced Excel/Google Sheets, SQL, Power BI/Tableau/Data Studio; hands-on with Meltwater, Brandwatch, Sprinklr, Talkwalker or equivalents. 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲: Python or R, NLP/sentiment analysis, Hindi-language text analytics, public-sector or large-scale institutional exposure. 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: New Delhi (NTA Headquarters) 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Contractual — initially 2 years, renewable 𝗥𝗲𝗺𝘂𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: As per industry standards; commensurate with experience 𝗟𝗮𝘀𝘁 𝗗𝗮𝘁𝗲 𝘁𝗼 𝗔𝗽𝗽𝗹𝘆: 𝟮𝟭 𝗔𝘂𝗴𝘂𝘀𝘁 𝟮𝟬𝟮𝟲 𝗔𝗽𝗽𝗹𝘆 𝘃𝗶𝗮 𝗲𝗺𝗮𝗶𝗹: dir-admin@nta.gov.in 𝗙𝘂𝗹𝗹 𝗱𝗲𝘁𝗮𝗶𝗹𝘀, 𝗲𝗹𝗶𝗴𝗶𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿𝗺𝗮𝘁 (𝗔𝗻𝗻𝗲𝘅𝘂𝗿𝗲-𝗜): #NTA# #NationalTestingAgency# #DataAnalyst# #MediaMonitoring# #DataAnalytics# #Insights# #PowerBI# #Tableau# #SocialListening# #Hiring# #JobsInDelhi# #CareersAtNTA# #Communications#

⚡ Make aggregations over billions of records up to 100x faster by adding one line at the top of your query, with statistically rigorous confidence intervals built in. That's approximate queries in Elasticsearch ES|QL. Title: Approximate queries in Elasticsearch ES|QL: 100x faster on billions of records, with built-in confidence intervals URL: 📝 Overview Elasticsearch 9.4 adds approximate query execution to ES|QL. Just prepend SET approximation = true; to an existing query and automatic sampling and extrapolation kick in, with no query rewrites needed. ❓ Challenges Solved Exact aggregations over billions of documents are costly because compute scales linearly with row count. That hampered interactive exploration and real-time dashboards on large indices. 💡 Methodology & Proposed Approach ・Sampling happens at the Lucene layer, reading only the sampled documents, so I/O and compute savings are proportional to the sampling rate ・The query runs on the sample, then results are automatically scaled up to represent the full dataset ・Confidence intervals are computed rigorously via a bootstrap over sub-partitions of the sample ・Each result carries a certified flag indicating whether formal statistical guarantees hold 🎯 Use Cases An agent can sweep billions of documents in sub-second time, narrow down candidates, and zoom into exact queries only where needed. It also speeds up dashboards and pattern detection over massive logs. 📊 Results ・On ClickBench, an average of 23x with confidence intervals, peaks around 100x per query, and up to ~300x without interval computation ・Since sampling cost stays constant, speedup grows with dataset size ・Supported aggregations include COUNT, SUM, AVG, MEDIAN, PERCENTILE, and STD_DEV, with rows and confidence_level tuning accuracy versus speed #Elasticsearch# #DataAnalytics#

Ready to sharpen your data and AI skills this fall? The Databricks Advanced Learning Festival runs through October 14, with self-paced learning pathways across Data Engineering, Data Analytics, Machine Learning, Generative AI Engineering, Apache Spark™ and Data Warehousing. Complete at least one listed pathway during the event window to receive 50% off any Databricks Certification and 20% off a yearly Databricks Academy Labs subscription! Explore the pathways and enroll through Customer Academy:

𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 3) 1. Artificial Intelligence + Data Analyst 2. Machine Learning + Data Science 3. Cloud Computing + Web Development 4. Ethical Hacking + Hacking 5. Data Analytics + DSA 6. AWS Certified + IBM COURSE 7. Data Science + Deep Learning 8. BIG DATA + SQL COMPLETE COURSE 9. Python + OTHERS 10 MBA + HANDWRITTEN NOTES (72 Hours only ) Cost About - $500 To get: - 1. Follow (So I can DM you ) 2. Like & retweet 3. Reply " Send "

peak at some of the work we do for our lending / borrowing cluster coming on @eulerfinance ... imo data analytics is conquered but AI is miles from doing any decent quant finance. I've been writing a paper recently using fable and it required huge rafts of edits. What is was really good for was finding holes in logic and filling out the blanks but in most cases as you can see from the first two pages it’s just a pile of slop that needs almost a total rewrite. The way in which it words things makes it sound arrogant and annoying and frankly confusing to read. The logic had some major flaws I had to correct in a few places too. The edits are dense throughout all 25 pages.

More infrastructure options for SAP customers. At #ApsaraConference2026#, Irfan Khan, President and Chief Product Officer, SAP Data & Analytics, SAP, highlighted Alibaba Cloud’s expanding support for SAP customers, with more certified machine types available to run enterprise solutions. Explore more: #AgenticEra# #AgentNative# #AIAgentsAtApsara# #BringYourAgent# #AlibabaCloud#

📢 𝗝𝗨𝗦𝗧 𝗜𝗡: $ORCL ORACLE and Buck Institute Partner to Advance AI-Driven Aging Research 👉 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: ➤ 𝗢𝗿𝗮𝗰𝗹𝗲 and 𝗕𝘂𝗰𝗸 𝗜𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗲 collaborate on predictive aging research. ➤ Buck will use 𝗢𝗿𝗮𝗰𝗹𝗲 𝗟𝗶𝗳𝗲 𝗦𝗰𝗶𝗲𝗻𝗰𝗲𝘀 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. ➤ Platform encompasses over 𝟭𝟮𝟮 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 de-identified longitudinal health records. ➤ Researchers aim to identify biomarkers enabling 𝗲𝗮𝗿𝗹𝘆 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 and intervention. ➤ Research targets the first FDA-grade 𝗜𝗻𝘁𝗿𝗶𝗻𝘀𝗶𝗰 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲. ➤ Researchers plan models predicting 𝗮𝗴𝗲-𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝗿𝗶𝘀𝗸 before disease onset. ➤ Collaboration supports the Stanford-led 𝗧𝗛𝗥𝗜𝗩𝗘 research consortium. ➤ THRIVE is part of 𝗔𝗥𝗣𝗔-𝗛'𝘀 𝗣𝗥𝗢𝗦𝗣𝗥 program. ➤ Oracle's platform combines 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 with real-world health data analytics. 👉 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: ➤ Could shift aging research toward earlier, 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 healthcare interventions. ➤ Large-scale longitudinal data could reveal signals years before 𝗰𝗵𝗿𝗼𝗻𝗶𝗰 𝗱𝗶𝘀𝗲𝗮𝘀𝗲. ➤ A validated vitality score could provide a standardized measure of 𝗵𝗲𝗮𝗹𝘁𝗵𝘆 𝗮𝗴𝗶𝗻𝗴. ➤ Research could accelerate identification and clinical testing of 𝗮𝗴𝗶𝗻𝗴-𝗿𝗲𝗹𝗮𝘁𝗲𝗱 interventions. 👉 𝗘𝘅𝗽𝗲𝗿𝘁 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁𝘀: 𝗗𝗿. 𝗗𝗮𝘃𝗶𝗱 𝗙𝘂𝗿𝗺𝗮𝗻, 𝗣𝗵𝗗, Buck Institute for Research on Aging: "Understanding how and why we age is one of the most complex challenges in modern medicine," said Dr. David Furman, PhD, Buck Institute for Research on Aging. "Having the right solution and accessible patient data to aid in the study, discovery, and measurement of health span is essential. Oracle Life Sciences Data Intelligence, together with the depth and breadth of its real-world data, will enable us to begin to link early clinical signals and long-term outcomes. We hope to fundamentally change how to predict, delay, and address age-related decline." 𝗦𝗲𝗲𝗺𝗮 𝗩𝗲𝗿𝗺𝗮, Executive Vice President and General Manager, Oracle Health and Life Sciences: "Buck Institute is helping lead one of the most ambitious efforts to transform research on aging into a predictive science, and Oracle is proud to provide the data and analytics foundation to accelerate this work," said Seema Verma, executive vice president and general manager, Oracle Health and Life Sciences. "By combining advanced AI-driven analytics with one of the industry's largest collections of de-identified longitudinal real-world health data, our solution can help researchers uncover earlier signals of age-related decline. This collaboration could reshape how we understand aging, and how to aid early intervention research to help people live healthier, longer lives."