1.6K
SAData Interpretation Mind Map + Important Formulas 📊 | UGC NET Paper 1 Quick Revision
@sameera.fernaz
#Data Governance Frameworks
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19.3K
AIMost people think AI = chatbots. In reality, it’s a stack.
7 layers:
• Data sources: APIs, docs, logs, IoT, SaaS
• Data prep & management: cleaning, normalization, embeddings, governance
• Models: base models, fine-tuning, adapters, safety, eval
• Orchestration: frameworks, context, workflows, guardrails
• Inference: optimized reasoning, caching, scaling, safety filters
• Integration: APIs, SDKs, auth, connectors, observability
• Applications: copilots, RAG, automations, domain agents
Each layer builds on the last. Remove one and the system falters.
Why it matters:
• Developers: know where to innovate
• Enterprises: blueprint to scale responsibly
• Leaders: see where value and risk emerge
AI isn’t just prompts. It’s an ecosystem.
Which layer will move fastest in the next year?
linkedin / Brij Kashore Pandey
@aitoolhub.co
8.2K
TEComment “Graph” to get the details
This open-source tool runs 100% in your browser:
GitNexus indexes your entire project into a structured knowledge graph—mapping dependencies, call chains, clusters, and execution flows—so nothing important gets missed (even by AI agents).
Here’s how it works:
→ Paste a public GitHub repository URL (or upload a ZIP)
→ It parses the code using Tree-sitter (AST-based parsing, not regex hacks)
→ Extracts functions, classes, imports, and relationships
→ Builds an interactive graph visualization in the browser (D3. js)
→ Generates a call graph and dependency map across the project
- Follow @techwith.ram for more such content
@techwith.ram
275
MAStruggling to harness the power of your organization’s data while ensuring security and compliance? Data Governance Unlocked is your ultimate guide to transforming chaos into clarity!
🌟 What You’ll Learn:
✅ The Pillars of Data Governance: Discover how to build a rock-solid foundation for data quality, accessibility, and trust.
✅ Compliance Made Simple: Navigate GDPR, CCPA, and other regulations without breaking a sweat.
✅ Data Security Hacks: Protect sensitive information from breaches with cutting-edge practices.
✅ Unlocking Business Value: Turn data into actionable insights that drive growth and innovation.
✅ Real-World Case Studies: Learn from industry leaders who’ve successfully implemented data governance frameworks.
🔍 Why Watch?
Whether you’re a data professional, IT leader, or business executive, this video equips you with actionable steps to future-proof your data strategy. Say goodbye to siloed systems and hello to streamlined, ethical, and efficient data management!
💡 Key Takeaways:
- How to align data governance with organizational goals.
- Tools & technologies revolutionizing data stewardship.
- Avoid common pitfalls that derail governance initiatives.
#EnterpriseArchitecture #EAFramework
#BusinessArchitecture #ITArchitecture #SolutionArchitecture #BusinessProcessModelling #InformationArchitecture #TechnologyStandards #DigitalTransformation #EnterpriseITStrategy #SystemIntegration #EnterpriseSolutionDesign #ArchitectureGovernance #ITInfrastructurePlanning #DataModelling #ApplicationPortfolioManagement #EnterpriseArchitecture #TOGAF
#Zachman
@madina.doup
14.7K
ASThis is the EXACT order I would learn Data Science in 2026.
Hi 😊 my name is Dawn. I’ve been a Data Scientist at Meta, Patreon and other startups. And have coached 20+ clients into landing their dream Data jobs in the past year.
1️⃣ Learn SQL
SQL is a must-have skill for every data professional because it’s the primary way you get data OUT of a database.
It’s also a very easy coding language to learn, so I would start there.
Use Interview Master to learn and practice SQL (link in bio):
→ Learn SQL: www.interviewmaster.ai/content/sql
→ Practice SQL: www.interviewmaster.ai/home
2️⃣ Start building Product Sense & Business Sense
Product sense & business sense basically means you know how to use Data to solve real problems.
I would start building this “soft” skill early because (1) it takes time to really learn this, and (2) as you’re learning Stats and Python, you already have context on how these might be used in the real world.
I found the book: Cracking the PM Career to be super helpful before I landed my first Data Science job.
3️⃣ Learn Statistics
How much Stats do you need for Data Science? Just the foundations, but you need to know it really really well.
→ Descriptive statistics
→ Common distributions
→ Probability and Bayes’ Theorem
→ Basic Machine Learning models
→ Experimentation concepts
→ A/B experiment design
Check out Stanford’s Introduction to Statistics, which is free on Coursera.
4️⃣ Learn Python
Python is the #1 skill for Data Scientists in 2025, but I put it 4th on this list because I find that it builds on skills 1-3.
I learned Python on my own using DataCamp’s Python Data Fundamentals (link in bio).
5️⃣ Use AI-assisted coding tools
Many data scientists are already using tools, like Claude Code & Cursor, to 2x their productivity.
And also many companies are evaluating you on your use of AI during interviews.
#datascience #datascientist
@askdatadawn
87
NEMost people think data protection is just policies…
But it’s more about making sure those policies actually work in real life.
If you’re in this field (or trying to get in), this is what really matters.
Save this for later you’ll need it.
Which one do you find hardest?
#DataProtection #InformationGovernance #GDPR #DataGovernance #DPO
@nellyingovernance
2.3M
CHThe DAL framework streamlines your executive dashboards - whether you're using Excel, Tableau, or another business intelligence tool - and highlights only what truly matters. Here's how it works:
D: Data-to-Ink Ratio
Level up your data visualizations by removing unnecessary elements. Every chart, label, or color should serve a clear purpose. An uncluttered design ensures critical Key Performance Metrics (KPIs) and insights stand out.
A: Anchoring
Place metrics within meaningful context -whether you're benchmarking in Excel or comparing trends in Tableau. Anchoring your data helps viewers instantly grasp its importance and make informed decisions.
L: Layout
Our eyes naturally scan in a Z-pattern: top-left to top-right, diagonally to bottom-left, and then across to bottom-right. Position high-impact metrics in the top-left to grab attention and guide viewers through your data story.
By applying DAL, you'll create effective dashboards that engage stakeholders, highlight key insights, and drive better data-driven decisions.
All data shown in the video is sample data and it does not resemble any real life events.
@chartosaur
160.6K
JEComment “project” for my full video that breaks each of these projects down in detail with examples from my own work.
If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT!
Save this video for later + send to a data friend!
#data #dataanalytics #project
🏷️ data analytics, data analytics project, data project, dataset
@jessramosdata
15.7K
TEAfter working as a data engineer, here are 5 things I wish I knew earlier:
1. It’s not just SQL or Python
Data engineering isn’t about syntax
It’s about moving data reliably between systems and transforming it correctly along the way
2. Testing data is surprisingly hard
Testing backend code is straightforward → input vs expected output
In data engineering → massive datasets, multiple columns, edge cases… validating correctness is a real challenge
3. It gets harder as you grow
Junior role → write SQL / PySpark pipelines.
Senior role → design architecture, ensure data governance, manage scalability, reliability, and costs.
4. “Pipelines once built are done” — wrong
Data pipelines break. Schemas change. Upstream systems fail.
Maintenance and monitoring are ongoing responsibilities, not one-time work.
5. “More tools = better engineer” — myth
Knowing 10 tools doesn’t matter.
Understanding fundamentals (data modeling, distributed systems, trade-offs) is what actually scales your career.
If you focus only on coding, you’ll plateau early.
If you understand data systems, you’ll grow fast.
💾 Save this for when the role starts feeling more complex than expected
💬 Comment if you’ve felt this shift already
🔁 Follow to keep your thinking sharp as you grow in data engineering
@techwithprateek
172
PEWhat would it look like to do data differently?
Part 5 of Who Decides What Counts? looks at frameworks — from medical ethics to Indigenous data governance — that put community control, consent, and dignity at the center of how we collect and use information.
#DataJustice #Maine #WhoDecidesWhatCounts #IndigenousDataSovereignty
@permanentcommission.ritp
119.4K
VEIf you want to crack Data Science jobs in the next 30 days, here’s the three step process which you will follow which literally no one talks about.
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.
.
#datascience #data #interview
@vee_daily19
206
ACWhat makes leadership confident enough to approve an AI tool?
In regulated environments, it comes down to control and transparency.
This clip from our webinar Responsible AI in Regulated Environments highlights the importance of:
• Clear audit trails
• Responsible data handling
• Governance frameworks that build trust
AI innovation is powerful—but without governance, it introduces risk.
Watch the full webinar at the link in bio.
#ResponsibleAI #AIGovernance #AICompliance #InformationGovernance #DataGovernance #EnterpriseAI
@accesstoday
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Discover the latest #Data Governance Frameworks content without logging in. The most impressive reels under this tag, especially from @chartosaur, @jessramosdata and @vee_daily19, are gaining massive attention. View them in HD quality and download to your device.
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