Do the Data

Founder

Deloitte

Data Analytics Manager

Novartis

Lead a team of data analysts and oversee the design and implementation of healthcare data analytics projects

Develop and maintain healthcare data management systems, ensuring data accuracy, completeness, and security

Develop and implement data analysis plans to support clinical and operational decision-making processes

Collaborate with stakeholders across the organization to identify and prioritize business intelligence needs, and ensure that data analytics projects align with the overall strategic goals of the organization
Analyze large datasets using statistical and data visualization tools to identify trends, patterns, and insights that can be used to drive business performance

Ensure compliance with data privacy and security regulations such as HIPAA, and work with legal and compliance teams to address any data-related issues

Develop and deliver presentations to communicate complex data analysis to non-technical stakeholders, including executive leadership, and make recommendations for process improvements based on data-driven insights

Stay up-to-date with the latest trends and technologies in healthcare data analytics and provide guidance on how the organization can leverage them to improve performance and patient outcomes

Data Analytics - Turning Coffee into Insights, One Caffeine-Fueled Query at a Time! | Healthcare Data | Financial Expert | Driving Business Growth | Data Science Consultant | Data Strategy

As a data analytics professional, I have a passion for turning raw data into meaningful insights that drive business decisions. With my experience in data visualization, statistical analysis, and data modeling, I can help your organization harness the power of data to optimize operations, improve customer experiences, and achieve your strategic goals.

🔍 Discovering Insights

My expertise lies in exploring complex data sets, uncovering hidden trends and patterns, and providing actionable insights that support informed decision-making. I can help you answer questions like "What are our customer's buying habits?" or "What factors are impacting our sales performance?", and use data to guide your business strategy.

🎨 Data Storytelling

I believe that data storytelling is an art form, and I love using creative and engaging data visualizations to communicate complex information in a way that's easy to understand. Whether it's through interactive dashboards or custom reports, I can help you see your data in a new light, and use it to tell the story of your business.

🧠 Continuous Learning

I'm a lifelong learner, and I'm always exploring new data tools and techniques to stay at the forefront of the industry. Whether it's machine learning, artificial intelligence, or predictive analytics, I'm excited to apply the latest advancements to help you gain a competitive edge.

🤝 Let's Connect

If you're looking for a data analytics professional who can help you unlock the power of your data, let's connect! Whether you're a startup or a large corporation, I'm excited to collaborate with you and help you achieve your business goals. Feel free to reach out at Ravenaravs@gmail.com

Python for Data Professionals: Why It’s the Preferred Choice

📍 Why is Python the go-to language for data roles?

Python isn’t just a programming language—it’s an essential tool for data professionals. Its simplicity, versatility, and vast ecosystem of libraries make it a top choice for handling complex data challenges with ease.

📍 Where does Python fit in the data ecosystem?

🔴 Data ingestion, cleaning, and transformation
🔴 Storage and orchestration
🔴 Visualization and reporting
🔴 Machine learning and model deployment

From building ETL pipelines to creating interactive dashboards, Python empowers data teams at every level.

📍 Why choose Python over Scala/Java?

While Scala and Java are excellent for large-scale data processing and system robustness, Python stands out for its flexibility. It’s a universal tool, adaptable across various data domains, making it the preferred choice for many professionals.
📍 Key data roles where Python is invaluable:
🔴 Data Analytics – Analyze data using Pandas, NumPy
🔴 Data Visualization – Create stunning visuals with Seaborn, Matplotlib, Plotly
🔴 Machine Learning – Build models with Scikit-learn, TensorFlow, Keras
🔴 Web Scraping – Extract data using Scrapy, BeautifulSoup, Selenium
🔴 Data Engineering – Implement pipelines with Spark, Kafka, Flink
🔴 Databases – Work with SQLite, PostgreSQL, MongoDB, MySQL

📍 Top free platforms to upskill in Python:
 - realpython.com
- pythonprogramming.net
- datacamp.com (free basic courses)
- w3schools.com/python
- https://lnkd.in/gCDhJvA2
- https://lnkd.in/grqYWfGW

 📍 Follow these Python experts for insights: 🐍

Matt Harrison, Khuyen Tran, Guido van Rossum, Dan Bader

Python remains an essential skill for data professionals—whether you're analyzing, engineering, or visualizing data, it has the tools to get the job done!

CC:Brij Kishore Pandey

Ravena  O

AI Researcher and Data Leader | Healthcare Data | GenAI | Driving Business Growth | Data Science Consultant | Data Strategy

José Lorenzo Sánchez Garnica

Ever wondered how companies actually build AI systems powered by LLMs?

It’s not just about calling an API and hoping for magic.

There’s an entire tech stack behind the scenes — and once you understand it, you’ll see how everything connects.

Let’s break it down step by step 👇

🟡 Step 1: The Brain (LLMs)
 Think of GPT, Gemini, or Claude as the intelligence layer.
 They understand, reason, and respond — but they need context, data, and direction.

🟡 Step 2: The Connectors (Frameworks)
 Frameworks like LangChain or LlamaIndex act as plumbing between your model and your tools.
 They let you combine the LLM with data sources, APIs, and logic — no need to reinvent the wheel.

🟡 Step 3: The Memory (Vector Databases)
 LLMs forget fast.
 That’s where databases like Pinecone, Weaviate, and Chroma come in — they store “embeddings” (a fancy word for text turned into numbers).
 It’s like giving your AI Google Search for your own data.

🟡 Step 4: The Fuel (Data Extraction)
 No data, no intelligence.
 Tools like Crawl4AI and FireCrawl pull information from PDFs, websites, and documents — then clean it so your AI can actually use it.

🟡 Step 5: The Open Source Edge
 Don’t want to rely on expensive APIs?
 Use open models from Hugging Face or run local ones via Ollama.
 Freedom + privacy + control = ✅

🟡 Step 6: The Language of Numbers (Embeddings)
 Before your text hits the vector DB, it must be converted into numerical form.
 That’s where OpenAI Embeddings or SBERT come in — enabling true semantic understanding.

🟡 Step 7: The Quality Gate (Evaluation)
Now test it.
Use tools like Giskard or Trulens to check for accuracy, hallucinations, and relevance.
Because every smart system needs smarter feedback loops.

💥 In short:
 1️⃣ Pick your LLM
 2️⃣ Connect it via a framework
 3️⃣ Feed it structured data
 4️⃣ Store it in a vector DB
 5️⃣ Add embeddings for meaning
 6️⃣ Use open tools for flexibility
 7️⃣ Evaluate relentlessly

This blueprint can help your team go from idea to intelligent system.

 🔁 Repost if you want your network to build smarter with AI.

CC: Rahul Agarwal

Bansari Kikani

Ever wondered how Agentic AI actually works behind the scenes?

Here’s a simple breakdown of how an AI agent transforms raw input → into intelligent output.

 Perfect for anyone curious about how autonomous AI systems really think. 

🟠 Input Sources
 Agentic AI starts by collecting data from multiple channels — knowledge bases, user queries, APIs, logs, and web data.

🟠  AI Processing
Once data is gathered, the agent gets to work — analyzing the query, reasoning through context, retrieving memory, planning tasks, selecting tools, and managing the entire flow.

🟠 3. Action Layer
 With a clear understanding of context, the agent performs real-world actions — making decisions, executing tasks, fixing errors, collaborating with other agents, and even scheduling future steps.

🟠 4. Output
 Finally, it delivers a response that’s accurate, relevant, and context-aware — the hallmark of truly autonomous intelligence.
That’s what sets Agentic AI apart from traditional chatbots or rule-based systems.

 💾 Save this post for your next deep dive into AI systems!

CC: Prem Natarajan

What is RAG (Retrieval-Augmented Generation)?
 
Curious how modern AI systems combine retrieval and generation to deliver smarter, more accurate responses?

 Let’s decode how RAG works 

🔹 What is RAG?
 RAG enhances Large Language Models (LLMs) by combining real-time data retrieval with text generation — producing responses that are not only fluent but also grounded in up-to-date, factual information.
Imagine asking an AI a question and it instantly pulls insights from the latest reports, PDFs, or knowledge bases — that’s RAG in action.

🔹 How It Works

1️⃣ Document Processing:
 Raw data — PDFs, videos, CSVs — are broken down into smaller, meaningful chunks.

2️⃣ Embedding:
 Each chunk is converted into a vector (embedding) that represents its meaning.

3️⃣ Retrieval:
 When a query comes in, it’s also embedded and compared against stored data to find the most relevant context.

4️⃣ Augmented Query:
 The retrieved information is added to the original query to enrich the prompt.

5️⃣ Generation:
 Finally, the LLM generates a context-aware answer — accurate, relevant, and grounded in your data.

🔹 Why It Matters
 RAG bridges the gap between static LLMs and dynamic knowledge — enabling real-world applications that are faster, more reliable, and enterprise-ready.

It’s the foundation behind modern AI assistants, enterprise chatbots, and domain-specific copilots.

 Repost if you found this useful!
 Follow me for more insights and practical breakdowns on Data & AI.

CC: Deepak Bhardwaj

 #AI #RAG #LLM #GenerativeAI #DataScience #ContextEngineering

freeCodeCamp  recently released a comprehensive AI Engineering Roadmap — and it’s surprising how many people missed it.

It’s not just another tutorial list.
It’s a reality check for anyone serious about building a career in AI.

Here are some key takeaways worth reflecting on 👇


🔴 1. Math is non-negotiable
Linear algebra, calculus, and probability form the backbone of every AI system.
If you can’t understand gradients, optimization, and loss functions — you’ll struggle to build anything meaningful.

🔴 2. Python remains the foundation
From TensorFlow and PyTorch to NumPy and Pandas — this ecosystem drives modern AI.
Fluency in Python isn’t optional; it’s expected.

🔴 3. LLMs aren’t everything
While everyone is chasing ChatGPT replicas, real impact (and opportunity) lies in building domain-specific models that solve practical business problems.

🔴 4. Depth beats credentials
Building and deploying CNNs, RNNs, and LSTMs will teach you far more than any certificate ever could.

🔴 5. Data work is underrated
Data cleaning, transformation, and validation consume most of an AI engineer’s time — and mastering these makes you truly valuable.

🔴 6. Prompt engineering isn’t the destination
It’s a useful skill — but true AI engineers design systems, not just prompts.

🔴 7. Deployment is the differentiator
A model sitting in a Jupyter notebook has no value.
Productionization is where engineering begins.

🔴 8. Generative AI goes beyond LLMs
Understanding GANs, VAEs, and foundational architectures separates professionals from hobbyists.

🔴 9. System design matters
AI solutions must scale. Knowing Kubernetes, Docker, and distributed systems is critical for real-world impact.

🔴 10. Ethics and bias awareness are essential
As AI adoption accelerates, responsible design and fairness aren’t optional — they’re requirements for trust and compliance.



In short:
The roadmap reminds us that AI engineering isn’t just about chasing trends — it’s about mastering fundamentals, building production-ready systems, and thinking responsibly about the technology create

AI Engineering Handbook

How do AI Agents actually “remember”?

 Let’s break down one of the most crucial — yet often overlooked — parts of Context Engineering: Agent Memory.

When we talk about an AI agent’s memory, we’re essentially referring to the context provided in prompts that helps it plan, reason, and act based on past interactions or data that’s not immediately available.

Here’s how we typically classify an agent’s memory 

🔹 1. Episodic Memory
 Stores the agent’s past interactions and actions.
 Think of it as the agent’s diary — actions and conversations are logged in persistent storage (often a vector database) so they can be recalled when needed.

🔹 2. Semantic Memory
 Holds the external information or internal knowledge the agent should “know.”
 This is similar to how RAG systems use context — grounding the model with relevant data for more accurate reasoning.

🔹 3. Procedural Memory
 Contains systemic details: the structure of system prompts, available tools, and guardrails.
 Usually maintained in prompt or tool registries, or even version-controlled repositories like Git.

🔹 4. Long-Term & Short-Term Memory
 Information retrieved from long-term memory is sometimes cached locally — this becomes the agent’s working or short-term memory.
 All of this combined forms the final context prompt sent to the LLM, enabling it to act intelligently.

 We often consider Episodic, Semantic, and Procedural as Long-Term Memory, while the Working Memory forms the Short-Term layer.

Ultimately, it’s all about how you architect your Agentic System’s memory topology — that’s what determines how “smart” your agent really feels.

Have you designed memory systems for AI agents?

 Would love to hear your experiences 

CC: Aurimas

#AI #LLM #ContextEngineering #AIagents #AgenticSystems

Aashish J.

Most RAG failures aren’t “model issues.”
They’re chunking issues. 🔍

I see this pattern everywhere: teams throw bigger models at the problem… while their chunking strategy quietly breaks context, splits ideas, and pulls the wrong knowledge at retrieval time.

So I put together a quick breakdown of 7 practical chunking approaches and when each one actually works.

Here’s the vibe:

🔴 Fixed-size chunks — predictable, but blind to meaning. Fine for logs or binary data.
🔴 Semantic chunks — driven by embeddings → best for RAG that depends on “understanding.”
🔴 Recursive chunks with overlap — my go-to when documents are messy or unstructured.

And yes — the other four are in the carousel. One of them is perfect when semantic models feel like overkill.

If you’re building or improving a RAG pipeline today, try this baseline:

🔴 Start with paragraph-level or recursive chunking (10–20% overlap)
🔴 Keep chunks tighter instead of maxing out context windows
🔴 Track which chunks your users actually retrieve — optimize the splitter, not just the model

Your chunking strategy is a design choice, not an afterthought.
Curious — what’s worked best for you? What trade-offs did you make in production?

CC: Bhavishya Pandit

7 Chunking Methods

K VamsiKrishna

Amazon Web Services (AWS) just dropped a new AI transformation blueprint — and it’s basically a cheat sheet for leaders trying to navigate AI in 2025.
It’s a tight, 22-page guide built with insights from Capgemini, IBM, PwC, and Devoteam — and it breaks AI transformation into five clear focus areas:

🔹 1. What actually drives AI success
 AWS outlines the core principles leaders need before thinking about models or tools.

🔹 2. How to build the right data backbone
 Devoteam breaks down what “AI-ready data foundations” look like in the real world.

🔹 3. Where agentic AI fits in
 Capgemini maps out practical agent-based use cases—not hype, actual workflows.

🔹 4. What responsible AI should look like
 IBM tackles governance, risk, trust, and the guardrails every enterprise needs.

🔹 5. How to take AI from prototype → production
 PwC explains the operational layers needed to scale AI without chaos.
If you’re shaping an AI roadmap for 2025, this is one of the cleanest frameworks released so far.

📘 Free executive playbook here: https://lnkd.in/e5VBUzsB

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