Agentic way to Data Analytics: Streamline Analytics Pipeline

Analyze gen AI adoption and impact across industries via a quadrant framework, highlighting high adoption and impact in finance, healthcare, and e-commerce, with opportunities in education, legal, compliance, and agriculture.

Leverage generative AI in finance to enhance risk management, fraud detection, and personalized customer services at scale, driven by high fidelity data and robust infrastructure.

Analyze why marketing and advertising show high adoption of generative AI for personalized campaigns, driven by efficiency and data-driven content, while impact remains low due to quality and trust concerns.

Explore how AI and data analytics can transform agriculture by enabling precision farming, pest and disease detection, and sustainable practices, while addressing barriers like infrastructure, cost, awareness, and data gaps.

Explore how data analytics drives generative AI across industries, from acquiring and cleaning data to machine learning and predictive or generative outcomes, with AI agents streamlining the analytics pipeline.

Trace the evolution of data analytics from BI dashboards to AI and Gen AI, highlighting big data, ML models, GANs, GPT, and the rise of AI delegation.

Discover how gen AI transforms the data analytics pipeline—obtaining and cleansing data, engineering features, modeling, inference, and generating automated insights via NLP, predictive analytics, and visualizations.

Explore how Gen AI transforms the data analytics pipeline in phase one, highlighting prompt engineering, retrieval augmented generation, and domain-focused fine-tuning.

Explore phase two's expansion of data analytics with multi-modal models, text, audio, and video, and emphasize agentic design, explainable AI, and self-supervised learning for practical analytics pipelines.

Explore how cogeneration, retrieval augmented generation, and agent-based analysis enhance the obtain, scrub, explore, model, and interpretations steps of the data analytics pipeline, with prompt engineering and code generation.

Master prompt engineering for data analytics by iterating prompts with large language models and using Lang chain to manage prompt history, enabling ideation and code generation.

Learn how code generation platforms like Copilot, ChatGPT, Claude, and Databricks support building a data science pipeline. Prompt engineering guides loading, cleaning, feature engineering, modeling with random forest, and interpretation.

Learn retrieval augmented generation (rag), grounding large language models with your own data via a vector/document database and search middleware, while weighing privacy risks and platform options.

Explain the agentic design pattern by defining agents, tasks, and tools to load, clean, and output a csv for analysis workflows.

Learn to set up an Azure Databricks environment, create a workspace, and manage notebooks and compute during a free trial to run data analytics experiments.

learn to obtain new york taxi data sets using spark and sql in azure databricks, filtering by year, exploring catalog, and generating data queries with an ai assistant.

Perform exploratory data analysis in databricks by saving sql queries to data frames, plotting distributions of numerical features, and visualizing New York taxi data to inform modeling.

Build a linear regression model in PySpark using NYC taxi data from data catalog, with trip distance, pickup zip code, and dropoff zip code, and evaluate with rmse and r2.

Learn how to evaluate a linear regression model using synthetic data, applying consistent transformations, and interpreting RMSE and R-squared while testing real-world fare predictions.

Learn how retrieval augmented generation grounds LLMs with external real world knowledge by retrieving relevant context, embedding and indexing it into a vector database, and generating accurate, context rich responses.

See how Rag enables fast, accurate answers from internal docs and large knowledge bases across customer support, enterprise knowledge retrieval, healthcare, legal research, and personalized education.

Assess the risks and benefits of retrieval augmented generation in analytics pipelines, including accuracy improvement through grounding, bias and privacy concerns, dynamic updates, and customization without retraining.

Explore the rag ecosystem powering analytics pipelines with cloud hosting platforms, vector databases, and frameworks like LangChain, LlamaIndex, and Bai deepest for production-grade retrieval augmented generation.

Use a no-code rag system in cloud AI to analyze PDFs like Nvidia’s 2024 10-K. Learn limitations of free plans and how retrieval augmented generation explains data insights.

Set up your Python environment for retrieval augmented generation by configuring LangChain and OpenAI, installing Python 3.12, and preparing a dotenv file with a UTF-8 OpenAI key.

Build a streamlit based rag app using OpenAI embeddings, FAISS vector store, and Pi PDF loader to convert pdf or CSV into chunks for retrieval question answering with OpenAI chat.

Understand tabular data rag challenges and why text embeddings fail for tables. Learn to use a pandas data frame agent for accurate counts and averages on diabetes data.

Apply a pandas data frame agent to translate queries into real pandas code for tabular data, avoiding text-based LLMs. Learn to count rows and compute averages.

Use the pandas DataFrame agent in a rag Streamlit app to analyze tabular data, perform data-frame queries, and explore a multi-agent system for choosing the right analysis agent.

Learn how agentic AI moves from prompt-based gen AI to autonomous workflows, enabling data cleaning, adaptive analytics, and automated ETL through integrated code generation and runtime execution.

Discover how agentic AI ecosystems empower autonomous, perceptive, and action-oriented agents to collaborate in multi-agent systems using tools, roles, and orchestration for data analytics.

Explore how a pandas dataframe agent using langchain analyzes the diabetes data set, capturing intermediate steps, tool usage, and the agent's thought process to derive insights.

Streamline the data analysis pipeline with an agentic approach, loading data into pandas, cleaning missing values, generating plots, and exporting a comprehensive diabetes report pdf.

Shows building and running a Streamlit data agent app that loads a CSV, performs data quality checks, and yields three key insights from descriptive stats, plots, and diabetes indicators.