Generative AI and Agentic Workflows for Data Professionals Training Course

Course Introduction

Generative AI and Agentic Workflows for Data Professionals Training Course provides a practical and advanced exploration of how generative AI and autonomous agent technologies are transforming modern data work. The program examines how data professionals can use large language models, multimodal AI, retrieval systems, intelligent automation, and agentic workflows to accelerate data preparation, analysis, documentation, engineering, visualization, reporting, and decision-support activities while maintaining appropriate quality and governance controls.

The course focuses on practical applications of generative AI throughout the data lifecycle, from data discovery and profiling through engineering, analytics, governance, and communication. Participants learn how to formulate effective instructions, structure AI-assisted analytical tasks, generate and review SQL, automate repetitive data operations, interpret complex datasets, produce analytical narratives, and develop reusable workflows. Emphasis is placed on combining AI capabilities with professional data practices rather than treating generated outputs as automatically reliable.

Participants explore the transition from individual generative AI interactions to structured agentic workflows. They learn how agents can plan tasks, use tools, retrieve information, execute actions, maintain context, evaluate outputs, and coordinate multiple steps. The course covers important technologies including retrieval-augmented generation, vector databases, semantic search, APIs, tool calling, agent memory, workflow orchestration, knowledge graphs, and multi-agent collaboration, with particular attention to how these capabilities apply to everyday data professional responsibilities.

A major focus is placed on AI-assisted data engineering and analytics. Participants investigate how generative AI can support SQL development, code generation, data transformation, pipeline documentation, testing, quality analysis, exploratory data analysis, visualization, forecasting, and reporting. They also explore autonomous workflows capable of connecting several activities into coordinated processes, such as receiving a business question, locating appropriate data, preparing the dataset, conducting analysis, validating findings, generating visualizations, and producing a structured report.

The course addresses the risks and limitations that data professionals must understand when adopting generative AI and agentic technologies. These include hallucinations, inaccurate SQL, fabricated sources, data leakage, prompt injection, biased outputs, inappropriate automation, weak validation, privacy concerns, access-control failures, and model dependency. Participants learn practical techniques for verification, grounding, testing, provenance, human review, secure tool access, responsible prompting, and output evaluation to ensure that AI-assisted data work remains reliable and accountable.

By completing Generative AI and Agentic Workflows for Data Professionals Training Course, participants gain practical capabilities for integrating modern AI into data engineering, analytics, governance, and business intelligence workflows. The program helps professionals move from basic AI experimentation toward structured, reusable, secure, and measurable AI-enabled processes that can reduce manual effort, accelerate analytical delivery, improve documentation, and expand the capacity of data teams.

Duration

10 days

Who Should Attend

• Data analysts seeking to use generative AI for faster analysis, visualization, reporting, and recurring analytical workflows.

• Data engineers interested in AI-assisted coding, pipeline development, testing, documentation, and intelligent workflow automation.

• Data scientists applying generative AI and agentic systems to exploratory analysis, modeling, experimentation, and analytical research.

• Analytics engineers developing automated SQL, transformation, testing, semantic modeling, and documentation workflows.

• Business intelligence professionals creating AI-assisted dashboards, reports, narratives, and conversational analytical applications.

• Data architects evaluating how generative AI and agents can integrate with enterprise data platforms and analytical ecosystems.

• Data governance professionals exploring AI-assisted metadata, lineage, classification, stewardship, policy, and documentation activities.

• Database professionals using AI to improve SQL development, query optimization, documentation, troubleshooting, and database operations.

• Machine learning and AI professionals developing practical agentic workflows for data-intensive enterprise applications.

• Research and insights professionals using generative AI for information discovery, synthesis, analytical interpretation, and evidence-based reporting.

• Technology managers overseeing AI adoption, data modernization, automation, and productivity initiatives across data teams.

• Business and analytics leaders seeking practical understanding of how generative AI and agentic workflows can enhance data functions.

Course Objectives

• Explain the foundations of generative AI, large language models, multimodal AI, and agentic systems and their practical relevance to modern data professionals.

• Apply effective prompting and context-engineering techniques to improve the quality, consistency, relevance, and usefulness of AI-assisted data tasks.

• Use generative AI to accelerate SQL development, code generation, query explanation, optimization, debugging, documentation, and data transformation activities.

• Design agentic workflows that decompose complex data tasks into structured stages involving planning, retrieval, tool use, execution, validation, and reporting.

• Apply retrieval-augmented generation to connect AI systems with trusted enterprise datasets, documentation, metadata, policies, and domain-specific knowledge.

• Use generative AI for data profiling, cleaning, transformation, exploratory analysis, anomaly investigation, visualization, and analytical storytelling.

• Integrate AI agents with databases, APIs, notebooks, cloud platforms, orchestration tools, business intelligence environments, and analytical applications.

• Develop human-in-the-loop workflows that combine autonomous AI capabilities with professional validation, approval, exception handling, and accountability.

• Evaluate AI-generated outputs for factual accuracy, analytical correctness, data consistency, source quality, reproducibility, security, and operational reliability.

• Identify and mitigate risks involving hallucinations, prompt injection, sensitive-data exposure, biased outputs, inaccurate code, unauthorized tool access, and excessive automation.

• Apply generative AI and agentic techniques to improve data documentation, metadata management, knowledge sharing, reporting, and collaboration across data teams.

• Develop practical strategies for implementing and scaling AI-enabled data workflows while controlling costs, managing risks, protecting information, and measuring productivity gains.

Comprehensive Course Outline

Module 1: Generative AI Foundations for Data Professionals

• Evolution of artificial intelligence from traditional machine learning to generative AI, foundation models, and agentic data workflows.

• How large language models process context, generate responses, use tools, and support different categories of professional data tasks.

• Differences between conversational prompting, generative AI applications, workflow automation, and autonomous agent-based systems.

• Practical opportunities, limitations, adoption considerations, and productivity implications of generative AI across data professions.

Module 2: Prompt Engineering and Context Engineering for Data Work

• Designing precise prompts that communicate analytical objectives, data context, constraints, expected formats, and validation requirements.

• Applying role, task, context, examples, constraints, decomposition, and verification techniques to improve AI-generated data outputs.

• Managing long-context information, structured instructions, reusable prompt templates, and domain-specific context for complex data tasks.

• Emerging context-engineering approaches for improving agent performance through dynamic information selection, memory, tools, and workflow state.

Module 3: Generative AI for SQL and Data Querying

• Using generative AI to create, explain, debug, optimize, document, and validate SQL queries across common database environments.

• Translating natural-language analytical questions into structured queries while maintaining correct business definitions and data relationships.

• Applying AI-assisted query optimization, error diagnosis, schema interpretation, joins, aggregations, filtering, window functions, and analytical calculations.

• Emerging text-to-SQL and agentic database systems that dynamically inspect schemas, construct queries, validate results, and refine analytical workflows.

Module 4: AI-Assisted Data Engineering

• Applying generative AI to data ingestion, transformation, pipeline development, testing, orchestration, documentation, and operational troubleshooting.

• Using AI agents to analyze pipeline dependencies, identify failures, generate corrective suggestions, and coordinate engineering tasks.

• Automating repetitive engineering activities while maintaining version control, testing standards, security practices, and human review.

• Emerging autonomous data engineering capabilities including self-healing pipelines, adaptive transformations, intelligent testing, and automated optimization.

Module 5: Generative AI for Data Preparation and Quality

• Using AI to profile datasets, identify missing values, duplicates, inconsistencies, anomalies, and potential data quality problems.

• Generating transformation suggestions, validation rules, cleansing logic, standardization procedures, and data preparation documentation.

• Designing AI-assisted quality workflows that combine automated detection with deterministic validation rules and human approval processes.

• Emerging intelligent data quality systems that continuously monitor datasets, investigate anomalies, identify root causes, and recommend remediation.

Module 6: Retrieval-Augmented Generation for Data Professionals

• Understanding retrieval-augmented generation and its role in grounding AI outputs in authoritative enterprise data and technical documentation.

• Designing retrieval workflows using embeddings, vector databases, semantic search, keyword search, metadata filters, and hybrid retrieval.

• Connecting AI systems to data dictionaries, schemas, policies, documentation, reports, knowledge bases, and other trusted information sources.

• Advanced grounding approaches for improving factual accuracy, source attribution, freshness, contextual relevance, and analytical reliability.

Module 7: Agentic Analytics and Autonomous Data Workflows

• Designing agents that plan and execute multi-step analytical tasks involving data discovery, preparation, analysis, validation, and reporting.

• Coordinating tools, databases, notebooks, APIs, retrieval systems, and analytical applications through structured agent workflows.

• Applying task decomposition, planning, memory, tool selection, execution monitoring, and result validation to autonomous data processes.

• Emerging autonomous analytics workflows capable of adapting their analytical steps according to objectives, findings, exceptions, and changing data conditions.

Module 8: AI-Assisted Data Science and Advanced Analytics

• Applying generative AI to exploratory data analysis, feature engineering, statistical investigation, visualization, experimentation, and model development.

• Using AI agents to investigate patterns, generate analytical hypotheses, compare methods, document experiments, and interpret analytical outputs.

• Combining generative AI with statistical and machine learning techniques while maintaining methodological validation and reproducibility.

• Emerging AI-assisted data science environments that automate portions of experimentation, analytical investigation, documentation, and model workflows.

Module 9: AI for Data Visualization and Analytical Storytelling

• Using generative AI to recommend visualizations, generate analytical narratives, explain trends, and transform findings into accessible business insights.

• Designing AI-assisted workflows for charts, dashboards, executive summaries, recurring reports, and interactive analytical communication.

• Applying validation processes to ensure visualizations accurately represent source data, statistical results, business definitions, and analytical context.

• Emerging natural-language analytics and autonomous reporting systems that dynamically generate visual and narrative outputs from governed data sources.

Module 10: AI for Metadata, Documentation, and Data Governance

• Using generative AI to create and maintain data dictionaries, metadata descriptions, technical documentation, business definitions, and catalog information.

• Applying AI agents to classification, lineage interpretation, policy mapping, stewardship workflows, and governance documentation activities.

• Designing AI-assisted governance processes that combine automated recommendations with established organizational policies and human accountability.

• Emerging intelligent metadata systems that continuously enrich enterprise data catalogues and connect technical assets with business meaning.

Module 11: Multi-Agent Workflows for Data Teams

• Designing specialized agents for engineering, analysis, quality, governance, documentation, visualization, research, and reporting responsibilities.

• Coordinating multiple agents through delegation, collaboration, verification, task dependencies, shared context, and structured communication.

• Establishing role boundaries, permissions, memory, escalation procedures, and validation mechanisms across multi-agent data workflows.

• Emerging multi-agent systems for collaborative data teams that distribute complex analytical and engineering workloads among specialized AI agents.

Module 12: AI Security, Privacy, and Responsible Data Practices

• Protecting sensitive data when using generative AI through access controls, data minimization, privacy safeguards, and secure deployment architectures.

• Addressing prompt injection, malicious instructions, data leakage, unauthorized tool use, insecure retrieval, and other AI-specific security threats.

• Applying responsible AI principles covering transparency, accountability, explainability, fairness, human oversight, and appropriate automation boundaries.

• Emerging security practices for agentic systems involving tool permissions, runtime controls, agent identity, audit trails, and continuous threat monitoring.

Module 13: Evaluation, Testing, and AI Output Quality

• Developing evaluation frameworks for assessing AI-generated code, queries, analytical findings, summaries, visualizations, and workflow decisions.

• Testing AI systems against hallucinations, incorrect calculations, misleading interpretations, incomplete information, adversarial prompts, and data inconsistencies.

• Monitoring agent behavior through tool calls, retrieval quality, latency, token usage, costs, errors, task completion, and output quality.

• Emerging evaluation approaches for long-running agents, non-deterministic workflows, adaptive systems, continuous improvement, and production environments.

Module 14: Enterprise AI Integration and Deployment

• Integrating generative AI and agentic workflows with databases, cloud data platforms, data lakes, warehouses, APIs, notebooks, and BI systems.

• Designing secure deployment architectures across cloud, hybrid, private, and enterprise environments with appropriate identity and access controls.

• Managing model selection, inference costs, latency, scalability, reliability, observability, versioning, and operational support requirements.

• Emerging AI-native data platforms and integrated development environments designed for intelligent data engineering and autonomous analytics.

Module 15: Emerging Trends and Issues in Generative and Agentic AI

• Exploring reasoning models, computer-use agents, autonomous coding, multimodal AI, agent-to-agent communication, and AI-native data operations.

• Examining emerging issues involving reliability, controllability, intellectual property, privacy, workforce transformation, regulation, and organizational accountability.

• Assessing the growing role of autonomous AI in data engineering, analytics, governance, business intelligence, and knowledge-intensive workflows.

• Evaluating adoption barriers, infrastructure requirements, workforce skills, implementation risks, technological uncertainty, and evolving enterprise AI capabilities.

Module 16: Implementation Strategy and Practical Capstone

• Identifying high-value generative AI and agentic use cases according to business impact, feasibility, data readiness, risk, and process complexity.

• Developing implementation roadmaps covering technology selection, workflow design, governance, security, integration, change management, and skills development.

• Designing practical capstone workflows that combine generative AI, data tools, retrieval, agents, analytics, validation, and automated reporting.

• Establishing continuous improvement frameworks for measuring productivity, quality, reliability, cost, adoption, business value, and operational performance.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.