data analyst – use of LLM and AI in analysis, insight generation, and business decision-making

Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

Smarter LLM use in analytics - You will learn where LLMs truly speed up analysis and where they introduce risk, so you can make better decisions about when AI should support your work and when it should not.

Effective analytical prompting - You will build prompts that guide the model toward useful answers, reliable formats and actionable output, instead of wasting time on vague requests, retries and endless corrections.

Stronger SQL workflows - You will practice generating, refactoring and reviewing SQL with AI support, so you can turn business questions into analysis faster and spot logic or performance issues earlier.

Faster insight generation - You will learn how to use AI for result summaries, hypothesis creation and product insight generation, helping you move from raw data to business recommendations with less delay.

More reliable test reading - You will learn how AI can support experiment design and A/B test interpretation, while also recognizing the risks of automated conclusions before they affect product decisions.

Fewer analytical mistakes - You will use sanity checks and methods for spotting anomalies, missing data and hallucinated figures, reducing the chance of presenting flawed insights or misleading KPI results.

Practical AI integration - You will see how to connect a language model with databases, code and business context, so AI becomes a useful copilot in ad hoc analysis, documentation and team workflows.

Safer data handling - You will learn when to use cloud or local setups, how to anonymize data and how to work with open-source models, so you can analyze sensitive information with less operational risk.

Training programme

1. Introduction to AI and LLM models in the analytical context

what LLM models are and how they work, deterministic vs probabilistic AI; strengths and limitations of LLMs in data analysis,
where AI realistically helps the analyst, and where it harms,
the evolution of the analyst's role: from „query operator” to „systems orchestrator”,
token economics and context window management RLM models – selecting the model for the task,
AI systems architecture: how to connect a language model with a database and code.

2. Prompt Engineering for data analysis – fundamentals

how the LLM model „thinks” and what this means for prompts, the structure of an analytical prompt,
context engineering, data preparation (Markdown, JSON, XML, CSV),
system prompt – defining roles, constraints and output formats,
generation and refactoring of SQL queries,
translating business requirements into analytical queries,
analysis of existing queries (readability, optimization, edge cases),
„memory” of AI systems – RAG, Agentic RAG, vector databases and graph databases.

3. Analytical cases – working with data and insights

automatic summaries of analysis results, generation of product insights and hypotheses, sanity checks: detection of anomalies, gaps, illogical results,
creation and validation of KPIs,
interpretation of A/B test results with the help of AI, identification,
of potential errors in data and analyses.

4. A/B tests and experiments using AI

AI support in designing experiments,
formulating test hypotheses, interpretation of statistical results,
supporting inference and communication of results,
risks of automatic interpretations of A/B tests,
„synthetic users” (AI Personas).

5. Workshops: real team scenarios

working on real examples from the team,
optimization of existing analytical prompts,
testing different variants of prompts,
comparing results and quality of responses,
assessment of the usefulness of AI in specific use cases.

6. Integration of AI with the analyst's daily work

AI as the analyst's „copilot”, not a replacement,
use of AI in:
- ad-hoc analyses,
- data exploration,
- analysis documentation,
- communication of insights to the business.
automation of repetitive analytical tasks,
best practices of teamwork with AI.

7. Quality, ethics and safety of working with AI

validation of results generated by AI, the analyst's responsibility for the results, how not to „let through” an incorrect insight,
methods of detecting hallucinations in numbers and facts,
auditability and „Explainable AI”, „LLM-as-a-Judge”,
running open-source models (Llama 3, Mistral, Gemma) using (Ollama, LM Studio),
analysis of sensitive data without sending it to the cloud (Local-First Analytics),
data anonymization and masking techniques before sending to external APIs. Hybrid approach,
good organizational practices.

8. Summary and recommendations for the team

when to use AI, and when classical methods,
checklists for analytical work with AI,
the recommended workflow of an analyst supported by AI,
areas for the team's further development.

What are the prerequisites for participating in the training?

Data analysis basics - You should be comfortable working with tabular data, filtering, aggregation and result interpretation, so you can focus on AI usage rather than core analytics fundamentals.

SQL knowledge - You should be able to read and write basic SQL queries, including SELECT, JOIN, GROUP BY and WHERE, because the training expands this area with practical AI support.

Business context awareness - You should understand how to frame business questions, KPIs and analytical hypotheses, because the training focuses on translating business needs into AI-assisted analysis.

Hands-on workshop readiness - You should be ready to test prompts, compare model outputs and assess answer quality on examples, because a large part of the training is practical and workshop-based.