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AI

ChatGPT in data science and analytics

Hands-on AI and data analytics workshops — built around your team's real cases.

Duration
24h · 3 days
Who it's for

Ideal for teams that…

1 Data scientists and analysts looking to automate and optimize analytics with AI
2 BI and reporting specialists searching for new ways of generating insights
3 Developers and implementers integrating ChatGPT with analytical tools
4 Managers and domain experts who want to understand AI’s potential in data workflows
5 Professionals familiar with data analysis and statistics who want to expand their expertise with AI
Outcomes after the program

Hands-on AI and data analytics workshops — built around your team's real cases.

Efficiently apply current language models (GPT-4o, ChatGPT-5, LLaMA 3, Mistral 7B, etc.) in analytics

Design prompts for analysis, insight generation, and reporting

Automate analytical workflows — from data ingestion to visualization and reporting

Select the right AI model for each task consciously and strategically

Clearly separate roles between ChatGPT and code (Python) to maximize efficiency

Use tools like Streamlit, LangChain, Flowise to build interactive analytics and automation

Integrate AI into analytical tools and business applications

Recognize risks, ensure data security, and monitor AI output quality

Program · 3 modules

What we actually do

Day 1: Introduction to ChatGPT in the context of Data Analytics

M01
Day 1: Introduction to ChatGPT in the context of Data Analytics
  • · Module 1: Current Landscape of Language Models in Data Science Architecture and capabilities of language models for analytics Overview of available models: GPT-4o, ChatGPT-5, LLaMA 3, Mistral 7B — differences in features, context length, file support Combining traditional analytical methods (statistics, ML) with AI Example applications in business analysis and reporting The future of AI in analytics — deeper integrations and multi-source data use
  • · Module 2: Prompt Engineering, Role Division, and Data Handling Designing effective prompts for data exploration and synthesis Handling multiple data formats: CSV, JSON, Excel — preparation and extraction Advanced data cleaning, transformation, and enrichment using LLMs Exploratory Data Analysis (EDA) assisted by generative tools Clear role separation: when ChatGPT generates code (Python, Pandas/NumPy), and when it directly analyzes data via API Integration of ChatGPT with Python for data automation Workshops: practical scenarios with CSV, JSON, and Excel datasets

Day 2: Automating Analyses and Practical AI Applications

M02
Day 2: Automating Analyses and Practical AI Applications
  • · Module 3: AI-Powered Data Exploration, Transformation, and Visualization Automated filtering, segmentation, and aggregation Generating statistical interpretations and visualizations — choosing measures and chart types Building and validating predictive models with LLM assistance Automated time-series analysis and forecasting Workshop: turning “raw” data into actionable insights using Python + ChatGPT
  • · Module 4: Report Generation and Workflow Automation Creating dynamic summaries, narratives, and recommendations from data Building pipelines that automate analysis and reporting Data storytelling: interactive dashboards and visualizations Overview of tools and frameworks: Streamlit, LangChain, Flowise — practical examples for building interactive apps and automated workflows Workshop: building a simple automation pipeline for data import, analysis, and reporting

Day 3: Integrations, Security, and Best Practices

M03
Day 3: Integrations, Security, and Best Practices
  • · Module 5: Deploying AI in Production and Monitoring Implementation and integration strategies in business environments Quality control, monitoring, and real-time optimization of AI outputs Managing analytics projects with AI as a decision-support tool
  • · Module 6: Security, Ethics, and Responsible AI Use Risks and pitfalls: incorrect or biased outputs, data issues, model bias Best practices for data security and GDPR compliance Monitoring AI quality and error-handling mechanisms Case studies of AI adoption in analytics and lessons learned
Every module is adapted to your stack and context. The above is a starting point — not a fixed agenda.
How we work

From brief to retro in 30 days.

01

Brief & diagnosis

A call with the team lead + a short survey for participants. We define goals, gap and context.

02

Program customization

We adapt modules, case studies and code examples to your stack. Approval in 5 days.

03

Workshop

Trainer-led sessions, hands-on, code review. Mentor available between sessions too.

04

Retro + report

Outcome report for the team and lead. 30 days of consulting included.

Inquiry

Send a brief. We'll reply within 1 day.

After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.

Quote within 48h of the brief
First session within 30 days
Pilot before the full decision
VAT invoice, payment in instalments possible

How we handle your data

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