Training: ChatGPT in Data Science and Analytics – Advanced Applications
Level
AdvancedDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedTraining: ChatGPT in Data Science and Analytics – Advanced Applications
The “ChatGPT in Data Science and Analytics – Advanced Applications” training is a 2–3-day intensive course where theory (20%) meets practice (80%). Its goal is to demonstrate how to effectively use state-of-the-art language models in analytical processes and data processing. Participants will explore advanced prompt engineering, AI integration with data exploration and visualization tools, automation of analyses, and report generation. The course focuses on currently available models — such as GPT-4o, ChatGPT-5, and popular open-source models (LLaMA 3, Mistral 7B, etc.) — to ensure participants can make conscious choices about which model to use (considering context length, file handling, and specific capabilities).
Who is this training for?
Data scientists and analysts looking to automate and optimize analytics with AI-
BI and reporting specialists searching for new ways of generating insights
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Developers and implementers integrating ChatGPT with analytical tools
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Managers and domain experts who want to understand AI’s potential in data workflows
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Professionals familiar with data analysis and statistics who want to expand their expertise with AI
What will you learn?
- 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
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
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
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
