Coming soon - Intelligent Research Methods: Using AI to Analyze, Predict & Discover Data

Coming soon – Intelligent Research Methods: Using AI to Analyze, Predict & Discover Data

Training details

Location

Bucharest

Start Date

Target Audiance

Scientist

Teaching language(s)

Romanian / English

Organizing institution

ICI Bucharest

Delivery mode

On-site

Level

Intermediate

Format

Case Study Session, Lecture, Workshop

Topics / Keywords

AI for Research, Research Methods, Data Analysis, Predictive Analytics, Data Discovery, Literature Discovery, Generative AI, Visualization, Responsible AI

“Intelligent Research Methods: Using AI to Analyze, Predict & Discover Data” is a two-day course designed for researchers, academic staff, analysts and R&D professionals who want to integrate Artificial Intelligence into their research workflows. The course focuses on practical ways AI can support the discovery, organization, analysis and interpretation of information and data, without requiring participants to become AI developers.

What You Will Learn

After attending this course, participants will be able to:

Understand how Artificial Intelligence can support different stages of the research lifecycle, from exploration and data preparation to analysis, prediction and interpretation.
Identify suitable AI tools and methods for literature discovery, information synthesis, data exploration and research support.
Use AI-assisted approaches to prepare, structure, explore and analyze research data while maintaining human oversight of the process.
Understand the principles of predictive analytics and recognize when Machine Learning methods can support forecasting, classification and pattern identification.
Use AI to identify patterns, relationships, anomalies and potentially relevant insights in datasets and communicate findings through clear visualizations.
Critically evaluate AI-generated results, considering data quality, reproducibility, bias, privacy, research integrity and responsible AI use.

Agenda

DAY 1 – AI-ENABLED RESEARCH METHODS	From research questions to data-driven discovery
10:00 – 10:30	AI in the research lifecycle Overview of how AI can support research design, information discovery, data analysis, prediction, interpretation and communication of results.
10:30 – 11:30	AI-assisted literature and information discovery Using AI to search, organize, compare and synthesize scientific information; identifying relevant concepts, themes and research gaps while verifying sources.
11:30 – 12:30	From research question to analytical workflow Translate a research objective into data requirements, variables, analytical questions and an appropriate AI-supported research workflow.
12:30 – 13:00	Break
13:00 – 14:15	Data preparation and exploratory analysis with AI Structuring, cleaning and understanding datasets; descriptive analysis, visualization, pattern exploration and identification of data-quality issues.
14:15 – 15:15	Prediction and pattern discovery Introduction to classification, regression, clustering, anomaly detection and other methods that can support prediction and discovery in research.
15:15 – 16:00	Responsible AI for research Research integrity, reproducibility, transparency, privacy, bias, hallucinations, source verification and the role of human validation.
DAY 2 – PRACTICAL AI-ASSISTED RESEARCH WORKFLOW	Analyze, predict and discover insights from data
10:00 – 10:45	Defining the practical research case Select a research question, define the expected output and understand the structure and limitations of the dataset used during the practical session.
10:45 – 12:15	Guided exercise: preparing and exploring data Import and inspect data, perform basic preparation, generate descriptive insights and use AI-supported techniques to explore relationships and patterns.
12:15 – 12:45	Break
12:45 – 14:00	Guided exercise: prediction or discovery Apply an accessible Machine Learning or AI-supported method for prediction, classification, clustering or pattern discovery and examine the generated results.
14:00 – 15:00	Interpreting and visualizing research results Evaluate outputs, create meaningful visualizations, identify limitations and translate analytical results into evidence that can support research conclusions.
15:00 – 16:00	From AI output to research insight Document the workflow, validate AI-assisted findings, discuss reproducibility and responsible reporting, and conclude with practical use cases and Q&A.

Course Description

Day 1 introduces AI-assisted research methods across the research lifecycle. Participants will explore how AI can support literature and information discovery, research-question refinement, data preparation, exploratory analysis, pattern identification and predictive approaches. Particular attention is given to choosing suitable methods and understanding the limits of AI-generated outputs.

Day 2 is practice-oriented. Participants will follow a guided research workflow using accessible AI and data-analysis tools: defining a research question, working with a dataset, exploring and visualizing the data, applying a simple predictive or discovery method, and interpreting the results. The course also addresses reproducibility, transparency, privacy, bias, research integrity and responsible use of AI in scientific and applied research.