EN47 minTutorial
Reliable AI Data Analysis: A PhD Statistician's Practical Guide
PhD statistician Dr. Lyndon Walker shows how to use SciSpace's AI Data Analyst while keeping statistical results accurate and honest.
AI for Research Data Analysis
Taught by Dr. Lyndon Walker, Dr. Loreta Tauginienė +1 expert
3experts9tutorials1language
Analysing research data is where small errors become published errors.
- Clean data and choose the right statistical test
- Code qualitative data ethically, with privacy in mind
- Check and reproduce every AI-assisted result
Try this on SciSpace
Data analysis tutorials from expert researchers
Showing 9 tutorials
- EN47 min
EN50 minTutorialResearch Data Analysis with SciSpace AI: From Data to Insight
See how SciSpace AI can support exploring, visualising and reporting research data while you keep rigor and interpretation in your hands.
EN35 minTutorialEthical AI for Qualitative Data: Transcription and Anonymisation
Dr. Loreta Tauginienė examines the ethical, legal and methodological risks of AI transcription and anonymisation in qualitative research.
EN38 minTutorialFinding, Analysing and Reporting Open Research Data with AI
Follow a SciSpace workflow for finding open datasets, running multi-step statistical analyses and reporting results within data governance rules.
EN37 minTutorialEsports Research Methodology: Study Design and AI-Assisted Protocols
Dr. Joanne Donoghue explains how to define esports populations precisely and use AI workflows to move from research idea to protocol and journal.
- ENTutorial
Visualising Research with SciSpace Agent: Figures and Diagrams
Learn to turn papers, notes or early ideas into clear figures, diagrams and graphical-abstract-style summaries with SciSpace Agent.
EN42 minTutorialAI for Research Data Analysis: Quantitative and Qualitative Workflows
See how an AI agent in SciSpace supports data extraction, statistics, qualitative coding and charts, and when specialised tools are better.
- EN42 minTutorial
How to Analyze Research Data with AI: A Reproducible Workflow
In SciSpace, AI can accelerate data inspection, code generation, visualization, and reporting, but the research question and study design should determine the method. Begin with a data dictionary and a written analysis plan, inspect quality problems before modeling, record every transformation, test assumptions, and verify results against the data and code before drawing conclusions.
- ENTutorial
AI Data Analysis for Researchers: A Tutorial Series
Use the SciSpace data-analysis tutorial series to move from a research question and data dictionary through quality checks, preprocessing, analysis, visualization, and interpretation. Decide the method before generating code, document every change, test assumptions, and retain a reproducible record that another researcher can inspect.
Learning path
Research Data Analysis with AI in 5 LessonsAnalyse data reliably with AI support: core workflows, a tutorial series, an expert analysis workflow, ethical qualitative analysis and working with open research data.About this use case
Analysing research data is where small errors become published errors. AI can help you clean data, choose and run appropriate tests, code qualitative material and visualise results, but every output still needs a researcher who understands the method and checks the numbers.
This hub covers SciSpace Academy's data analysis tutorials and tutorials, including a PhD statistician's guide to reliable AI-assisted analysis, qualitative research data processing, and finding, analysing and communicating open research data. Each tutorial focuses on a workflow you can reproduce and on the checks that keep your analysis defensible.
Frequently asked questions
Can I trust statistics produced by AI?
Treat AI-generated analysis like work from a new research assistant: useful, but always checked. Confirm that the test suits your data, re-run key results, inspect assumptions and keep the code or steps so the analysis is reproducible.
Can AI help with qualitative data analysis?
AI can suggest initial codes, group excerpts and summarise themes, which saves time on large datasets. The researcher should still define the coding framework, review assignments and interpret meaning, and should consider participant privacy before uploading any data.