Data preparation
Missingness patterns, outliers, coding, leakage, transformations and feature quality are assessed and documented before modelling.
RESEARCH & INDUSTRIAL ANALYTICS
Turn raw data into defensible evidence.
We audit and prepare data, select assumption-aware analytical methods, validate results and translate technical outputs into transparent findings for research, engineering and business decisions.
Bachelor ProjectsMaster’sMPhil / PhDIndustry & R&D
Analysis designed around the question.
Methods are chosen for the data structure, assumptions, sample size and research objective—not simply because a software package offers them.
Statistical inference
Assumption checks, effect sizes, confidence intervals, regression, group comparisons, reliability and hypothesis tests are reported clearly.
Predictive analytics
Machine-learning and time-series models use suitable baselines, cross-validation, imbalance handling, calibration and task-specific metrics.
Dashboards & insight
Tables, figures and dashboards communicate trends, uncertainty, limitations and actionable findings without overstating the evidence.
A transparent analytical workflow.
Every transformation, assumption and modelling decision is documented so the results can be independently reviewed and reproduced.
Define the objective
Translate the question into variables, outcomes, hypotheses and decision metrics.
Audit the data
Check structure, missingness, imbalance, bias, assumptions and confidentiality needs.
Select methods
Choose justified tests, models, validation strategy and meaningful baselines.
Analyze & validate
Run the analysis, test sensitivity and robustness, and quantify uncertainty or performance.
Report the insight
Prepare auditable visuals, interpretation, limitations and reproducibility notes.
Degrees & domains
- Bachelor, Master’s, MPhil and PhD research
- Management, marketing and social sciences
- AI, computing and engineering datasets
- Energy, renewable systems and smart grids
- Applied biomedical and operational analytics
Analysis tools
The stack is selected for the method, scale and required output.
PythonRSPSSSmartPLSMATLABExcelTableauPower BIJupyterGoogle Colab
Analysis outputs you can explain.
Deliverables are tailored to a thesis, journal paper, presentation, technical report or evidence-based management decision.
Prepared datasetClean variables, data dictionary, coding notes and quality summary.
Analysis codeOrganized scripts or syntax for reproducibility.
Test & model outputsAssumptions, effect sizes, metrics, comparisons and diagnostics.
Figures & dashboardsReadable visuals matched to the audience.
Interpretation reportEvidence-led findings, limitations and implications.
Reproducibility notesData steps, software settings and execution guidance.
Responsible analysis: results are reported as obtained, including uncertainty and limitations. We do not alter data to force significance or support a preferred conclusion.
Have data but need a clear plan?
Share the research question, file format, variables and expected output for an initial review.