Tools by Subject
Sciences & ESS
Statistical & Lab Tools
Excel/Google Sheets (descriptive stats), R or Python (advanced stats), SPSS, Logger Pro (for lab data), GeoGebra (for maths), Vernier Graphical Analysis.
Economics
Economic Data Sources
World Bank Open Data, IMF Data, OECD.stat, Trading Economics, Our World in Data. For econometric analysis: STATA, EViews, or Excel regression.
Psychology
Behavioural Research Tools
PsychoPy (experiments), SPSS or JASP (statistical analysis), Survey tools (Google Forms, Qualtrics), ethical consent templates.
History & Politics
Qualitative Analysis
NVIVO (for coded qualitative analysis), timeline tools (Aeon Timeline, TimelineJS), historical GIS tools, document annotation tools.
Mathematics
Mathematical Software
Desmos, GeoGebra, Wolfram Alpha, MATLAB, Python (numpy/scipy/matplotlib). For statistics EEs: R is the industry standard.
Computer Science
Programming & Data
Python, Java, GitHub, Jupyter Notebooks, Tableau (data visualisation), Kaggle (datasets), UCI Machine Learning Repository.
How to Present Data in Your EE
1
Choose the right graph type
Line graphs for trends over time. Bar charts for comparisons. Scatter plots for correlations. Histograms for distributions. Pie charts almost never — they are rarely informative.
2
Label everything clearly
Every figure needs: a title, labelled axes with units, a source citation, and a figure number (Figure 1, Figure 2…).
3
Analyse, do not just display
Every graph or table must be followed by analysis: what does this data show? What does it mean for your RQ? What are the limitations?
4
Acknowledge uncertainty
For quantitative sciences: include error bars, state confidence intervals, and discuss the statistical significance of your results.
💡 Expert Tip
If you are using human participants in your research (surveys, interviews, experiments), you MUST get ethical approval from your school before collecting any data. Check with your supervisor first.
Need help choosing the right analysis method for your EE?