Learn to identify different types of data (categorical, numerical, textual), understand sampling strategies, and distinguish between observational studies and experiments.
Understanding and Visualising Data
This is a Level 1 course from the Economics, Politics and International Relations, Management and Innovation, and Data Science and Artificial Intelligence majors, part of the Open Bachelor’s programme. It is worth 6 ECTS and takes place in Term 1 in Lisbon.
Course Summary
Data here, data there, data everywhere… Let us make sense of it! In today’s business landscape, mastering data is no longer optional — it is essential! This course invites you to dive into the world of descriptive statistics, where you will learn how to analyze, summarize, and communicate data effectively. Through hands-on projects, you will apply statistical tools to real-world business challenges. By the end, you will not only understand the numbers, but also tell their story. Whether you are presenting to stakeholders or making strategic decisions, this course will give you the confidence and skills to succeed in a data-driven world. No prior experience needed, just bring your curiosity!
Course Learning Outcomes (CLOs)
| Description | |
|---|---|
| CLO 1 | Acquire and apply basic statistical tools to describe, summarise, and interpret quantitative and qualitative data, and rationalise their appropriate use. |
| CLO 2 | Effectively and accessibly communicate the findings from data analysis in collaboration with peers. |
| CLO 3 | Use digital tools (e.g. spreadsheets, data visualisation software, and AI) to create accurate and effective visual communications of data analysis to a non-specialist audience. |
Assessment
| Assessment Type | Weighting of Course Grade | Group Assessment? | Invigilated? | CLOs Mapped | |
|---|---|---|---|---|---|
| Assessment 1 | Evaluative – Quiz | 20% | No | Yes | CLO 1 |
| Assessment 2 | Digital – Data Project | 40% | Yes | No | CLOs 1, 2, 3 |
| Assessment 3 | Practical – Case Study | 40% | No | Yes | CLOs 1 & 3 |
- Assessment 1 Description: A multiple-choice quiz testing understanding of core statistical concepts and vocabulary, administered around the midpoint of the course. Questions cover basic descriptive statistics, data types, and graphical representation techniques. Designed to support early diagnostic feedback and reinforce foundational knowledge.
- Assessment 2 Description: In small groups (3–4), students conduct a short data investigation based on a real-world question in economics or management. Projects should use publicly available data (e.g. Eurostat, World Bank) and apply statistical methods introduced in class. This is a course-long project, intended to evolve alongside their learning, encouraging regular engagement. Students produce either a written report (e.g.: 5 page max, times new roman, 12pt, 1.5 spacing, normal margins, excluding figures and references) or a video (e.g.: 15-20 min) explaining their findings, supported by a basic literature review and clear visualisation.
- Assessment 3 Description: A 30-minute one-on-one conversation between the student and the course Fellow, where the student responds to an unseen data scenario under exam conditions. Students must showcase their knowledge (e.g. demonstrate their ability to compute basic statistics, interpret them in context, and sketch relevant graphs or tables on the board). No digital tools are allowed. The emphasis is on clarity of reasoning, real-time analysis, and the communication of quantitative insights to a non-specialist audience. This assessment takes place towards the end of the course.
Indicative List
of Topics
Use R and AI tools to create histograms, box plots, bar charts, and cross-tabulations to explore trends, patterns, and group differences in both quantitative and qualitative data.
Develop an intuitive understanding of distributions, outliers, and variability using concepts like the mean, median, standard deviation, and percentiles.
Use confidence intervals and simple hypothesis tests (e.g. for proportions or means) to support claims with evidence.
Learn how to identify and interpret associations using cross-tabulations, correlation, simple linear regression, and introductory multiple and logistic regression, with practical examples from politics, economics, and business.
