Instructional goals
The course aims to equip students with the ability to collect, process, and interpret data deemed useful for forming independent judgments on the economic, political, and social aspects that describe the actual functioning of a country, as well as the ability to communicate information, ideas, problems, and solutions to both specialists and non-specialists, in accordance with the Dublin Descriptors 3 (autonomy of judgment) and 4 (communication skills).
Furthermore, the course aims to develop the ability to transform a current economic, managerial, or financial issue into an empirical question, identifying the most appropriate data, constructing indicators, applying statistical and economic analysis techniques, and discussing the implications of the results for policy, management, and corporate finance decisions.
The methodological treatment of the topics is carried out through empirical analyses of real-world data at the national and international levels, drawn from databases (e.g., EUROSTAT, Moody’s, etc.), to conduct spatial (benchmarking) and temporal (time series) analyses. In addition to official statistical sources, the course also utilizes microeconomic and firm-level data, such as business surveys and corporate financial statements, to apply data analysis to real-world problems in macroeconomics, management, and corporate finance.
The teaching method is interactive and involves students using computers to work with Excel and other statistical/econometric software. The tools acquired are applied to empirical cases through a workflow that includes: selecting a current economic or business event, defining a research question, identifying and downloading data, constructing indicators, descriptive analysis, data visualization, basic statistical and econometric analysis, interpreting results, and discussing the related policy or business implications.
The course also introduces students to data analysis workflows, including data acquisition techniques from digital sources, open data, and, where appropriate, web scraping, with a focus on data quality, cleaning, and integration. Particular emphasis is placed on transforming empirical results into communicable outputs through data visualization tools (interactive charts, maps, infographics) on platforms such as Flourish.
The course aims to equip students with the advanced Excel skills and digital competencies required by the European DIGCOMP 2.0 standard, with specific reference to Area 1—Information and Digital Literacy—and Area 3—Digital Content Creation. With regard to Area 3, the workshop focuses primarily on the content of the ECDL Advanced Spreadsheets and Advanced Database modules. Students also take an advanced Excel test modeled on the AICA “Advanced Spreadsheets” syllabus, upon completion of which they receive a badge. Building on these practical skills, the course introduces more advanced economic and statistical analysis applications, such as correlation analysis, scatter plots, OLS and probit regression models, and moderation analysis exercises, including the use of STATA for certain econometric applications.
Prerequisites
Basic statistical knowledge; basic knowledge of Excel, the contents of which will be covered in the first few lessons of the course.
Intended learning outcomes
Knowledge and Understanding:
knowledge of Advanced Excel: knowledge of data types (hard and soft) and related analysis techniques through the use of spreadsheets for logical, statistical, financial, and mathematical operations; creation and formatting of charts; pivot tables; filtering and sorting data; designing macros for repetitive operations.
Knowledge and understanding:
knowledge of (official) data sources; data types and related analysis techniques (trend, cycle, and seasonal decomposition, graphical representations, dispersion) and linear regression models for forecasting hard data using soft data. Knowledge of sources and datasets useful for applied economic-statistical analysis, including international macroeconomic data, firm-level survey data, and corporate financial statement data. Knowledge of the main methods for acquiring and organizing data from digital sources, including open data portals, online databases, APIs, and simple web scraping procedures, with attention to source traceability, data quality, and the methodological limitations of the various information sources.
Ability to apply knowledge and understanding:
ability to select appropriate measures for summarizing data and analyzing the relationship between variables in economic, financial, and corporate contexts. Ability to apply descriptive and econometric analysis techniques, as well as data visualization, to real-world problems in macroeconomics, management, and corporate finance. Ability to create static and interactive visualizations designed to compare countries, companies, sectors, and time periods, using tools such as Excel and visual storytelling platforms like Flourish
Independence of judgment:
ability to collect, process, and critically interpret quantitative and qualitative data related to economic, financial, and business phenomena. Ability to transform a current issue into an empirical question, assess the adequacy of available data, and critically analyze and interpret results while taking into account the limitations of the analysis.
Communication skills:
ability to effectively communicate data analyses. Ability to discuss empirical results in terms of policy implications, managerial decisions, and corporate finance choices, adapting language and level of detail to the target audience.
Ability to present data effectively for different types of users.
Proficiency in the tools available for presenting results (tables, static and dynamic charts, infographics, etc.)
Learning skills: ability to independently learn data analysis techniques in professional activities or further studies. Ability to independently use datasets, digital tools, and statistical-econometric software to explore applied problems in the fields of economics, management, and finance.
Course Contents
Practical lessons.
The course addresses current economic policy issues through the analysis of real-world data using applied statistical techniques, including Excel functions and functions from other software packages (e.g., STATA)
The teaching method is interactive, as it involves the simultaneous use of computers by both the instructor and students to work with Excel and other statistical-econometric software. The first part of the course is dedicated to developing practical skills in Excel Advanced, with a particular focus on data management, the use of formulas and functions, the creation of pivot tables, graphical representation, data import, validation, worksheet protection, and the introduction of automation tools.
The second part of the course applies these skills to empirical cases based on real data, following a problem-driven approach.
Each module begins with a current economic topic related to the fields of Economics, Management, and Finance, and guides students in defining a research question, identifying and downloading data, constructing indicators, conducting descriptive and statistical-econometric analysis, visualizing results, and discussing the related policy or business implications. The selected topics allow for linking data analysis to various fields within Economics, Management, and Finance. Given the course’s highly applied nature, classes include guided exercises conducted jointly by the instructor and students using real-world datasets. The instructor provides progressive and personalized support, guiding students through the operations and functions covered until they achieve an adequate level of operational autonomy.
Consistently with the Grand Challenge orientation of the course, students have the opportunity to carry out a group project focused on applying economic data analysis to a major current challenge using real-world data. The project may benefit from interaction with representatives of economic and financial institutions, who will help frame the relevant macro-themes, data needs and policy questions.
Reference Books
Course materials and handouts distributed during lectures
Teaching Methods
Practical lessons.
The teaching method is interactive, as it involves the simultaneous use of computers by both the instructor and students to work with Excel and other statistical-econometric software.
Given the course’s highly applied nature, classes include guided exercises conducted jointly by the instructor and students using real-world datasets. The instructor provides progressive and personalized support, guiding students through the operations and functions covered until they achieve an adequate level of operational autonomy.
Consistently with the Grand Challenge orientation of the course, students have the opportunity to carry out a group project focused on applying economic data analysis to a major current challenge using real-world data. The project may benefit from interaction with representatives of economic and financial institutions, who will help frame the relevant macro-themes, data needs and policy questions.
Assessment Method
In-class test on the full Excel Advanced programme covered in Weeks 1–6. Successful completion awards the Luiss Excel Advanced badge and constitutes a prerequisite for sitting the final exam of the course.
The group project presentations are designed to assess students’ ability to apply the data analysis workflow developed during the course, from the formulation of an empirical question to data collection, analysis, visualization and interpretation of results. Particular attention is devoted to the critical discussion of the results and to their connection with the relevant policy implications. The discussion also aims to evaluate students’ independence of judgment and communication skills in applying data analysis to real-world economic issues.
Thesis assignment criteria
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Week 1
Week 1 (Session 1): Introduction to the Course and Microsoft Excel
Introduction to the course and Microsoft Excel. Worksheet and workbook structure. Cells and basic arithmetic formulas. Introductory concepts in quantitative data analysis. Applied exercises on data entry and calculation of means.
Week 1 (Session 2): Basic Excel Functions and Conditional Formatting
Basic Excel functions. Relative and absolute references. Structuring data in tables. Introduction to table formatting and conditional formatting for descriptive data analysis. Applied exercises on basic functions, table formatting, and the use of conditional formatting to highlight patterns in data.
Week 2
Week 2 (Session 1): Logical Functions, Conditional Aggregation and Filters
Logical and conditional functions (IF). Conditional counting and sum functions (COUNTIF, SUMIF). Simple, custom, and advanced filters for selective data analysis. Applied exercises on structured datasets.
Week 2 (Session 2): Advanced Conditional Formatting, Date Functions and Custom Formats
Advanced conditional formatting. Date and time functions. Rounding and numerical precision management. Custom cell formats. Applied exercises on time series and formatted data.
Week 3
Week 3 (Session 1): 3D References, Statistical and Text Functions, Introduction to AI Tools
3D references across worksheets. Introduction to descriptive statistical functions. Text functions for data cleaning, transformation, and standardization. Advanced logical functions (AND, OR, XOR, NOT, IFS). Introduction to Large Language Models (LLM): how they work, contextual ambiguity, and the role of training data. Basics of prompt engineering: structuring effective prompts to interact with AI tools. Applied exercises using AI assistants to support Excel-based data analysis tasks.
Week 3 (Session 2): Applied Exercises – Statistical Functions, Text Functions and Logical Operators
Hands-on exercises consolidating the topics introduced in Session 1. Practice on 3D references across worksheets. Applied exercises on descriptive statistical functions (COUNTBLANK, COUNT, COUNTA, RANK). Text function exercises on real datasets: extracting, combining and standardizing string data (LEFT, RIGHT, MID, SEARCH, CONCATENATE). Applied exercises on advanced logical functions (AND, OR, XOR, NOT, IFS) combined with IF. Practical use of AI assistants to support formula writing and data analysis tasks.
Week 4
Week 4 (Session 1): Pivot Tables (Theory) – Structure, Layout and Aggregation
Pivot tables: concept, structure, and operating logic. Creating pivot tables from structured datasets. Configuring rows, columns, values, and filters. Aggregation functions (SUM, COUNT, AVERAGE, MAX, MIN). Sorting, grouping, and conditional formatting within pivot tables. "Show values as" options: percentages, rankings, and cumulative totals. Recommended pivot tables and dynamic data ranges. Use of pivot tables for exploratory data analysis and synthesis of information.
Week 4 (Session 2): Pivot Tables (Practice) – Applied Exercises
Guided hands-on exercises on pivot table construction, configuration, and interpretation using real datasets. Comparison of pivot-based analysis against SUMIF/COUNTIF formulas. Extended practice on grouping, sorting, conditional formatting within pivot tables, and “Show values as” options (percentages, rankings, cumulative totals). Students complete a take-home mid-term self-assessment covering all topics addressed in Weeks 1–4.
Week 5
Week 5 (Session 1): Charts, Sparklines and Lookup/Reference Functions
Charts: main types (column, bar, line, pie), selection criteria, customization, and formatting. Combined charts with secondary axes and unit scaling. Sparklines for inline data visualization. Lookup and reference functions: from VLOOKUP and HLOOKUP to the modern XLOOKUP function – syntax, advantages, and applied exercises on data querying and cross-referencing.
Week 5 (Session 2): Subtotals, Data Validation, Scenario Analysis, Financial Functions and Data Import
Subtotals and automatic summaries with expandable/collapsible detail levels. Data validation: input restrictions, dropdown lists, and custom error messages. Scenario analysis and what-if tools: Scenario Manager for comparing multiple input sets. Introduction to financial functions: FV (future value), PV (present value), PMT (payment). Importing data from external sources: text-to-columns and CSV import via Power Query. Applied exercises on economic and financial simulations.
Week 6
Week 6 (Session 1): Formula Auditing, Named Ranges, Database Functions, Workbook Protection and Macros
Tracing precedents and dependents; showing all formulas in the worksheet; error checking tools. Comments and annotations for collaborative workbooks. Named ranges: creation, management, and use in formulas and charts. Database functions (DSUM, DAVERAGE, DCOUNT, DMAX, DMIN) for structured, criteria-based data analysis. Hyperlinks and external references: linking cells across worksheets, workbooks, and external files. Worksheet and workbook protection: locking/unlocking cells, password protection, and workbook structure protection. Introduction to macros: recording, storing, and assigning macros to buttons; automating repetitive formatting and data-cleaning operations.
Week 6 (Session 2): Final Test
In-class test on the full Excel Advanced programme covered in Weeks 1–6. Successful completion awards the Luiss Excel Advanced badge and constitutes a prerequisite for sitting the final exam of the course.
Week 7
Week 7 (Session 1): GDP growth and aggregate demand: descriptive analysis with Eurostat data The session applies economic data analysis techniques to real Eurostat national accounts data. Starting from a current macroeconomic issue, students define a research question on GDP growth and aggregate demand components. They identify and download data on GDP, private consumption, public expenditure, investment, exports and imports; construct indicators; compute growth rates, shares of GDP and contributions to growth; and produce tables and charts to compare countries and trends over time.
Week 7 (Session 2): GDP growth and aggregate demand: correlation, regression and policy interpretation
Building on the dataset developed in Session 1, students apply correlation analysis, scatter plots and linear regression to examine the relationship between GDP growth and the main components of aggregate demand. The analysis may also consider selected macroeconomic challenges — such as inflation, interest rates, economic shocks, trade uncertainty and European funding programmes, including NGEU — and discuss their policy implications for European economies.
Week 8
Week 8 (Session 1): Management, innovation and digital transformation: firm-level analysis.
The session applies firm-level data analysis techniques to exploit data on enterprises. Starting from the policy debate on digital transformation, deep technologies, technological security and European competitiveness, students define a research question on the role of management in shaping firms’ technological innovation capacity. They use Excel functions — including AVERAGE, IF, AND and OR — to construct indicators of management practices, managerial training and technological innovation. Descriptive statistics, scatter plots and pivot tables are used to compare groups of firms, with particular attention to small enterprises and family-managed businesses.
Week 8 (Session 2): Management, innovation and digital transformation: Econometric and policy analysis.
Building on the dataset developed in Session 1, students apply OLS, probit models and moderation analysis, also using STATA, to examine the relationship between management capabilities and innovation outcomes. Results are discussed in relation to EU priorities on SME digitalisation, upskilling and reskilling, highlighting managerial skills as a lever for technology adoption, productivity and upgrading.
Week 9
Week 9 (Session 1): Corporate Finance and financial resilience: Descriptive analysis of European firms’ balance sheet data
The session applies corporate finance data analysis techniques to real balance sheet data on European firms from Moody’s. Starting from the debate on firms’ financial resilience, equity financing and stock market listing in Europe, students define a research question on the relationship between capital structure, equity base, listing status and corporate performance. They construct standard corporate finance indicators, including equity ratio, leverage, debt-to-equity ratio, liquidity ratio, EBITDA margin and ROA, and use descriptive statistics, data visualization tools and pivot tables to compare firms across countries, sectors, size classes and listed versus non-listed companies.
Week 9 (Session 2): Corporate Finance and financial resilience: Regression and policy interpretation
Building on the dataset developed in Session 1, students apply correlation analysis, scatter plots and linear regression, also using STATA, to examine whether firms with stronger equity bases and listed companies are more resilient and perform better. Results are linked to debates on corporate vulnerability, equity financing and stock markets, connecting firm-level financial structure with the EU agenda on Capital Markets Union and Savings and Investments Union and highlighting the role of integrated capital markets in diversifying firms’ financing sources beyond bank credit.
Flexibility note
The specific issue refereed to weeks 7-9 may be further refined according to the most relevant current economic and policy developments at the time of the course, in order to ensure a close connection between data analysis, real-world events and policy debate.
Week 10
Week 10 (Session 1): Statistical sources, data downloading and data cleaning
The session provides an overview of the main statistical sources used for applied economic analysis, including ISTAT, Eurostat, OECD, IMF, World Bank, UNCTAD and other relevant databases. Students review how to access and manage official databases, download data in suitable formats, organize datasets for analysis, and carry out basic data cleaning procedures. Particular attention is devoted to source reliability, comparability across countries and time periods, metadata interpretation and the preparation of datasets for empirical analysis.
Week 10 (Session 2): Specific review: Advanced economic-statistical analysis methods
The session reviews the main advanced methods of economic and statistical analysis introduced during the course, with particular reference to correlation analysis, scatter plots, linear regression, OLS models and probit models. Students consolidate how to select the appropriate empirical tool according to the research question, the type of variables and the structure of the available dataset.
Week 11
Week 11 (Session 1): Grand challenges in the economy: institutional perspectives and policy priorities
The session is delivered by a representative of an economic or financial institution and is devoted to presenting major current challenges for the economy from different institutional and policy perspectives. Students are exposed to how institutions identify, frame and prioritize grand challenges, such as growth, productivity, competitiveness, innovation, financial resilience, sustainability, demographic change, international trade and geopolitical uncertainty. The session helps students connect data analysis with real-world policy agendas and provides guidance for framing their group projects, identifying relevant research questions, data needs and possible policy implications.
Week 11 (Session 2): General course review and group project support
The session provides a general review of the main contents of the course, consolidating the key tools and concepts developed across the Excel Advanced module and the applied data analysis sessions. Particular attention is devoted to the workflow of applied economic analysis: defining a research question, identifying and organizing data, constructing indicators, applying descriptive and econometric methods, visualizing results and interpreting findings. The session also supports students in the development of their group projects, with guidance on research design, data sources, empirical strategy, interpretation of results and presentation of findings.
Week 12
Week 12 (Session 1): Group project presentations on major current economic challenges
The session is devoted to the presentation of students’ group projects on major current economic challenges. The presentations are designed to assess students’ ability to apply the data analysis workflow developed during the course, from the formulation of an empirical question to data collection, analysis, visualization and interpretation of results. Particular attention is devoted to the critical discussion of the results and to their connection with the relevant policy implications.
Week 12 (Session 2): Group project presentations on major current economic challenges
As in session 1, the session is devoted to the presentation of students’ group projects on major current economic challenges. The presentations are designed to assess students’ ability to apply the data analysis workflow developed during the course, from the formulation of an empirical question to data collection, analysis, visualization and interpretation of results. Particular attention is devoted to the critical discussion of the results and to their connection with the relevant policy implications. Finally, the discussion also aims to evaluate students’ independence of judgment and communication skills in applying data analysis to real-world economic issues.