Data Science for Economics and Finance: Methodologies and Applications is an open-access Springer volume that shows how machine learning, language technologies, large administrative datasets and network methods are being used in economic and financial analysis. Edited by Sergio Consoli, Diego Reforgiato Recupero and Michaela Saisana, the 2021 book is most useful as a map of applied methods and case studies rather than as a single, linear course in data science.
At a glance
| Detail | Information |
|---|---|
| Title | Data Science for Economics and Finance: Methodologies and Applications |
| Editors | Sergio Consoli, Diego Reforgiato Recupero and Michaela Saisana |
| Publisher | Springer Cham / Springer Nature |
| Edition | First edition, 2021 |
| Publication dates | eBook: 9 June 2021; hardcover and softcover: 10 June 2021 |
| Length | XIV preliminary pages plus 355 pages |
| Access | Open access eBook |
| eBook ISBN | 978-3-030-66891-4 |
| Hardcover ISBN | 978-3-030-66890-7 |
| Softcover ISBN | 978-3-030-66893-8 |
Springer lists 14 chapters including front matter. The book is aimed primarily at data scientists and business analysts, with research students and other professionals working in digital economics and finance as additional audiences.
What the book covers
The editors organize the volume around a practical question: how can data-science technologies turn heterogeneous data into forecasts, classifications, indicators or risk measures that economists and financial professionals can use? The examples span advanced and deep machine learning, big-data analytics, Semantic Web technologies, natural-language processing, social-media and news analysis, time-series forecasting and nowcasting, and network analysis.
That breadth is the book’s defining feature. A reader moves from firm-level prediction and credit scoring to central-bank data, financial-news sentiment, ESG monitoring and ownership networks. The chapters are applications, so each method is discussed in the context of a substantive economic or financial problem rather than as an isolated algorithm catalogue.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute#1 Best Overall
Chapter-by-chapter map
| Chapter focus | Primary method or data | Economic or financial task |
|---|---|---|
| Supervised learning for firm dynamics | Supervised machine learning; firm data | Predicting firm outcomes and dynamics |
| Interpretability and inference | Machine-learning interpretation tools; economic forecasts | Understanding and assessing model-driven forecasts |
| Financial stability | Machine learning; financial-system data | Stability monitoring and risk analysis |
| Credit scoring | Machine-learning classification | Assessing credit risk |
| Counterparty-sector classification | Learning from EMIR data | Classifying counterparties in derivatives records |
| Macroeconomic nowcasting | Massive-data analytics; time series | Estimating current macroeconomic conditions |
| New central-bank data sources | Alternative and large-scale data | Building indicators for monetary and economic monitoring |
| Financial-news sentiment | Natural-language processing and sentiment analysis | Extracting market information from news |
| ESG monitoring | Semi-supervised text mining; company disclosures and text | Constructing signals about environmental, social and governance performance |
| Financial-entity extraction | NLP and Semantic Web-style representation | Identifying and representing entities in financial text |
| News narratives and market risk | Text analysis and narrative quantification | Predicting movements in market-risk measures |
| Extremely volatile assets | Forecasting and evaluation of new data-science tools | Testing predictive claims in highly unstable markets |
| Firm-ownership networks | Network analysis | Studying ownership structures and relationships |
The comparison shows why the volume is broader than a machine-learning textbook. Some chapters predict a target variable, while others create indicators from text, classify entities, monitor systemic conditions or represent relationships as networks.
How the methods differ in practice
Prediction and classification
Supervised learning chapters use labeled outcomes to estimate firm behavior, assign credit-risk categories or classify counterparties. These applications are close to standard predictive modeling, but their inputs and consequences are domain-specific: an EMIR record, a firm balance-sheet variable and a lending decision do not carry the same meaning or error costs.
Forecasting and nowcasting
Time-series work addresses the timing problem that matters in economics: official statistics can arrive after the conditions they describe. Massive datasets and alternative indicators can support nowcasts of the present, while financial applications attempt to forecast risk or prices in settings where relationships can change quickly.
Text, sentiment and narratives
News and other documents contain information that is difficult to use until language is converted into structured variables. Sentiment analysis measures tone; entity extraction identifies companies, instruments and organizations; semi-supervised methods can extend labeling when manually annotated examples are scarce. Narrative quantification goes further by representing recurring themes and relating them to market-risk movements.
Interpretability and inference
The interpretability chapter addresses a central limitation of high-capacity models: a forecast can be accurate without being easy to explain. Interpretation and inference tools help analysts examine which inputs drive a result and whether a model supports an economic conclusion, rather than treating predictive performance as proof of causality.
Semantic and network representations
Entity representation and Semantic Web techniques give text and linked data a machine-readable structure. Network analysis represents firms and owners as connected nodes, making relationships, concentration and indirect exposure visible in ways that a row-by-row dataset may hide.
Rank #3
Who should read it
- Data scientists and business analysts: The applications show how familiar techniques must be adapted to economic definitions, changing data-generating processes and high-stakes decisions.
- Economists and financial researchers: The volume offers examples of alternative data, text-derived indicators, nowcasting systems and network measures that can complement traditional statistics.
- Research students: The chapter range helps identify possible methods and datasets for a thesis or applied project.
- Central-bank, regulatory and risk teams: The chapters on financial stability, EMIR data, central-bank sources and market-risk narratives are directly relevant to monitoring work.
Readers seeking a gradual introduction to probability, programming or basic machine learning may need a separate foundational textbook. This book assumes interest in real applications and is best read selectively according to the problem being studied.
What readers can take away
A framework for matching data to task
The cases distinguish between data that predicts an outcome, data that supplies a real-time indicator and data that reveals relationships. That distinction prevents a common mistake: treating every large or unconventional dataset as a forecasting input.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
A realistic view of alternative data
News, social and administrative sources can arrive faster or contain concepts absent from official releases, but they also require cleaning, entity resolution, labeling and validation. The text-mining and central-bank chapters show the work needed before an apparently useful signal becomes an economic measure.
Rank #4
Evaluation beyond a single accuracy score
Economic and financial models must be judged for stability over time, interpretability, data leakage, changing regimes and the consequences of false positives and false negatives. The coverage of interpretability and extremely volatile assets is particularly useful for readers tempted by impressive but fragile demonstrations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Open-access and print editions
The eBook is open access through Springer, so readers can consult the digital edition without purchasing it. Springer also lists physical editions: the hardcover uses ISBN 978-3-030-66890-7 and the softcover uses ISBN 978-3-030-66893-8. Availability, price and delivery options vary by country and should be checked on the retailer or Springer product page at the time of purchase.
For libraries or readers who need a durable reference copy, the hardcover is the exact-title print option; the softcover provides the same edition in a lighter format. The ISBNs are the safest way to distinguish these editions from similarly titled data-science books.
Free tools Windows power users keep installed
One-click scans. No signup required.
Best Value
Publication context and current page metrics
This is a 2021 first edition, not a continuously updated manual. Techniques, software ecosystems and regulatory data practices may have changed since publication, so current projects should verify implementation details and relevant rules independently. Springer’s page display showed 37 citations and 1.34 million accesses when checked on 27 September 2026; those counters are volatile and should not be treated as permanent measures of quality or popularity.
Bottom line for prospective readers
Data Science for Economics and Finance: Methodologies and Applications is a strong reference for seeing how modern data methods are translated into economic and financial work. Its value lies in the range of concrete applications—from supervised prediction and credit scoring to nowcasting, NLP, ESG text mining and ownership networks—and in its attention to interpretation and evaluation. Because it is an edited collection rather than a step-by-step course, readers will get the most from it by choosing chapters that match their data source and decision problem.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
