• Title: Data Science for Economics and Finance: Methodologies and Applications
• Author(s) Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana
• Publisher: Springer; 1st ed. 2021 edition (June 10, 2021); eBook (Creative Commons Licensed)
• License(s): CC BY-NC-SA 4.0
• Hardcover/Paperback: 372 pages
• eBook: PDF and ePub
• Language: English
• ISBN-10/ASIN: 3030668932
• ISBN-13: 978-3030668938
• Share This:  

Book Description

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models.

• Sergio Consoli is a Scientific Project Officer at the European Commission, Joint Research Centre, Italy, working on the project "Big Data and Forecasting of Economic Developments" aiming at exploring novel big data sources and methodologies to provide better economic forecasting.

• Amazon

• Data Science and Data Engineering
• Financial Mathematics, Financial Engineering, etc.
• Data Analysis and Data Mining, Big Data
• Statistics, Mathematical Statistics, and SAS Programming

• Data Science for Economics and Finance: Methodologies and Applications
• The Mirror Site (1) - PDF

• Data Science for Wind Energy (Yu Ding)

  This book provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, etc.

• Financial Machine Learning (Bryan T. Kelly, et al.)

  This book is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.

• R for Data Science: Visualize, Model, Transform, Tidy, Import

  This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

• Julia Data Science (Jose Storopoli, et al.)

  An accessible, intuitive, and highly efficient makes Julia a formidable language for data science. This book will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work.

• The Data Science Workshop, 2nd Edition (Anthony So, et al.)

  Learn how you can build machine learning models and create your own real-world data science projects. By learning to convert raw data into game-changing insights, you'll open new career paths and opportunities.

• Introduction to Data Science (Rafael A. Irizarry)

  Introduces concepts and skills that can help tackling real-world data analysis challenges. Covers concepts from probability, statistical inference, linear regression, and machine learning. Helps developing skills such as R programming, data wrangling, etc.

• Introduction to Data Science (Jeffrey Stanton)

  This book provides non-technical readers with a gentle introduction to essential concepts and activities of data science. For more technical readers, the book provides explanations and code for a range of interesting applications using the open source R language.

• Introduction to Probability for Data Science (Stanley Chan)

  This book is an introductory textbook in undergraduate probability in the context of data science to emphasize the inseparability between data (computing) and probability (theory) in our time, with examples in both MATLAB and Python.

• Python Data Science Handbook: Essential Tools (Jake VanderPlas)

  Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

• Data Science at the Command Line, 2nd Ed. (Jeroen Janssens)

  This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. Learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.

• Computational and Inferential: The Foundations of Data Science

  Step by step, you'll learn how to leverage algorithmic thinking and the power of code, gain intuition about the power and limitations of current machine learning methods, and effectively apply them to real business problems.

• Data Science: Theories, Models, Algorithms, and Analytics

  It provides a bucket full of information regarding Data Science, covers a wide variety of sections by giving access to theories, data science algorithms, tools and analytics. You'll explore the right approach to best practices to guide you along the way.

• Regression Models for Data Science in R (Brian Caffo)

  The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.

• The Ultimate Guide to Effective Data Cleaning

  With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Experts share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.

• Elements of Data Science (Allen B. Downey)

  This book is an introduction to data science for people with no programming experience. The goal is to present a small, powerful subset of Python that allows you to do real work in data science as quickly as possible.

• Statistical Inference: Algorithms, Evidence, and Data Science

  A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century. Every aspiring data scientist should carefully study this book, use it as a reference.

• Exploring Data Science (Nina Zumel, et al)

  This book introduces readers to various areas in data science and explains which methodologies work best for each, with practical examples in R, Python, and other languages.