
Discover how data science reframes money as data and explains value in economics, banking, and finance. Explore implications for securities trading, payments, social media, and cryptocurrency.
Explore how data science blends math, statistics, and programming to analyze money and finance data—from traditional indicators to unconventional sources—boosting profitability while managing risk.
Algorithm trading uses computer programs to process data and trade securities, with automated or human-in-the-loop decisions, leveraging stock history and social signals to improve speed and profitability.
Learn how automated loan reviews use data-driven algorithms to predict default risk, compare with FICO scores, and apply models like logistic regression, trees, and neural networks while guarding against bias.
Explore how data science detects fraud in finance using real-time and hybrid approaches, from rule-based methods to machine learning, balancing speed, accuracy, and regulatory constraints.
Learn how social media informs micro and macroeconomic analysis, using data science to extract employment indicators, trends, and policy signals from unstructured, geo-located digital data.
Explore how decentralized crypto currencies, smart contracts, and cryptography influence finance, economics, and banking, with data science insights from mining, ledgers, and big data.
Clarify that correlation does not imply causation and explain practical approaches to causal inference. Highlight methods like randomized experiments, quasi experiments, instrumental variables, and causal modelling.
Explore ethics in data science for economics, banking, and finance, noting that correlation is never perfect, biases arise, and careful variable selection plus anonymized data prevent unintended consequences.
Develop practical data science skills for economics, banking, and finance by working with real data, mastering Excel, R, and Python, and understanding statistics and data science principles.
Data Science is changing virtually every aspect of our lives. In recent years, the ability of data science and machine learning to cope with a number of major financial tasks has become an particularly important point. Increasingly more companies want to know what improvements data science brings and how it can reshape their business strategies.
The amount of data available to organizations and individuals is unprecedented. Financial services sector have the most digital data stored. Companies that want to maximize use of this available data require professionals who have a keen understanding of data science and know how to use it to solve meaningful business challenges.
In this course, I'm going to provide you with proven steps and strategies on how to use data science for economics and finance. We will go over the main aspects of analyzing data correctly by using various strategies you need to implement in order to get results that are precise and beneficial. You'll discover the ways that principles and operations of data science can help people when trading securities, sending or receiving credit, and averting fraud. You will learn how to understand and apply modern machine learning to financial data. Also we'll see how social media and current developments in Cryptocurrencies connect to data science. In addition, I'm going to give you a clear understanding of the statistical algorithms needed to model and estimate the joint dynamics of the markets.
What you'll learn in this course:
· Understand the advantages of data science and specific analytical methods
· Discover how finance professionals can use data science to solve real-world problems
· Understand why data is your single most powerful tool
· The Importance of data analysis In financial sectors
· Understand Algorithmic trading
· Understand Human-in-the-loop trading
· Automated Application Reviews for Loans & Credit
· Data Science & Cryptocurrencies
· Correlation & Causality in Economic Data
· Fraud Detection