- Examine how and why data science is applied to money.
- Interpret the benefits of algorithmic and human-in-the-loop trading.
- Evaluate how automated application reviews for loans and credit can change.
- Justify how social media can be beneficial to economics.
- Analyze the relationship between cryptocurrencies and data science.
- Interpret the ethical and technical challenges and possibilities of data science.
Skill Level Beginner
- [Poulson] Money is an idea. The vast majority of our financial transactions these days involve no physical exchanges but rather the exchange of data that signifies wealth. And so, to understand money, you need to understand data. I'm Barton Poulson, and in this brief course we'll take a non-technical, conceptually oriented look at how data science can be effectively used to understand money in the fields of economics, banking, and finance.
We'll explore the ways that principles and practices of data science can help people when trading securities, giving and receiving credit, and averting fraud. We'll see how social media and recent developments in Cryptocurrencies connect to data science. And we'll explore important procedures in trend analysis, in causal inference, and in ethics as they apply to data science. We'll mention some of the important procedures that are used in data science, but more significantly, we'll explore how those ideas are used in the financial sectors to find, create, and support value.
Because this is a non technical overview, anyone with an interest in the business of money, regardless of their technical background can get important ideas and insights from this course. And so, let's get started with The Data Science of Economics, Banking, and Finance.
Q: This course was updated on 05/09/2018. What changed?
A: A: We added one video on data science careers in economics, banking, and finance.