Last year I wrote a post on three computer science classes I recommended taking at UC Davis. I thought it would be a good idea for me to do the same for my major, Economics. Before I get into specifics, I want to mention that I studied Economics at UC Davis, not Managerial Economics. Managerial economics is different than regular economics in a sense that it’s more business oriented. In other words, Managerial Economics is closely related to a business degree (There is no undergraduate business program at UC Davis).
Now that I have some of the disclaimers out of the way, I can get started with the classes!
ECN 140 - Econometrics
I took ECN 140 with Professor Oscar Jorda, and I have to say, he was the reason why I became so interested in large data analysis in the first place! Econometrics is the application of statistics on economic data. This course heavily uses regressional techniques to model uncertainty.
In my opinion, a lot of the techniques taught in this class will be useful in data analytics fields. Furthermore, ECN 140 usually ends on an empirical research project in which you get to choose what to analyze on a large set of governmental data.
Some important skills you will learn in this course include:
- How to conduct empirical research in economics
- Using regression analysis tools (STATA or R)
- Statistical models
As someone transitioning into big data, I would like to note that this course gives you the basic foundation on how to work with large datasets. This is especially more true since big data deals with such volumes of information that without specific analysis methods, you would be getting more false positives than you would like.
I also want to mention two other important skills you should learn during this course:
- How should we handle missing/corrupt data?
- What methods can we use to separate false positives from positives?
The two points above will bring you one step closer to a successful career in data!
ECN 190 - Time Series Analysis and Financial Econometrics
In a way, this course is an advanced 140 course. The course will cover the following topics:
- Dynamic time-series models
- Trend modeling
- Seasonal adjustments
- Multi-factor pricing
- Volatility modeling
I recommend taking this course because it goes into detail about dynamic models and volatility. Professor Shen does an excellent job showing examples from current stock data and open datasets.
This class is quite mathematical; I believe you will have a easier time learning the proofs and inner workings of the models with a stronger mathematical background. I highly recommend being proficient with discrete math (needed for deriving time series models) and advanced statistics before taking this class.
ECN 190 - Economics & Mathematics
Economics & Mathematics is an introduction to mathematics commonly used in Mathematical Economics. This course is taught by Professor Andres Carvajal and is one of the rotating special topics courses offered in the Economics Department.
The topics covered in this course include:
- Differential and integral calculus (single and multivariate)
- Unconstrained and constrained optimization
- Comparative statics
- Applications of math to financial economics
These topics also lead to examples in consumer and firm behaviors, and market exchange.
This class is by far, one of the mathematically hardest Economics classes taught at UC Davis besides ECN 103 (Theory of Uncertainty). I also highly recommend taking this course if you plan on studying anything economics and math related at the graduate level.
By the end of the course, you will be quite proficient in deriving common mathematical models and interpreting them. Furthermore, you will work with multivariate equations and linear algebra, and learn how to apply those techniques in economic terms.
I strongly suggest you have a strong mathematical background before taking this course, especially advanced calculus.
I realize that the classes I listed are all quite mathematical. This is not just because of my background in data, but rather because math is the foundation behind economics. Economics is basically a social science - the study of how humans make choices. In order to come up with a prediction/model, Economists need to know a lot of math to devise an accurate representation.
As such, those who pursue a higher economics degree have a strong background in math. This goes the same in the job market. For example, I mentioned big data earlier in the post. A good question to ask ourselves would be: What does economics have to do with big data?
Based on my definition earlier, I can tell you several examples right from industry! For example, how do users on Spotify choose certain songs to listen to – AKA algorithms which predict a possible song you may like. This is just as much an economics problem as a math problem, and as a statistics and computer science problem.
In other words, economics cannot go without math, and the more you understand math, the more you will understand economics. Therefore, I believe the mathematical economics courses at UC Davis are definitely worth your time.
I want to end this post on a positive note, so I want to tell you all what Professor Jorda told us (the students) at the beginning of Econometrics. At the beginning of class, he asked us all one question: How many of you enjoy drinking beer? (Granted, Econometrics is an upper division course, so there were quite some students of legal age).
A lot of people raised their hands.
Then, he asked us: How many of you who raised their hands know how beer is made?
When none of us answered, he laughed and told us that we have to be more curious about learning and exploring because this is what economics is about. How can you be a good Economist if you don’t know the factors of why people enjoy something?
I’m happy to say that my time at Davis has partially answered this question for me. There is, however, still so much more for me to learn!