It is a well-known fact that the key role of an economist is optimisation of costs for the maximisation of payoffs.
Data scientists also perform a relatively similar job. They too optimise the cost functions to maximise the model fit. They both are highly interlinked job profiles. The tools that economists and data scientists use are the same. Perhaps, economists are the best suited to become data scientists, not engineers.
Apart from the overt generalisations which have been mentioned above that economists optimise the payoffs and minimise the costs, it might not be true always. But that is the general theme of economics because economics is based on resource constraints. Resources are scarce, so the use of available resources needs to be optimised.
The same happens with data scientists. Data scientists don’t have a lot of data or their clients or their problems don’t have really infinite resources. Some kind of modulations is needed be given to those resources, so that they are getting optimised appropriately. There are a plenty of data science problems in which resources are to be optimised and payoffs are to be maximised.
Generally, economists used data science as one of its tools and one of its techniques. And, data scientists are all technique. So, if you are a data scientist, without a domain expertise, you are a kind of technician. But suppose that you have a domain expertise, maybe you are a healthcare data scientist, so you are high in demand having a specific skill set. So, an economist and a domain specific data scientists have a lot in common.
Tools You must know that in economics, there is something called econometrics which essentially comprises of a lot of regression. Also, if you begin any data science course or any machine learning course, they will start teaching you regression, simple and multiple correlation, and what happens when your data has some kind of deviation from the assumptions of regression. These are the specific things which are discussed in detail in econometrics because econometrics is perhaps the most powerful tool of economics. Economics uses statistics, math, econometrics and a lot of other tools, which are analytical in nature and so does the data science industry. Therefore, economists are well suited to become a data scientist.
Are Software Engineers better Data Scientists or Product Managers or MBAs? Software engineers have an extremely difficult task at hand and they are quite good at it. But just because data science has a bit of coding in its ambit, you cannot say that software engineers are better suited to become data scientists. But yes, software engineers come in a lot of shapes and forms, so may be there is a specific kind of software engineer who really is specializing in the artificial intelligence and machine learning domain. So they are quite invaluable data scientists. But you cannot say that all software engineers are meant to be data scientists. There is a subset of software engineers which is kind of connected to data science. But economics loosely is all about analytical tools and using models to describe the ideas, the theories and the philosophies, that ultimately run the trade of goods and services. Therefore, it can be said that an economist will be a traditional data scientist and an AI/ML software engineer will be a kind of maverick and that person will really bring in cutting edge innovation in data science and both of them are needed.
So, if you still have a question that on studying economics can make you a data analyst or a data scientist. The answer is yes, but the only point over here is that if you are beginning your economics education, don’t think it through like this will be a stepping stone to a data science career. If you want to study data science, these days there are specialised courses in data science. Now a days, there are a lot of practitioners in data science not really trained in data science. They have just become one. But in future, data scientists are going to be trained. So, if you really believe that this is what you want to do in your life, you can find a plenty of resources for that. But if you believe that you want to become an economist, then do so and study economics because economics also is a pretty interesting field of study and it has a lot of value. The predictions which economists make is just one part of the value pyramid and it has a very high degree of explanatory power and interpretation which an artificial neutral networks or the ML models don’t have. They are a kind of black box models. This is the main reason why economists bring a lot of value to data science.
So don’t be scared of switching from economics to data science, because they are really close siblings.