Economists can become excellent data scientists and complement the skills of Data Scientists from other fields. Actually, Data Scientists and Economists have a lot in common.

These fields have strong statistical underpinnings, use modeling to address quantitative issues, and demand adept analytical abilities. In addition, data science is transforming several fields that economists work in, including banking, finance, public policy, and consulting.

A Quick Comparison between data science and economics

The primary distinction between economics and data science is how they approach their research questions: whereas data scientists are interested in prediction, economists are more interested in causation. In order to draw conclusions about a complex system from data, statistical hypotheses must be satisfied in both economic and data science modeling methodologies. You can visit the comprehensive data science course in Bangalore if you want to gain in-depth knowledge about it. 

• Assume a system can be described by a set of linear equations that look like this:

y = βX + e (where e is the error term) (where e is the error term)

Data science and economics employ distinct terms, yet they both have a vocabulary to explain the same system. Data scientists would refer to matrix X as a feature matrix. In contrast, economists would refer to it as a matrix of regressors or independent variables (also include a constant term here). Like x, y is an array that denotes the dependent variable to economists, but it would be referred to as the target variable in data science.

• This is the main distinction between the disciplines' approaches to system analysis:

Estimating the regression coefficients () is of utmost importance in economics.

The target variable y prediction is what data science is most interested in.

Consider a dataset of millions of commercial loan payments at a bank that contains detailed data on every aspect of the loans, from the application process through the history of payments. 

When talking about a topic like "What are the main factors that increase the credit risk of commercial loans?" economic procedures are particularly well suited. (Causality is the main emphasis. What is the best model to predict the credit risk of commercial loans, for example?" data science approaches would be more effective (focus is on prediction).

Both research topics are crucial, and many bank stakeholders may have questions about them. Although someone on another analytics team may be more interested in using machine learning to forecast credit risk for new applications, a financial analyst may want to find ways to rebalance a portfolio by minimizing exposure to the most important credit risk indicators.

• The main comparison of the two fields, although there are additional areas in which they diverge.

For instance, although Stata and Matlab are commonly linked with the toolkit of economics, big data frameworks and open-source programming are more commonly connected with the toolbox of data science (e.g., Spark).

The table below presents other comparison tools, such as model validation and data-gathering strategies.

Nature of scientific economics research

Early developments in the area of data science were more closely related to computer science and engineering abilities, but the emergence of more specialized career pathways has been made possible by the widespread use of low-cost data storage and cloud computing. Experts in fields that have embraced big data now have additional options to tackle business challenges scientifically thanks to sophisticated tracking of metadata and micro occurrences. This includes those who work in the social sciences, such as economics.

Although analytical rigor is crucial, it only significantly impacts when it is understandable to a less technical audience. The prominent economist Alfred Marshall increased the discipline of economics through mathematical modeling, but he also intended to make his theories (such as consumer surplus) more widely known.

• Instead of using mathematics as a tool for investigation, use it as a shorthand language.

• Till you are finished, stick to them.

• English translation.

• Then use examples from actual life to further your point.

• Throw the math away.

• Burn if you can't succeed in step 4 (3).

In a similar line, it can be challenging to "burn the math" and have the output "translate into English" in a data science endeavor. High-performance models frequently lack any real-world interpretability. Clearly, demonstrating ensemble techniques and deep learning applications in the real world is challenging.  In contrast to coding and statistics, communication skills are often overlooked in Data Science projects while being essential to their success. Upskill yourself with the best data scientist training in Bangalore, designed in partnership with IBM. 

Data science and economics work well together.

Data science is disrupting many industries, including banking, insurance, journalism, healthcare, and public policy. As they operate in highly regulated fields, accurate documentation of the modeling procedures is also necessary. As many economists work in client-facing positions in such industries, they gain extensive subject expertise and get accustomed to summarizing the results of quantitative initiatives for customers (e.g., of regulatory issues).

I think data science and economics work really well together. They share several traits, including a strong background in statistics. Each field, though, has a lot to learn from the others. 

Although having an extensive understanding of topics like finance and consumer behavior, economists are rarely exposed to important Data Science tools in their academic studies. Economists have a history of explaining mathematical models to a larger audience, but data scientists may find it challenging if the models they use are highly abstract.

Why should economists become skilled in data science?

• Programming in open source is expanding quickly. As opposed to proprietary software, R and Python libraries facilitate analytical workflows (e.g., data cleaning, scripting to automate tasks, and more flexible modeling).

• Even compared to the learning curve experienced by some software engineers, the learning curve to get started in data science is not very steep. Machine learning's statistical underpinnings are comparable to those of econometrics.

• Today's business issues make extensive use of data, such as metadata. It needs knowledge of numerous data kinds, databases, and specialized big data tools to interact with that data.

Positive indications of the economics profession embracing data science already exist. Paul Romer, who has embraced Jupyter Notebooks for their utility in repeating and sharing research, was given the 2018 Nobel Prize in Economics. The Big Mac Index data from The Economist newspaper was also made available for its first open-source project, which uses R scripts. Check out the advanced data science courses in Bangalore, to upgrade your knowledge of top-notch tools.