Transitioning from an economics background to a data science

How Graph Attention Networks are Revolutionizing Data Science

In the ever-evolving landscape of the global economy, the roles and skill sets demanded by industries undergo constant transformation. One significant shift in recent years has been the transition from traditional economists to the emerging field of data science. Data scientists use data to solve problems in different fields. They need to think critically, test ideas, and communicate their findings. The early development of Data Science as a field was more closely aligned with computer science and engineering skills, but the proliferation of cheap data storage and cloud computing has enabled more specialist paths to emerge. Economists have all these skills. To become a data scientist one needs to learn programming. R and Phyton are the two major programming languages, popular amongst data scientists. Transitioning from an economics background to a data sciences career is feasible with a strategic approach. 

Let’s first understand the key responsibility of both : 


  • Study economic markets and predict trends in economic growth, declines, or stagnation
  • Collaborate with financial experts to make predictions and create economic policies
  • Study the effects of politics on economic policies
  • Advise politicians, businesses, or individuals on economic policies or best practices
  • Teach the fundamentals of economics to students, groups, or businesses
  • Study the effects of supply and demand on good prices and macroeconomics
  • Study microeconomics in business
  • Provide economic insight for fiscal policies

Data scientists

  • Analyze data sets to identify trends
  • Provide data management and database services
  • Differentiate high-quality data from poor-quality sources
  • Interpret data to discover patterns, solutions, or problems
  • Apply models and algorithms to data sets to achieve the desired output
  • Identify data collection challenges and suggest new methods for higher-quality data
  • Ensure quality and uniformity of data sets
  • Use computer software to create specific models or charts for review. 

Similarities between Economists and Data Scientists 

Economics and data science are two fields that share many similarities. Economists can make great data scientists and can complement the skillsets of data scientists from other backgrounds. Both fields have a lot of skills in common, like both require a solid foundation of statistics, seek to solve quantitative problems through modeling, and require strong analytical skills. Data science is also revolutionizing some of the sectors in which economists work: e.g. banking, finance, public policy, and consulting. Economists spend a lot of time thinking about prices, whether these could be monetary or paid through time, where they are used to signal factors relating to the supply and demand in a particular market. Indeed, many data science problems involve aspects that relate to the functioning of markets; and with the use of large human-centric data sets, they are often able to provide a more accurate description of the role that these prices play. Furthermore, it is worth noting that economists already have many of the common tools data scientists use. Regression analysis is the standard empirical tool used by economists, and Data Science also uses the logistic regression as the most common data science tool. Economics and Data Science modeling techniques both require statistical assumptions to be met, to make inferences from data about a complex system. The terminology used in economics and Data Science is different, but both have a language to describe that same system.

Differences between Economist and Data Scientists 

Economists often use data science in their work and data scientists may consider economic figures, but the two careers have some stark differences.

While economists possess strong analytical and quantitative skills, transitioning to data science requires additional expertise. Data scientists need to be proficient in programming languages such as Python or R, have a solid understanding of data manipulation and visualization, and be well-versed in machine learning algorithms. The ability to communicate findings effectively is also crucial, as data scientists often need to convey complex insights to non-technical stakeholders. The main difference between economics and Data Science lies in the focus of their inquiry: the economist focuses on causality, whereas the data scientist focuses on prediction.

Challenges economists face while transitioning to data science 

There are three essential areas where economists are comparatively weak. The first is the ability to work in a scripting language like R or Python. Traditionally, economists make use of Excel, Stata, and SPSS as their tools for empirical analysis, which are largely menu-driven tools. The ability of scripting languages to automate much of daily procedures in a predictable way makes them indispensable in the data science field. 

Understanding overfitting and the effects of splitting a sample into training and testing subsets is also important. Economists generally use all the data they have when conducting an analysis. This means  that the model  ‘overfits the data’ – it explains the patterns specific to the sample data but this may not be generalized to a broader sample. In data science, it is the model building which can be generalized. The standard methodology that is followed to overcome overfitting is to split the sample and build the model to fit the training data and then see how well it functions on the testing data set. In economics, one would often call this an ‘out-of-sample evaluation’.

There are also other skills including more sophisticated machine learning approaches, like Neural Networks, Random Forests, and aspects relating to Bayesian statistics that can be useful. Knowing when these are appropriate to implement and when a simple regression could be more effective is especially useful. Economists would also usually know relatively little about the use and structure of databases and how data should be stored. Knowing a bit more about this, and how to extract data from databases, and join tables, through for example SQL queries, is a useful skill. Economists must navigate these challenges by adopting ethical frameworks and staying abreast of evolving regulations in the field of data science.

Why economists should learn data science skills:

  • Open-source programming is growing rapidly. There are R and Python packages that make analytics workflows easier compared to proprietary software (e.g. data cleaning, scripting to automate tasks, and more flexible modeling).
  • The learning curve to get started in Data Science is not steep, even compared to the learning curve faced by some software engineers. The statistical foundations of machine learning are similar to those used in econometrics.
  • Business problems now draw upon data on a vast scale, e.g. metadata. Interacting with that data requires knowledge and experience with various data types, databases, and specialist big data tools.
  • Data Science is one of the most cutting-edge fields in today’s society. It offers the greatest solutions for dealing with the difficulties of rising demand and ensuring a sustainable future.


The transition from economists to data scientists marks a profound evolution in the field of economics. Embracing data science techniques allows economists to enhance their analytical capabilities, providing deeper insights into economic phenomena. The fusion of traditional economic theories with modern data science methodologies has the potential to revolutionize economic research, policymaking, and decision-making.

As economists embark on this transformative journey, the importance of continuous learning, ethical considerations, and a nuanced understanding of both data science and economics cannot be overstated. The synergy between these two disciplines holds the promise of unlocking new possibilities and addressing complex challenges in our dynamic and interconnected global economy. The future of economics lies in the hands of those who can seamlessly navigate this transition, embracing the power of data to drive meaningful change.