You have slogged to become a data scientist. You not only have a strong educational background with a Master’s degree or a PhD thrown in, but also have an in-depth knowledge of data science with R, Python Coding, and even Hadoop Platform and SQL Database/Coding. In addition, you may have acquired other data-science-specific skills too.
It is evident that all these qualifications help you in better decision making and understanding of business and customers. In other words, you possess the same traits as any good problem solver. This means you look at the problem from many perspectives, understand it before applying your skills and, once you do, you work rigorously not only to resolve the problem, but also explain the results of your execution.
The one big advantage of acquiring data science skills is it makes you competent in entering any career field, even development. A career in data science can move you further in the corporate ladder and provide you other options to work on other industries. In fact, today, data science skills are considered invaluable tools for taking up career in development.
Data Science Skills in Development
If you are wondering how these skills can be applied in development, here’s how:
#1 Predictive framework
In order to increase their social impact, many organizations in the development sectors use a predictive framework. For example, in the health care sector, this facilitates targeting vulnerable households or patients that stop taking their medication and the like, making it easy to specifically know where to implement programs.
Furthermore, use of machine learning also helps in predicting emergency room visits that in the US are expensive and almost out of reach of low-income households. Data science predictions, based on disease type, condition of patient, etc., are helping in preventing emergency room visits. There are many other exampleswhere people have used their data science skills for predicting various outcomes.
#2 Bridging communication gaps
Data science skills make it possible to communicate in technical language with professionals in other fields. For example, even if you don’t play any part in coding the data visualization, say in a cancer care institution, its knowledge will help you in bridging the communication gap with data engineers. This will help you understand how to trace the care pathway of the patient after cancer has been diagnosed by using a flow diagram. For you, opening the technical lines of communication becomes a critical asset.
Even in organizations, where both data scientists and engineers work to help various businesses and organizations use their data better, data science skills come in handy for effectively communicating with technical teams and decision makers in business and policy.
#3 Leveraging big data
Big data has entered the development lexicon and international organizations, such as the World Economic Forum, IDB, the World Bank, etc.,have started leveraging the potential of big data to complement traditional data sources with real time, high frequency and low-cost data. This is achieved by partnering with companies like LinkedIn, Uber, etc.
Your data science skills will come in handy in development startups, such as Zenysis and Premise Data, that are helping governments and international organizations in fields, such as vector surveillance, control of contagious diseases and the like. Not only this, your skills will also be gainfully utilized by companies with high development impact potential in making decisions.
#4 Program targeting and product performance
How to best deploy limited program resources in the development sector is always a problem for governments, NGOs and other organizations. Data science skills, that include machine-learning algorithms, will help you in identifying the beneficiaries most in need of such resources and enable you to take program allocation decisions correctly.
These skills have wide and varied applications. For example, they can help in predicting which households are most likely to purchase certain products or which farm is likely to have a low crop yield, or even which students are most likely to discontinue their studies. In healthcare sector too, data science is being used in things like recording and analyzing the performance of individual refrigerators storing vaccines in order to ensure vaccine safety and efficiency.
Conclusion
Data science skills in the current professional scenario are indispensable and are growing in importance with every passing day. You need to master either data science with R or data science with Python for landing a job.
However, if you want a career in development with these skills, it is important to have a strong foundation in econometrics and statistics to help you in correct data interpretation.
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