The real challenge in data science

Big Data Analytics, Econometrics

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Developing the right mindset of Data-Curiosity

The real challenge in data science is to creatively utilize the opportunities that R and Python can open up for you in Big Data Analytics

In continuation form our previous article, we explored the many benefits of using advanced statistical languages like R and python for Big Data Analytics. The question is not whether you need to invest time in learning R. The question is how do you utilize the world of opportunities that raw data analysis platforms like R & Python open up for you! This is a real challenge in data science.

We find that more than learning these languages, the challenge in data science becomes how to exercise and develop the muscles of data-curiosity correctly. What we have witnessed is that once familiar with the different possibilities of data analysis available through R, managers need to be introduced to most suitable way of framing the data analysis problem, applying correct statistical methods and making accurate and statistically valid inferences. With great power comes great responsibility!

Also, I would like to point that developing a mind-set for satiating data-curiosity is not dependent on learning a data-analysis language like R. With a new wave of students graduating with strong experience in R, Python, etc. and entering the work force, it is important for senior managers to know about the capabilities and risks of advanced data analysis instead of blindly believing newly hired Data-Scientists. They need to know the what and why of the steps employed for the analysis, they need to be able to ask , understand and approve which statistical methods were employed to create a model, etc. in order to evaluate the ‘correctness’ of the inferences and insights presented to them.