Last Updated on by Kumar Raja

Data Exploration With Python & R

Data Science is a multi disciplinary technology which relies on Statistics, Programming, Data Analytics, Machine Learning & Data Visualization concepts. Speaking of programming, both Python & R are the most extensively used languages for the data modelling process in Data Science. Some of the Data Scientists rely on R programming for their data modelling operations where as the number of Data Scientists who rely on Python programming is much higher comparatively. 

If you are planning to step into the Data Science industry, then having knowledge of either R or Python would be very crucial. Work towards leveraging intense hands-on skills in relation to Data Science along with the basics of Statistics & Programming by joining for the best Data Science Training In Hyderabad program by Kelly Technologies.

Data Exploration With Python-

Python has the best libraries that best fits the data exploration operations. Pandas is the most predominantly used data analysis library in Python. With Panda, Data Scientists can easily explore the hidden insights from Big Data. Data Scientists prefer using this library as it helps in eliminating the lag which occurs while using Excel.

Another benefit of using Pandas for Data Exploration is that we can redefine its Data frames throughout the project. If there are any data issues like having non-factual data in the tables, then we can easily detect & clean them up with Pandas. Replacing NaN with a value that has verifiable value like 0 for numerical analysis can be possible using Python.

Data Exploration With R-

The process of Data Exploration is all about performing intense numerical and statistical analysis. R is best suited for Data Exploration operations R has a number of packages that support basic optimizations, analytics, statistical, machine learning etc. However, R is having its own limitations in exploring data & to overcome these limitations we need to use other third party tools.

One of the advantage of using R is that data can subjected to n number of statistical tests, build probability distributions. We can easily incorporate Machine Learning functions using R. 

To excel in career as a Data Scientist having knowledge of any of the two programming languages is crucial.

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