Which is better for data analysis: R or Python or else?


Data sciences have become a crucial part of everyday jobs. The availability of data, advanced computing software, and a focus on decisions that are analytics-driven has made data sciences a booming field. Jobs abound in this field and hence large interest also exists on which languages to learn. 

R and Python are the most popular tools for data science work. Both are flexible, open source, and evolved just over a decade ago.R is used for statistical analysis while Python is a programming language that can be termed general-purpose. These are both in combination essential for data analysis where you are involved in working with large data sets, machine learning, and creating data visualization insights based on complexities involving data sciences.

The process of Data Science:
Very simply put the course on data science involve the four subdivisions discussed below. Let’s compare the two for the following.

Data Collection:
Python is supportive of different data formats. You can use CSVs, JSON and SQL tables directly in your code. You can even find Python solutions when stuck on Google. Rvest, magrittr, and beautiful soup packages in Python resolve issues in parsing, web scraping, requests etc.

Data can be imported from CSV, Excel, text files etc. Minitab or SPSS file formats can be converted into R data frames. R is not as efficient in getting web information but handles data from common sources just as well.

Data Exploration:
One can hold large volumes of data, sort, display data and filter large amounts of data using Pandas without the lag of Excel. Data frames can be redefined and defined throughout a project. You can clean data and scan it before you clean up empirical sense data.

R is an ace at numerical and statistical analysis of large datasets. You can apply statistical tests, build probability distributions, and use standard ML and data mining techniques. Signal processing, optimization, basics of analytics, statistical processing, random number generation, and ML tasks are easy to perform from its rather limited libraries.

Data Modeling:
Numerical modelling analysis with Numpy, scientific computing with SciPy and the scikit-learncode library with machine learning algorithms are some excellent working features in Python.

The R’s core functionality and specific modelling analysis are rather limited and compatible packages may have to be used.

Data Visualization:
The Anaconda enabled IPython Notebook, the Matplotlib library, Plot.ly, Python API, nbconvert function and many more are great tools available in Python.

ggplot2, statistical analysis abilities, saving of files in various formats like jpg, pdf etc, the base graphics module and graphical displays make R the best tool for statistical analysis complexities.

Before choosing, ask these questions
• Do you have programming experience?
• Do you want to do a Python course for business analytics or a business analytics course?
• Do you want to go into research and teaching or work in the industry?
• Do you want to learn ML or statistical learning in data sciences?
• Do you want to do software engineering?
• Do you want to visualize data in graphics?

Research well and you will find that depending on what functions you need both are excellent languages to learn for a career in data science.

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