Technological advancements and innovation are driving business operation today. Both play a significant role in developing a product that stands out among its competitors. As digitalization is attaining pace, about 2.5 Exabytes of data is being generated every day. Such a significant amount of data is essential to store which does not come for free. Organizations use this data to plan for the future by insights drawn from existing data. Planning needs Data Analysis techniques and the selection of the right tool that is R, Python or SAS to perform analytics. The choice amongst tools might sound like debating Mac vs. Windows vs. Linux as all three work properly. However, this is important for beginners who seek a career in Data Science with advanced analytics capabilities. This article focuses on R, SAS, and python and explains to you what makes them essential in data and analytics.

SAS – Statistical Analysis Software: 

SAS is well-known for model development and data processing. SAS became the first choice when analytics function started emerging in the financial service sector. The use of SAS is common for data processing and automated scripting. A constraint that SAS poses is its overpriced nature. Moreover, SAS does not support visualization and parallelization.

R Programming:

Today,  modelers and data scientists use R because of its support for machine learning algorithms and it’s vast library base that keeps on growing. Mostly all disciplines except biotechnology or geospatial processing provide a ready package available in R. A fresher can learn R faster as tutorials are available online– though it is free of cost, support is mostly in forums and examples instead of in documentation. You can use R for data and plot visualizations which are vital for data analysis. You can accommodate business analytics with excel data science course with R-programming for solving advanced statistical problems.

Python: 

Python is not new. However, Python for analytics has come to view recently. Being a free language/software, Python facilitates functional programming. It is vital for text processing, web-scraping, file manipulations, and simple or complex visualizations. When it comes to dealing with analytical models and structured data, Python is progressing fast compared to R,  but python does not support data visualization.

Visualization: 

With functional graphical capabilities, SAS and SPSS allow minor changes in graphs, but fully customizing plots and visualizations in SAS and SPSS is hard. R and Python have an array of modules available to customize and optimize your graphs. Ggplot2 is the most widely used module for R which involves a set of graphs where you can change everything. These interactive graphs help users manipulate data with the help of applications, such as shiny.

Both Python and R learn from each other, for example, Python also has a ggplot-module with functionality and syntax similar to R. Matplotlib is a module used for visualization in Python.

Costs:

The open-source platform of R and Python make them freely available to users. A significant constraint is both languages are hard to understand compared to using SAS or SPSS GUI. Therefore, analysts equipped with R and Python in their skills get higher salaries than analysts who don’t know these languages. Training employees who are presently not familiar with R and Python cost money too. Here, the case is not that the open source programming language is free of cost, but the business case is very quickly made when compared with the license fees for SAS or SPSS that is R and Python are cheaper.

Data Handling and Management: 

SAS is safe for data handling and management on standalone systems. As data size multiplies, we have to be cautious about memory allocation principles of languages and software. A significant disadvantage of R is that it works only on RAM thus creating a big problem for small exercises as they will consume time according to RAM of your machine. Packages like Plyr and DPlyr make easier to work on data manipulation. Python is out of this problem as extensions like Panda and NumPy make data handling and fundamental analysis functions in Python.

Conclusion: 

Staying competitive in the field of data analytics involves high-level coding and programming.  R and Python are actively gaining the trend across all business levels through its open community forums and regular updates. With a high demand for these open source tools, data analytics aspirants must master any one of these tools for better career opportunities.

• R is the best option if you have understood mathematics and statistics well. Python is the best option to get started if you are good at programming and coding.
• If you are an experienced professional, then upgrading is undoubtedly applied to either R or Python integration or SAS Python integration

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