Statistical data modeling is an interesting subset of data analytics, and for a healthy number of tech data enthusiasts, it’s all a matter of choosing between two options to maximize impact – Python versus R.
Let’s take a big bite out of that debate, but set the table first with a good explanation of statistical analysis.
In a word, so-called “data science” is a key component of data analytics. Statistical analysis includes the collection and dissemination of business intelligence, using data samples that present specialized data to analysts. The web site WhatIs.com breaks the definition down further, into five discrete steps, as follows:
- Describe the nature of the data to be analyzed.
- Explore the relation of the data to the underlying population.
- Create a model to summarize understanding of how the data relates to the underlying population.
- Prove (or disprove) the validity of the model.
- Employ predictive analysis to run scenarios that will help guide future actions.
As WhatIs.com puts it, the end game for statistical analysis (also known as “machine learning” in some tech circles) is to identify trends. “A retail business, for example, might use statistical analysis to find patterns in unstructured and semi-structured customer data that can be used to create a more positive customer experience and increase sales,” the site concludes.
The two key languages most used by statistical data developers are Python and R, and each offer unique “pros and cons” to data scientists.
Let’s look at each, one at a time:
R – There’s a conventional wisdom growing in data statistics circles that R offers more flexibility for data analysts than Python. Integration with other computer languages is a highly sought after attribute of statistical data tools, and R offers users plenty of language integration options, including the ability to merge with C++, Java, C, and technology tools like SPSS, Stata, and Matlab. Industry data shows that R can integrate with over 6,500 different software packages, giving some clout to the notion that the language is broader, and more flexible for statistical data analysts than Python (it even has a Python port).
Data mining is another area where R gets the nod over Python. Data mining, i.e., the process used by companies to turn raw data into useful information, is easier with R than with Python. A recent survey shows that 70% of professional data analysts who specialize in mining data prefer R over Python in that category.
On the “cons” side of the equation, there is more of a steeper learning curve for using R, which can slow down performance, and add a measure of uncertainty to data analysis that isn’t as pronounced as other statistical data languages, like Python.
Python – Termed by many end users as a powerful programming language with huge community support, Python is a free, multi-paradigm data software package that is is considered to be highly productive for data analysts who like to create reusable applications, and who place a high priority on reading and exporting data files. Users can distribute their applications for free, with no licensing charges, and in many data analysis labs, is considered to be the go-to language for data scientists.
Data statisticians say Python is an easier, more approachable statistical data analysis tool – many analysts say it can be learned in a day. That might be an easier task for software engineers, as the technology was created by a fellow engineer, Guido Van Rossem back in 1991, who placed a high priority on productivity and code readability.
On the downside, Python is considered by data scientists as a slower language than R, a big element on the data analysis front where the speed of information is arguably as important as any other priority. Python also treat data science analysis as purely as R does, but does offer end users an extensive data library that can pretty much offer the same attributes as a strict data analysis tool.
On the all-important data visualization front (a/k/a) the “look and feel” attribute, Python offers users its Matpltlib, 2D data library (R offers a similar data visualization package, called GGplot 2) that offers a comprehensive array of data options, such as bar charts, pie charts, scatter plots, and other data analysis graphical tools that help data scientists gain a tighter grip on data. Bother offer data modelers a clear and compelling format for visualizing data, and fairly, it’s like splitting hairs choosing between the two – both get the job done, and after that, it’s a matter of personal preference for data scientists in choosing between R and Python on a “look and feel” basis.
Python also has a formidable machine learning tool, called Scikit-learn, that leverages sophisticated algorythms and data patterns to predict end user “likes and dislikes”, that insiders say has a slight edge over a potpourri of similar packages offered by R.
In the end, choosing between Python and R depends on what data scientists value in a data analysis language tool. In fact, data analysis professionals may want to use both simultaneously to see for themselves what language meets their needs best in their never-ending search for optimal data analytics.
In that regard, it’s a “win-win” for data scientists, who have easy access to a pair of powerful and pervasive data analysis language tools, both of which can make a fair claim to being the fastest-growing statistical language in the data science market.
With CoolaData you can now connect to using Python or R in order to build statistical and advanced data mining models. The connection is done by addressing CoolaData’s servers through the simple Query API. You can then run CoolaSQL, a behavioral extension to the SQL language, in the form of a cohort, path or funnel to retrieve sample data for training your models.