Although computer programming has improved exponentially in the past several decades, not all programming languages are capable of processing the data used for machine learning. Big data has grown significantly, and the resulting data sets are both larger and more complex. Text data analysis, commodity modeling, and other processes must have the appropriate coding infrastructure in order to give detailed and useful results.
Fortunately, computers and their languages have largely caught up. With well-crafted statistical analysis programs, AI can now make stunningly accurate predictions about everything from healthcare trends to traffic patterns.
SAS and SPSS are statistics-oriented softwares that have been widely used across a variety of industries. However, the open-source capabilities of Python and R have allowed them to surpass SAS and SPSS in many ways.
SPSS and SAS
SPSS is designed to be Click and Play, so statisticians with relatively little programming knowledge can still run complex technical programs.
SAS has a flow-based interface and drag-and-drop functionality to make it easier for statisticians to use. It has similar overall capabilities to SPSS, but with a different interface.
Traditionally, statisticians have used SPSS or SAS for modeling across a wide range of industries. They were designed to answer complex explanatory questions, like why a group of customers is buying certain products or services. These softwares are very powerful but are also expensive to license.
Python and R
Python and R are two open-source programming languages that are common for statistical analysis. The University of Auckland created R in 2000, and Python was created in 1989. However, they have only really spiked in popularity in the past decade.
R was technically designed as a counterpart to SAS and is commonly used in statistics. However, when combined with Python, it makes an excellent open-source program for data analytics. Both Python and R are capable of predicting future customer trends and can answer certain explanatory questions in the same way SAS and SPSS can.
Current Features and Capabilities
Although Python and R are open-source, they can currently handle significant amounts of deep learning. They have already evolved significantly in the past decade, yet new features still appear all the time. Their deep learning capabilities are fantastic, especially for open-source languages. The only real difference is that R is a little slower with large datasets but was an early adopter of deep learning language and can handle more complex programs.
SPSS is limited in its syntax and is slow to add new features. It is also somewhat slow to run programs involving large datasets. SAS allows more customization, but it still does not have a large library of possible features, and currently has limited support for deep learning.
SPSS and SAS both require separate programs and licenses for big data applications. While these separate programs work well with the base SPSS and SAS code, they are a separate expense that can be burdensome for small- and medium-sized businesses.
Programming Complex Operations and Graphs
All four languages have significant ability to handle complex equations and operations. Python can create a wide array of customized graphs thanks to free code libraries like VisPy, but it is limited in how it can compute and display answers to explanatory questions. R has even better graphing capabilities and can still handle analysis with most explanatory questions.
Python and R can also be automated and plugged directly into CRM back-end to increase customer care and marketing efficiency or can be used for predictive maintenance to reduce downtime. Small and large companies can calculate and easily access more data and answer more questions than ever before.
SAS has limits on how much graphical customization it can do and requires an additional program license to customize graphical displays. Although it is very powerful software overall, it lacks user-friendly features and usefulness in fields that increasingly require deep learning.
Similarly, SPSS has powerful features, but they are limited in applicability. They are most useful at universities and research institutions, and present significant challenges when used with CRM software or other business purposes.
Open-Source Computing Power
Open-source programming languages are, by definition, free. This means their creators have little ability to offer formal technical support, which can make dealing with these languages difficult for non-programmers.
However, open-source languages also have a rich community of programmers who create new features all the time. They craft tutorials and other training for beginning and advanced programmers alike.
Python has a massive worldwide community of users that work in a variety of industries, so millions of already-programmed features can be customized to suit your needs. R is slightly less common, but plenty of statistical programs are still available.
SAS and SPSS both have excellent technical support, but it comes at a steep price, and they still have hard limits to their capabilities. Data specialists who use Python and R can provide their own technical support and training to companies that need it.
Keeping Up with Trends
Most professional coders now learn multiple languages in order to compete in the programming job market. However, it’s not feasible for them to learn every language.
Python is one of the most popular programming languages, so it’s not difficult to find professionals who work with it. It’s easy to learn, and most computer programming degrees and coding boot camps include extensive training in it. Although applying it to big data and statistical analysis takes some special training, many can learn these skills.
R is not nearly as common outside of statistics, largely because it has a much steeper learning curve. It’s popular enough in data analysis that many statisticians work with it in fields like medicine and astronomy, which require highly detailed data and equations.
SAS and SPSS, on the other hand, are becoming less common. It is expensive for companies to train professionals in these languages, so it is much harder to find and recruit new talent that already has up-to-date training and knowledge.
Conversion to Open-Source Platforms
Because of the annual renewal fees for SAS and SPSS, switching to Python or R can result in significant savings over time. Although there are minor up-front costs associated with the transfer, the low ongoing maintenance and customization costs make the investment worthwhile for startups, major corporations, and everyone in-between.
The increased customization and better deep learning features allow businesses to learn and improve their operations, unlocking increased profits and customer loyalty. Switching to Python and R modernizes a company’s systems and makes it easier to make future improvements by tapping into the knowledge of the vast open-source programming community.
Switching to open-source platforms requires specialized knowledge of both the original coding language and the new open-source platform. Fortunately, data analysis specialist, Syntelli Solutions can help.
We can use open-source software to create brand new databases and deep learning systems for you or convert your old SAS and SPSS programs to Python and R.
We can customize a solution to meet your needs, whether you are refining your data analysis or deciding which program is best for your unique business.
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