Analytics
Embrace Open Source with Open Arms via SPSS
October 23, 2017 | Written by: Steen Christoffersen
Categorized: Analytics
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Data Science skills are some of the most valued globally. Statistical Analysis and Data Mining are the second most in-demand skills globally for the second year in a row, according to LinkedIn. Like SPSS these are essential to successful data science.
Skills to Pay the Bills
No matter where you’re doing data science for, you need to know how to use the tools of the trade. This means a statistical programming language, like R or Python, and a database querying language like SQL. These skills are growing more and more essential. For data scientists, and almost every data science role, you need to know R, Python or both.
Tilt the scales (!) in your favour with Python skills
R and Python are the two most popular programming languages used by data analysts and data scientists, with Python the top programming language for 2017and R the sixth. Both developed in the early 1990s, free and open source, the two have a lot in common. You mainly use R for statistical analysis and Python as a general-purpose programming language.
Open source technology defines these high-demand skills. But organisations still value the integrity of their data and want processes and outcomes that fit their business need. How do we combine the two?
SPSS Integrations
The two latest releases of IBM’s SPSS tools, Statistics and Modeler, further deepen the link between the proprietary software and open source tools. This gives data scientists the best of both worlds.
With its latest release, version 18.1, SPSS Modeler introduced additional functionality to its already extensive integrations with R, Python and Spark. Allowing you to bring open source flexibility to the non-coding environment, Modeler 18.1 introduced three nodes that run Python algorithms — one-class SVM, SMOTE and XGBoost. These well-regarded algorithms, first only available via Python coding, are now exposed directly in the Modeler GUI. The latest version even ships with Python 2.7.
Use advanced data analysis with SPSS Modeler and Open Source tools to find hidden patterns in your data
And it is not only Modeler that has extensive links with open source tools. SPSS Statistics allows you to enhance SPSS Syntax with both R and Python through specialised extensions. You can leverage over 130 extensions, or build and share your own to create a customised solution.
Accessing SPSS allows you to merge the knowledge and tools available with this known and respected software, while keeping with the latest developments in data science with its close links to open source. Take advantage of these integrations and try out SPSS today.
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