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Tools to get started with the Analytics, and Machine Learning!

Tools to get started with the Analytics, and Machine Learning!

“Tools enable, tools restrain, and tools kills” – choosing them wisely will make the difference between learning and frustration. Given the broad nature of the subject, a variety of tools – ranging from data ingestion till visualisation – are used for Analytics and Machine Learning.

“Make sure that you always have the right tools for the job. It’s no use trying to eat a steak with a teaspoon, and a straw.”

– Anthony T. Hincks

For a starter, the best way to go is to understand the basics of the subject (start here, if you’ve just dropped by), and then apply the concepts through a tool. In this blog, I will introduce you to the tools I use – this is by no means an exhaustive list – and sufficient to get you started with. Once familiarity with the subject is gained, then feel free to explore other tools.

Microsoft Excel

Yes, the good ol’ excel offers a variety of plugins, to get started with the analytics.

Plugin URL
Analysis ToolPak https://support.office.com/en-us/article/Use-the-Analysis-ToolPak-to-perform-complex-data-analysis-6C67CCF0-F4A9-487C-8DEC-BDB5A2CEFAB6
Other plugins https://www.datasciencecentral.com/profiles/blogs/5-excel-add-ins-every-data-scientist-should-install

“R” environment

R is a widely used and accepted open-source software for Machine Learning and Analytics:

Software Software URL
R Project A free software environment for statistical computing and graphics https://www.r-project.org/
RStudio An integrated development environment (IDE) for R https://www.rstudio.com/products/rstudio/


If coding is an issue for you, then the Rattle library, that runs on the “R environment”, can bail you out from the situation.

Software Software URL
Rattle A Graphical User Interface for Data Mining using R https://rattle.togaware.com/

I found Rattle to be the best companion with my exploration and learning of Analytics. It takes away the burden of writing the code (as in the case of Python), and instead focus on the statistics and analytics. The tool is organised to enable a structured approach to analytics – starting with data processing, exploration, testing, modelling, and evaluating (as highlighted below).

Source: https://rattle.togaware.com   

Thank You!

This brings me to the end of this series on Analytics Translator. It was a fun ride, over the past couple of weeks, and Learning and Analytics. Thank you for your overwhelming support on various social media. Hope you had at least a few takeaways from the