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Anything processing data can be a people analytics platform.
First, it was paper and pen. Then, we added calculators. Later, spreadsheets like Excel, Keynote, and Google Sheets. Even later, SPSS, M*Plus, R, and Python. Today, we even have instances dedicated to graphs—Tableau and Google Data Studio.
You can spend your entire life hunting down and mastering new technologies. And, what we have right now is enough to fill a lifetime to truly master.
What should you choose?
Many say, “master what you need.” Others, “master everything you can.”
I say, master Python.
As an R user, you probably did not expect me to say Python.
Yet, here I am. Why?
A good people analytics tool needs to be efficient, reliable, and flexible.
Arguably, all solutions above afford a powerful way of approaching data and analysis with efficiency, reliability, and flexibility.
Yet, like a snake, Python wins the game of flexibility.
With Python, you get a full suite of data processing and manipulation.
Out of your Excel spreadsheets and Access data structures! You are now swimming in a real sea of data objects floating around in Python. You are now limitless in what you can do with the data and how quickly you can convert it from one data form to the next.
One line of code.
One line of code is all it takes to run operations.
Your data structures become flexible, and so is your mind.
Though eliminating many platforms immediately from running, a few have similar capabilities (i.e., R).
Yet, the future potential of analytics in Python is superior to R’s.
With Python, you get anything from basic arithmetic to more complex statistical models. You can venture further, moving from basic analytics to state-of-the-art data science models.
Although this powerful suite is equal to (and sometimes falling behind) R today, it will outgrow R soon.
R is a statistical programming language focusing on coding for statistics. Hence, it focuses on applying statistical theory to data.
The philosophy of Python is different.
Python is the language of outcomes.
It is not made by statisticians for statisticians.
It answers the question, “So what?”
It is made by data scientists for data scientists.
This is why I predict most key developments in people analytics will be in Python. I predict the new generation of data scientists and analysts will learn Python from the start, replacing the old software for greater alignment with the field and flexibility.
This happened 5 years ago with SPSS and M*Plus fading into the background, as I learned R.
This will happen again with Python.
Choose flexibility. Choose the future. Choose Python.
Cheers,
K
Want to learn practical people analytics? Check out my upcoming course and sign up to be the first to know about the launch!