In this online training course “Introduction to Using Python for People Analytics”, Ben Zweig takes us through the basics of using Python and how you can use it for your People Analytics projects. This course will show you how to download and get started with using Python and Jupyter notebooks, as well as teaching you some of the basic code that you need to get started using Python for your HR analytics projects. In this bitesized learning Ben explains how the tools being used in people analytics are changing and why Python is becoming one of the more popular choices to analyse large sets of data.

Why should you use Python for People Analytics?

It is fair to say we’re in the midst of the fourth industrial revolution with the pace of change considerably faster than we’ve ever experienced before. All fingers are being pointed at the digital transformation as the biggest culprit of driving such rapid change. Over the last two years we’ve produced nine times the amount of data than ever before. With such a vast amount of data being produced, CEOs are required to continually transform their organisations, acquire new skills and capabilities and implement new tools to be able to thrive in this digital world.

Within the HR function, we’ve seen a growth in adoption of HR cloud applications, with companies investing heavily in programs to use data for all aspects of workforce planning, talent management, operational improvement to drive real business value.

According to research by the Corporate Research Forum, 69% of organisations with 10,000 employees or more now have a dedicated team focusing on People Analytics. In a study carried out by Deloitte 71% of companies view people analytics as a high priority. However, despite knowing the importance and having a dedicated people analytics team only 9% felt they had a good understanding of the talent dimensions that drive performance and only 8% felt that had data that was considered usable in the first place.

In order for HR as a function to really overcome some of the strategic challenges they’re faced with - such as developing and retaining talent, enabling a growth mindset, driving agile working cultures and improving performance to name a few, understanding people data is imperative. HR or people analytics is the science of gathering, organising and analysing the data related to areas like recruitment, talent management, workforce planning and employee engagement to turn the data available into valuable insights that can be leveraged by the business to make decisions that are grounded in the data and add business value.

The changing nature of analytics within HR has called for a variety of new skills to be acquired in areas such as data science, programming and analytics. As the data available at our fingertips continues to grow, it seems that tools like Excel are no longer able to do everything we need for our analytics projects. So, how are the tools needed for people analytics changing? The need for more advanced statistical calculations when analysing people data has seen HR analysts moving away from Excel and SPSS and embracing programmes such as Python and R.

So, why should you use Python for People Analytics projects?

For those that might not be that familiar with Python here a little background information, firstly it’s an open source programming language with a wide variety of supports and developers. This means there’s an array of packages designed to work with Python for all sorts of industries. Ultimately, it’s used for general purpose programming and is well known for its readability, scalability and the instant feedback it provides. Python is also a glue language meaning that it can integrate with existing software and other programming languages.

As Ben Zweig explains in our online course “Introduction to Using Python for People Analytics” there are 5 fundamental reasons why you might choose to use Python over Excel when trying to derive insights from people data.

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Readability & Collaboration

You may be very well versed in using Excel to analyse data and Python's got a few key advantages over Excel. Number one is it's readable. If you're looking at someone else's Excel sheet or even your own from a long time ago you might not know exactly what’s going on. You have to double click on every cell and look at the formula. It takes a long time to understand what's really going on behind the scenes in an Excel document. Whereas with Python everything's laid out very clearly. The code you write to carry out analysis can be saved as scripts and reused multiple times. Unlike in Excel you can ensure that your code has executed correctly and the output will be consistent, which means you can have confidence that the output will always be correct if others repeat your code. Due to the way that code is structured in Python you’re able to collaborate with others, which is increasingly difficult to do with Excel.  

Wide Ranging Capabilities

The types of statistical tools that are available in Python are just enormous. As HR along with the rest of the world moves into an era of big data and deeper insights, researchers and academics are increasingly turning to Python to create predictive models and derive greater insights from their data. Python alongside other methods such as natural language processing and text analysis is being used by some HR functions to conduct analysis from social media websites, employee engagement and pulse surveys in order to mine insights on organisational networks, culture and sentiment and examine how they drive change.

Speed and Scalability

Another reason why you might prefer Python quickly becoming a preferred tool when it comes to people analytics is speed and scalability. You can analyse large datasets in Python whereas with Excel you max out after a certain number of rows. How long have you waited idle for calculations to complete or for large data sets to be imported? If you're doing a large complex analysis you might find that Excel is just too slow for your needs. Whereas with Python you’ll be able to accomplish the same thing very quickly. It’s important to note that Python allows you to repeat analysis, whereas with Excel there is often the requirement to rewrite the same formula multiple times, reducing productivity.

Both R and Python have more advanced statistical capabilities than Excel, this is particularly true of R, which was designed with advanced statistical analyses in mind. Both languages also allow for the creation of machine learning models often with the integration of machine learning packages and frameworks like caret, scikit-learn, and TensorFlow.

As mentioned, Python is an open source programming language. So if you're using it for statistical analysis being an open source language gives it a few key advantages over programs like SPSS and SAS. There are three key advantages are, firstly it’s free. This provides considerable cost savings to many HR functions as they negate the need to purchase multiple and often expensive licences. The second advantage is due to the fact that it is free, it has resulted in a larger proportion of users than some other programming languages and we all know that with greater users comes greater support. Finally, the third key reason is that new capabilities and improvements are rolled out to Python far more regularly than other statistical packages.

Finally, Python is often compared with R, which is also becoming increasingly used in HR analytics teams. R is a great tool to leverage for statistical analysis and has the ability to cope with large datasets. It is also allows you to visualise data in a wider variety of ways unlike Excel. Like Python, R is also an open source programming language that is specifically geared towards statistics. However, it's a little bit narrower in breadth than Python, and of course being a broad-based programming language is what makes Python so popular among many data scientists. If you're working on a team that wants to integrate with other programmers, data engineers or people who create software you can integrate your Python code right into that process and right into a product.

The world of people analytics and HR is changing - for every dollar invested in analytics an ROI of $13.01 can be seen, according to research from Nucleus. The amount of data available at our fingers is rapidly growing and in order to be able to generate real insights to support the business, make strategic decisions that are grounded in data, HR must move away from leveraging legacy tools and embrace statistical packages and programming languages such as Python to meet the needs of the business.

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The myHRfuture academy is a learning experience platform for HR professionals looking to invest in their careers. We have many training courses that are targeted at People Analytics professionals and HR Business Partners to help learn about HR analytics and how to use data in conversations with the business.

For more information on all of the courses that we have available on People Analytics for learners of all levels, as well as more information on our HR learning platform, that combines bitesized learning content on HR analytics with articles, research, case studies, podcasts, videos and access to a community collaboration platform for you to share ideas with peers and get answers from experts, take a look at our academy. If you’re looking to upskill and learn how to become an HR analyst, these courses will help you to further your career in HR analytics.

ABOUT THE AUTHOR

Ian Bailie is the Managing Director of myHRfuture.com and an advisor and consultant for start-ups focused on HR technology and People Analytics, including Adepto, Worklytics and CognitionX. In his previous role as the Senior Director of People Planning, Analytics and Tools at Cisco Systems, he was responsible for delivering the tools and insights to enable and transform the planning, attraction and management of talent across the organisation globally. Ian is passionate about HR technology and analytics and how to use both to transform the employee experience and prepare companies for the Future of Work.