Price: $9.99
(as of Dec 27,2024 19:17:56 UTC – Details)
![](https://i0.wp.com/ziontechgroup.com/wp-content/plugins/wp-automatic-plugin-for-wordpress/images/amazon-buy-now.png?ssl=1)
ASIN : B0989W148N
Publisher : Wiley (June 30, 2021)
Publication date : June 30, 2021
Language : English
File size : 87230 KB
Text-to-Speech : Enabled
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Enabled
Print length : 886 pages
HR Analytics: Practical Approach Using Python
In today’s data-driven world, human resources departments are increasingly turning to analytics to improve their decision-making processes. By utilizing data analysis techniques, HR professionals can gain valuable insights into their workforce and make more informed decisions.
Python, a powerful programming language commonly used for data analysis, is a popular choice for HR professionals looking to leverage analytics in their work. In this post, we will explore a practical approach to HR analytics using Python.
1. Data Collection: The first step in implementing HR analytics is collecting relevant data. This may include employee demographic information, performance reviews, time and attendance data, and more. Data can be gathered from various sources such as HRIS systems, surveys, and performance management tools.
2. Data Cleaning and Preparation: Once the data is collected, it needs to be cleaned and prepared for analysis. This involves removing any inconsistencies or errors in the data, handling missing values, and formatting the data in a way that is suitable for analysis.
3. Exploratory Data Analysis (EDA): EDA is a crucial step in the data analysis process, as it helps to uncover patterns and relationships in the data. Using Python libraries such as Pandas and Matplotlib, HR professionals can create visualizations and summary statistics to better understand their workforce data.
4. Predictive Analytics: With the data cleaned and explored, HR professionals can now move on to predictive analytics. Using machine learning algorithms in Python libraries such as Scikit-learn, HR professionals can build models to predict employee turnover, identify high-performing employees, and more.
5. Reporting and Visualization: Finally, the insights gained from HR analytics can be communicated to stakeholders through reports and visualizations. Python libraries such as Seaborn and Plotly can be used to create interactive dashboards and visualizations that make the data more accessible and understandable.
By following this practical approach to HR analytics using Python, HR professionals can unlock the power of data to make more informed decisions and drive positive outcomes for their organization.
#Analytics #Practical #Approach #Python