Training Online – Statistic for HR

DESCRIPTION

As HR embraces the promise of data-driven decision-making, a HR Data Scientist can provide organizations with the leadership, guidance, and governance to scale HR data science initiatives. Here’s what the role of a HR Data Scientist looks like.

Data has rapidly become the backbone of many industries and business functions, including human resources. In the era of digital-everything, data is the new oil powering business growth and profitability. The shift towards data-driven decision making in HR, underpinned by algorithms and machine learning is uncharted territory for most organizations.

One of the most significant data issues for HR Data Scientists will be the lack of sufficient datasets required for machine learning and AI algorithms. Even across enterprise-grade organizations, HR data available pales in comparison to data from other business functions like sales and marketing. Considering how these technologies rely on historical data for training, modern machine learning and AI approaches to HR data might not be successful. However, traditional statistical and analytical approaches may be better suited for these types of situations.

The key responsibility for a HR Data Scientist will be approaching the development of statistical models with ethical principles in mind from the get-go. With tech giants like Google and Amazon beginning to create mechanisms for ethical AI development, it is only a matter of time before regulators ask organizations to have a system of data accountability in place, and HR Data Scientists will be expected to help navigate the organization through data privacy and data governance issues.

BENEFIT

• Data analytics and sciences will also help the organizations to harness the business goals more effectively and also cut down their total hiring costs.
• Data sciences can also be used to provide better techniques for optimization, forecasting, and reporting.
• It can be used for tracking, analyzing and sharing employee related information that can be used to gain an insight into the candidate’s profile.

OUTLINE

1. Plan for People Analytics

• Implementing Data Driven HR
• CRISP-DM framework for HR
• 5 Steps Scenario for HR Predictive Analytics
• Type Insights of Prediction
• Intelligent System for HR Decision Support System
• Use Case of Predictive Analytics Across Employee Life Cycle

2. Power Up Your HRIS Data

• Data Preparation and Cleansing
• Exploration Data Analysis

3. Statistical and Prediction Modeling

• Causality and Correlation
• Linear Regression
• Multiple Linear Regression

4. Data Sprint

• Design Methodologies for Data Science and Analytics Projects

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