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Business Analytics & Intelligence
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HR Analytics - Workforce Management using R

Using erlang c formula to find out if and how many more agents does the bank need to maintain a good grade of service

     
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HR Analytics - Workforce Management using R

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This Course Includes

  • iconudemy
  • icon4.1 (18 reviews )
  • icon3h 32m
  • iconenglish
  • iconOnline - Self Paced
  • iconprofessional certificate
  • iconUdemy

About HR Analytics - Workforce Management using R

This course revolves around finding the right people to be deployed for a given task at the right time, to ensure that the customer expectations and metrics are being met. As an HR professional or a business partner, you play an important role, which is managing the workforce. In this course: You will learn the basics of

workforce management

. Tackle the problem of shortage of employees by performing

resource management

. We will solve a business problem that involves customers getting dissatisfied with the customer care service, due to non-availability and high waiting time which leads to customers abandoning the calls. You will study the

call volume data

and relate with employee demographics. Learn about

Erlang C

and it's applications in

Rstudio

to predict the number of employees required on an hourly interval to meet customer expectations. Master practical skills to solve an HR business problem using a Step-by-step approach called “

Anatomy of a Statistical Model

”. Understand how to

prepare

and

explore

the data for meaningful insights. Applying

feature engineering

techniques to get in-depth knowledge hidden inside the data.

What You Will Learn?

  • Work Force Management .
  • Erlang .
  • HR Analytics .
  • Anatomy of Statistical Model to understand the technique of solving any business problem through analytics. .
  • Learn how to understand Business problem and what are the key factors. .
  • Getting the most out of data using basic analytics technique or you can say Exploratory Data analytics. .
  • Applying feature engineering techniques to get in depth knowledge hidden inside the data..