Welcome to the course! Ben Teusch Human Resources (HR) Analytics - - PowerPoint PPT Presentation

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welcome to the course

Welcome to the course! Ben Teusch Human Resources (HR) Analytics - - PowerPoint PPT Presentation

Sep 12, 2023 92 likes •349 views

DataCamp Human Resources Analytics in R: Exploring Employee Data HUMAN RESOURCES ANALYTICS IN R : EXPLORING EMPLOYEE DATA Welcome to the course! Ben Teusch Human Resources (HR) Analytics Consultant DataCamp Human Resources Analytics in R:

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Welcome to the course!

HUMAN RESOURCES ANALYTICS IN R: EXPLORING EMPLOYEE DATA

Ben Teusch

Human Resources (HR) Analytics Consultant

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Introduction to HR analytics

also known as people analytics, workforce analytics, or talent analytics a data-driven approach to managing people at work

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Tools for the course

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DataCamp Human Resources Analytics in R: Exploring Employee Data

A general process for HR analytics

Identify the groups to compare Calculate summary statistics about those groups Plot or test the differences between those groups

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Identify the groups to compare

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Calculate summary statistics about each group

> mean(finance$tenure) [1] 4.81287 > mean(engineering$tenure) [1] 5.78693 > max(engineering$overtime_hours_worked) [1] 188 > sum(finance$sick_days) [1] 372

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Compare the differences statistically or visually

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Course Overview

Chapter 1: Identifying the best recruiting source Chapter 2: What is driving low employee engagement? Chapter 3: Are new hires getting paid too much? Chapter 4: Are performance ratings being given consistently? Chapter 5: Improving employee safety with data

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Let's practice!

HUMAN RESOURCES ANALYTICS IN R: EXPLORING EMPLOYEE DATA

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Applying the HR analytics process

HUMAN RESOURCES ANALYTICS IN R: EXPLORING EMPLOYEE DATA

Ben Teusch

HR Analytics Consultant

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Applying the process to recruiting

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Applying the process to recruiting

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Quality of hire

What makes one hire better than another? retention, or how long the employee stays their manager's satisfaction with the hire job performance the amount of time it takes to become fully productive

> names(recruitment) [1] "attrition" "performance_rating" "sales_quota_pct" [4] "recruiting_source"

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Calculating the attrition rate

attrition rate: If attrition = 1 when the employee left, this can be rewritten as: mean(attrition) headcount attrition

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Review of tools from dplyr

> library(dplyr) > > recruitment %>% + group_by(recruiting_source) %>% + summarize(highest_performance = max(performance_rating)) %>% + arrange(highest_performance) # A tibble: 4 x 2 recruiting_source highest_performance <chr> <dbl> 1 Search Firm 3 2 Referral 4 3 Applied Online 5 4 Campus 5

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DataCamp Human Resources Analytics in R: Exploring Employee Data

New Tools

> recruitment %>% + count(recruiting_source) # A tibble: 4 x 2 recruiting_source n <chr> <int> 1 Applied Online 130 2 Campus 56 3 Referral 45 4 Search Firm 10

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Let's practice!

HUMAN RESOURCES ANALYTICS IN R: EXPLORING EMPLOYEE DATA

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Visualizing recruiting data

HUMAN RESOURCES ANALYTICS IN R: EXPLORING EMPLOYEE DATA

Ben Teusch

HR Analytics Consultant

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Small number of groups

> call_center_a %>% + group_by(team) %>% + summarize(avg_calls = mean(calls_made)) %>% + arrange(desc(avg_calls)) team avg_calls <chr> <dbl> 1 D 84.21283 2 B 79.65947 3 A 73.80612 4 C 61.73712

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Large number of groups

> call_center_b %>% + group_by(team) %>% + summarize(avg_calls = mean(calls_made)) %>% + arrange(desc(avg_calls)) team avg_calls <fctr> <dbl> 1 J 98.16318 2 U 89.64824 3 M 84.90123 4 L 82.90802 5 D 82.62958 6 E 82.08344 7 C 80.46505 8 K 79.91899 9 F 77.28148 10 R 75.66100 # ... with 16 more rows

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DataCamp Human Resources Analytics in R: Exploring Employee Data

A simple bar chart

> call_center_b_summary team avg_calls <fctr> <dbl> 1 J 98.16318 2 U 89.64824 3 M 84.90123 4 L 82.90802 5 D 82.62958 6 E 82.08344 7 C 80.46505 8 K 79.91899 9 F 77.28148 10 R 75.66100 # ... with 16 more rows > ggplot(call_center_b_summary, aes(x = team, y = avg_calls)) + + geom_col()

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DataCamp Human Resources Analytics in R: Exploring Employee Data

A simple bar chart

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DataCamp Human Resources Analytics in R: Exploring Employee Data

A polished bar chart

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DataCamp Human Resources Analytics in R: Exploring Employee Data

Let's practice!

HUMAN RESOURCES ANALYTICS IN R: EXPLORING EMPLOYEE DATA