# Data Analytics for Optimizing and Predicting Employee Performance
## 3 Methods and Results
### Data Preparation
#### 3.3.1 Data Overview  
In order to better understand our data, we will have a look at the attributes that are part of this dataset, as well as descriptive statistics based on them.  
If we look at some visualizations of the dataset as shown in Figure 3, we can obtain an overview of the analyzed inputs, as follows, with the number of employees (No of emp) being analyzed according to various criteria:  
It can be seen that there are 311 employees that are under review and, therefore, 311 distinct registries in our dataset. Looking at the split by recruitment source, we observe  
Figure 3: Employee distribution based on recruitment source, sex, race and department (source: authors’ own study).  
that most of these employees were recruited through LinkedIn and Indeed, while others were recruited through Google search, employee referral, diversity job fair, or career builder. The online platforms are the most popular for the analyzed organization.  
Moreover, if we look at the sex and race, we can first say that very few are Hispanic, while there is a balanced split between females and males that are part of this research. Lastly, we can conclude that most of our employees are part of the Production department, while another big group come from the Information Technology/Information Systems (IT/IS), Sales, and Admin offices. We can see that very few employees come from the Executive offices or the Software Engineering departments.  
In the second set of visualizations, presented in Figure 4, we can see that the average satisfaction score is lowest for the executive officers, and the highest for software engineers. In contrast, if we look at the average salaries for these categories, we can see that the Executive Office department has the highest value; therefore, it seems that not only salaries contribute to overall employee satisfaction. In addition, we can see that females have a slightly higher percentage than males in terms of employee satisfaction, while also having higher salaries on average than males.  
Analyzing the same statistics, as shown in Figure 5, but with negative characteristics such as absences or number of days that employees were late to work, we can see that, on average, the Sales department has the highest number for both categories. Nonetheless, if we look at the Executive Office, we can see that, while they have the lowest satisfaction, they do not have any days in which they were late in the last 30 days, and also have a medium value for the absences taken. If we also return to the previous figure, we see that the Production department has the lowest salaries and low employee satisfaction, a fact that we can understand if we investigate the second chart, in which it is shown that the Production department has high values for both absences and days late at work.  
Moreover, if we look at Figure 6, it is easy to observe, using historical data, how performance scores are distributed across departments. While Software Engineers have high levels of satisfaction but are still classified, in terms of performance, as needing improvement, the other departments, like IT/IS, Production, and Sales, have lower levels of satisfaction associated with lower performance scores. In addition, while there are absences for all employees (despite their performance), we can see that days late at work are only present for those with the lowest performance scores. Therefore, we can conclude  
Figure 4: Employee satisfaction and salary distribution based on department and sex (source: authors’ own study).  
that performance scores are not fully correlated with satisfaction; however, absences or tardiness in arriving at work are correlated with lower performances.