Happy at work, happy in life? Analysis of the relationship between job satisfaction and happiness

Step 1: Gathering of the data through a survey (46 answers)

71.8% are between 25 and 34 years old. 69.6% of them are women. 43.5% have a mid-level position. 34.8% of them work remotely everyday. Main industries: Education, Technology, Business, Healthcare, Finance. Job Satisfaction average: 6.1/10 Happiness Level average: 7.1/10

Step 2: Data cleaning with Python

Using pandas and numpy to clean the column and make sure I will be able to use the data collected for my analysis.

Step 3: Data Analysis - Demographic Analysis and Job Characteristics Analysis

Demographic Analysis - Pearson correlation comparing the age and gender to the job satisfaction scores and happiness level. Job characteristics Analysis - Correlation between the job satisfaction and the happiness level of 0.61. We can then deep dive into each criteria to better understand their impact and influence. Other studies - Checking studies on work-life balance to understand it better.

Step 4: Conclusions Age, gender, or job level are not enough to justify the job satisfaction or happiness level. However, as it is strongly correlated, an employee satisfied by his job will be happier. For managers, it is important as we know that happy employees will also be more productive and engaged. Work-life balance is the most correlated aspect with the job satisfaction score. It can be improved by increasing the remote frequency of employees but also depends on working hours, autonomy and flexibility. All those results need to be nuanced by the size of my sample.