The document contains several types of graphs, focusing categorical vs categorical variables. This report explores the impact of various factors on job markets across different industries using a dataset that includes information on job titles, industries, company sizes, locations, salaries, AI adoption levels, automation risk, required skills, remote-friendliness, and job growth projections. By Power BI, we will visualize and analyze the dataset using different types of charts, to highlight how multiple categorical variables interact with each other.
Count of Index by Job Title and AI Adoption Level:
This graph shows the distribution of job titles across different levels of AI adoption (High, Medium, Low). Each bar represents a job title, and the height of the bar indicates the count of indices for that job title
Count of Index by Location and Company Size:
This graph displays the count of indices by location (e.g., San Francisco, Singapore) and company size (Large, Medium, Small). It helps in understanding the geographical distribution of companies of different sizes
Sum of Index by Job Growth Projection:
This pie chart shows the proportion of job growth projections categorized into Decline, Stable, and Growth. Each segment of the pie represents a different job growth projection, with the size of the segment indicating the sum of indices.
This bubble chart shows how salaries vary across different job titles and industries. It makes it easy to see which jobs and industries offer higher pay. The chart also highlights how the same job can have different salaries depending on the industry and location, helping people and companies make better decisions about careers and hiring.
Count of Index by Industry and Location:
This graph shows the number of industries in different locations by combining industry and location data. It gives a clear picture of how industries are spread out across various places.
