Job Trends Analysis

About Project

SITUATION

As a Data Analyst, I undertook an in-depth analysis of a dataset containing 500 job listings across 10 industries. The aim was to generate insights into salary trends, job growth projections, AI adoption, automation risk, and skill requirements to inform job seekers and employers about compensation patterns and technological impacts on the workforce.

TASK

The analysis focused on the following key objectives: 1. Salary Trends and Variability: Analyze salary distributions across industries, job titles, company sizes, and geographic locations, handling outliers and identifying high-paying sectors and skills. 2. AI Adoption and Automation Risk: Investigate the relationship between AI adoption, automation risk, and salaries to assess potential impacts on job roles. 3. Job Growth Projections: Identify roles and industries with the highest growth potential and assess trends in remote work adaptability. 4. Skills and Requirements: Determine how specific skills influence salary, job stability, and adaptability to remote work. 5. Location-Based Analysis: Examine geographic trends in salaries, AI adoption, and automation risk across cities and countries to identify high-paying locations and potential automation vulnerabilities.

ACTION

1. Data Preparation and Cleansing • Data Collection: Loaded data into Jupyter Notebook and Google Colab to leverage flexible, cloud-based processing. • Data Cleaning: Addressed null values, standardized data types, and handled outliers through Z-score and IQR analysis. • Feature Engineering: Created new features such as salary ranges and risk scores (combined AI adoption and automation risk) to facilitate targeted analysis. 2. Exploratory Data Analysis and Visualization • Salary Distribution Analysis: Used descriptive statistics, boxplots, and histograms to explore salary patterns across cities, industries, and job titles. • Location-Based Analysis: Employed choropleth maps and bar charts to visualize salary variations and AI adoption across different regions. • Industry and Job Title Trends: Analyzed compensation trends using stacked bar charts and heatmaps to identify high-paying sectors and in-demand roles. • AI Adoption and Automation Risk Analysis: Assessed automation risk levels across industries, identifying sectors with balanced AI adoption and risk. • Skills and Requirements: Visualized the impact of specific skills on salary, job growth, and remote-friendliness.

RESULT

The analysis uncovered valuable insights into salary trends and job market dynamics: 1. High-Paying Skills and Locations: Technical skills like JavaScript and Python command the highest salaries, especially in cities like Singapore, Berlin, and New York. 2. AI Adoption and Automation Risk: Industries with high AI adoption don't always face high automation risk, suggesting strategic use of AI to support rather than replace roles. 3. Growth and Remote Flexibility: Roles in Data Science and AI Research show strong growth potential, while tech roles exhibit high adaptability to remote work. These insights reveal the importance of skill selection, location, and industry alignment to maximize career stability and compensation. The full analysis in the notebook provides detailed visualizations and metrics to explore these trends further.

Built With Python Libraries

Pandas and NumPy

Used extensively for data wrangling and analysis, enabling efficient handling of large datasets. Pandas was crucial for cleaning and organizing job market data, while NumPy supported numerical operations for calculating summary statistics like mean, median, and standard deviation across multiple columns.

Scipy

Leveraged primarily for outlier detection, Scipy's statistical functions enabled precise identification of extreme salary values across different job titles and regions. This step was essential for ensuring data quality, removing anomalies, and achieving a reliable, representative dataset for further analysis.

Matplotlib, Seaborn, and Plotly

Used for comprehensive data visualization. Matplotlib provided foundational plotting capabilities, while Seaborn enabled advanced statistical plots, highlighting salary distributions and trends. Plotly allowed for interactive, location-based analysis, enhancing insights into salary variations and AI adoption levels across different regions with choropleth maps.