prodigy234/Data-Analytics-Project-On-Tech-Jobs-in-Nigeria-A-NumPy-Based-Insight

This project focuses on performing in-depth data analytics on data representing tech job roles in Nigeria. The analysis leverages the power of NumPy, a core Python library for numerical computing, to extract meaningful insights related to job roles, salary trends, experience levels, cities, and employment types across the Nigerian tech ecosystem. # Numpy Data Analytics on Tech Jobs in Nigeria ## Project Overview This project uses **NumPy** to simulate, analyze, and extract insights from synthetic data representing tech jobs in Nigeria. It covers aspects like job roles, cities, employment types, experience levels, salary distributions, and gender pay analysis. The goal is to understand salary trends, disparities, and job market characteristics in the Nigerian tech sector. --- ## 📬 Author **Gbenga Kajola** LinkedIn Certified_Data_Scientist Certified_Data_Analyst Certified_SQL_Database_Programmer --- ## Dataset Simulation - **Job Titles:** 20 common tech roles (e.g., Data Analyst, Software Engineer, AI/ML Engineer). - **Cities:** 5 Nigerian cities (Lagos, Abuja, Port Harcourt, Enugu, Ibadan). - **Employment Types:** Remote, On-site, Hybrid. - **Experience Levels:** 0-2 yrs, 3-5 yrs, 6-10 yrs, 10+ yrs. - **Salaries:** Simulated based on realistic salary ranges per job role, randomly assigned to 200 records. A fixed random seed ensures reproducibility of results. --- ## Code Explanations ### 1. Salary Generation per Job Role ```python salary = np.array([ np.random.randint(*salary_ranges[job]) for job in job_role ]) ``` - For each job role in `job_role`, randomly generates a salary within its defined range. - Uses list comprehension to efficiently create a salary array. - `*salary_ranges[job]` unpacks the low and high range values for `np.random.randint`. - The resulting `salary` array holds 200 simulated salary values corresponding to each job role. --- ### 2. Average Salary by Job Title ```python for title in np.unique(job_titles): avg_sal = np.mean(salary[job_role == title]) print(f"{title}: Average Salary = ₦{avg_sal:,.2f}") ``` - Loops over unique job titles. - Filters salaries corresponding to each title. - Calculates and prints the average salary formatted with the Naira symbol and two decimals. --- ### 3. Average Salary by Experience Level ```python for level in np.unique(experien …