Tools: Jupiter Notebook / Python / Pandas

Objectives

1. Identify the most popular programming languages among IT professionals.

2. Analyze average salaries and income statistics for IT professionals.

3. Explore the age distribution among IT professionals.

4. Provide statistical information on working hours for part-time and full-time IT professionals.

5. Determine the most popular databases among IT professionals.

6. Examine the relationship between income and various factors such as working hours, age, education, and other variables.

Tools:

Python, Jupyter notebook

Libraries

numpy , pandas , seaborn , matplotlib.pyplot

Dataset:

Our data set is a survey works among IT professional , collected and published on Github in the link below.

It has 11551 records and 84 columns. (11552, 85)

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1. Identify the most popular programming languages among IT professionals.

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There are unique 28 programming languages , the top most popular ones are:

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2. Analyze average salaries and income statistics for IT professionals.

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3. Explore the age distribution among IT professionals.

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4. Provide statistical information on working hours for part-time and full-time IT professionals.

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Reading into dataset:

The Max of 1,012.00 hours is mathematically impossible (there are only 168 hours in a week). This indicates “noisy” data or data entry errors in the dataset.

Similar to the full-time data, the Max of 375.00 hours is impossible for a single week, suggesting errors in the source data.

Full-time workers are a much larger group in this data and center strictly around a 40-hour week. Part-time workers have a much broader distribution relative to their average, typically working between 20 and 35 hours.

After applying outlier treatment:

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5. Determine the most popular databases among IT professionals.

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There are 13 unique databases , the top most popular ones are:

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6. D Examine the relationship between income and various factors such as working hours, age, education, and other variables.

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The pair plot shows that experience‑related variables are tightly correlated, compensation rises only weakly with experience, and tool knowledge scales modestly — highlighting that experience is consistent, but not a strong standalone predictor of pay