This project was built for my course, ChatGPT for Data Analytics, to uncover the most optimal skills to learn as a data nerd based on real-time data science job posting data.

Data Source: datanerd.tech

AI Note: This entire project was built using ChatGPT, from generating the code for visualizations to producing the text for this markdown file.

The most optimal skills were determined by developing a metric, termed "Skill Multiplier", that aggregates both the normalized demand and normalized salary of a given skill for a job title into a single metric.

When analyzing the 3 most popular jobs in data science, Python and SQL stick out consistently as two of the most optimal skills. (Note: I also analyzed other titles in data science and they had similar trends)

🔢 Data Analysts: SQL is a must-have with a median salary of $92,500 and a significant presence in job postings. Python isn't far behind, proving essential in the data toolkit.

💻 Data Engineers: They benefit greatly from Python, with a median salary of $130,000. SQL also plays a pivotal role in this high-demand job title.

🧪 Data Scientists: Python leads the way with a median salary of $126,500, reflecting its critical role in data modeling and analysis. SQL remains a fundamental skill, underscoring the importance of data manipulation and retrieval.

The normalized value for each skill within a job title is calculated as:

$$ \text{Normalized Value} = \frac{\text{Value} - \text{Min Value within Job Title}}{\text{Max Value within Job Title} - \text{Min Value within Job Title}} $$

The skill multiplier, which combines the normalized skill count and normalized median salary, is calculated as:

$$ \text{Skill Multiplier} = \text{Normalized Skill Count} \times \text{Normalized Median Salary} $$

• The USA holds the majority of data job postings, a clear market leader.
• A noticeable presence in Europe and India, showcasing a growing global demand.
• South America and Africa show emerging markets, indicating potential growth areas.

The data job market is primarily concentrated in North America, with significant opportunities across Europe and Asia. 📡📈

• LinkedIn is the colossus, with the highest number of job listings.
• BeBee and Trabajo.org show strong presence, possibly indicating niche or regional preferences.
• Indeed and Recruit.net are significant players, demonstrating their global reach.

LinkedIn’s dominance suggests it’s a critical platform for job seekers in the data field. 🌐💡

• Data Analyst takes the lead, indicating a high demand for data processing skills.
• Data Engineer and Data Scientist follow, reflecting the industry's need for both infrastructure and analytics expertise.
• Senior roles like Senior Data Engineer and Senior Data Scientist suggest a market for experienced professionals.

The high numbers for foundational roles like Data Analyst and Engineer highlight their importance in the data ecosystem. 🌐🚀

• SQL and Python dominate as essential programming languages.
• Cloud services are crucial, with AWS leading the cloud skillset.
• Visualization tools like Tableau and Power BI are key player tools.

Programming prowess, cloud fluency, and visualization proficiency are the pillars of today's data field. 🌐🔍

• SQL is the top skill, crucial across Data Analyst, Data Engineer, and Data Scientist roles.
• Python shows strong demand, especially for Data Scientists, reflecting its versatility in data analysis and machine learning.
• R has a notable presence, particularly in data science positions, indicating its statistical analysis strength.
• Tableau and Excel showcase the need for data visualization and spreadsheet skills.
• Power BI is also featured, though less prominently, highlighting its role in business intelligence.

The data underscores the importance of a strong foundation in data querying and programming languages. 🖥️✨

• The histogram shows a bimodal distribution of yearly salaries, suggesting two common salary ranges or peaks (one for junior roles, another for senior roles)
• The first peak occurs below $100,000, indicating a large number of entry to mid-level positions.
• The second peak, between $200,000 and $300,000, could represent senior or specialized data roles.
• There's a long tail extending towards higher salaries, though these cases are less frequent.

This visualization highlights the potential for high earnings in the data field, especially with advanced expertise or roles. 🚀💼

• A striking trend: the majority of countries show less than 10% of job postings include an annual salary in a job postings.
• Outliers like Brunei and the Bahamas are rare, with Brunei's job postings revealing salary over 30% of the time and the Bahamas above 25%.
• The United States sits just above 5%, but this still means around 95% of job listings leave applicants guessing. 🤔💡

The data speaks volumes about the global opacity in salary information, with most job seekers left in the dark. 📉🌐

• Senior Data Engineer leads the pack with a median annual salary well above $14 0,000.
• Senior Data Scientist isn't far behind, marking a high value on seniority in data roles.
• Interesting to note, a Data Analyst position shows a noticeably lower median salary compared to engineering roles.
• Cloud Engineer closes the list, hinting at a competitive but slightly lower earning bracket within the tech field.

This bar chart illustrates the premium placed on senior roles and specialized engineering skills in the data job market. 🔝👩‍💻

• Spark and Java are at the top, commanding salaries near the $140,000 mark.
• Snowflake skills also yield a high median salary, despite fewer job postings (3382).
• SQL is a common skill with over 16244 postings, yet the median salary doesn't top the chart.
• Excel shows the lowest median salary, aligning with its position as a foundational, rather than a specialized, skill.

The bar chart clearly aligns high median salaries with specialized tech skills, showing the market's demand for advanced competencies. 🚀📈

• Python: Dominates as a high-impact skill across nearly all data-centric roles.
• SQL: Consistently important, essential for data manipulation and querying.
• Java: More significant for Software Engineering roles; less so for pure data positions.
• Scala: Valuable in Big Data environments, especially for roles involving Spark.

• AWS: A top skill for Senior Data Engineers, reflecting the cloud's dominance in the industry.
• Azure: Important for Software Engineers and Cloud Engineers, showing a trend towards multi-cloud expertise.
• Snowflake: Notable for data roles, aligning with the rise of cloud data platforms.

• Tableau: A key skill for Data Scientists and Senior Data Analysts, indicating the importance of data storytelling and business intelligence.
• Power BI: More specific to Senior Data Analysts, suggesting a niche for business-focused analysis within certain sectors.

• Excel: Still relevant, especially for Business Analysts, signifying that traditional spreadsheet skills retain their value in the job market.
• Pandas: Mentioned for Data Scientists, underlining Python's ecosystem for data manipulation as a critical skill.

Overall, the trend suggests a high value placed on versatile programming skills, proficiency in major cloud services, ability to use advanced visualization tools for insights, and a blend of traditional and modern data processing abilities. Each role requires a unique combination of these skills to meet industry demands and maximize salary potential. 🌟📋

Data Source: datanerd.tech