To find the most demanded skills for the top 3 most popular data roles, I filtered out those positions by which ones were the most popular, and got the top 5 skills for these top 3 roles. This query highlights the most popular job titles and their top skills, showing which skills I should pay attention to depending on the role I'm targeting.
View my notebook with detailed steps here: 2_Skills_Count.ipynb
fig, ax = plt.subplots(len(job_titles), 1)
for i, job_title in enumerate(job_titles):
df_plot = df_skills_percent[df_skills_percent['job_title_short'] == job_title].head(5)
sns.barplot(data=df_plot, x='skill_percent', y='job_skills', ax=ax[i], hue='skill_count', palette='dark:b_r')
ax[i].set_title(job_title)
ax[i].set_ylabel('')
ax[i].set_xlabel('')
ax[i].get_legend().remove()
ax[i].set_xlim(0, 78)
plt.show()
- Python is a versatile skill, highly demanded across all three roles, but most prominently for Data Scientists (72%) and Data Engineers (65%).
- SQL is the most requested skill for Data Analysts and Data Scientists, with it in over half the job postings for both roles. For Data Engineers, Python is the most sought after skill, appearing in 68% of job postings.
- Data Engineers require more specialized technical skills (AWS, Azure, Spark) compared to Data Analysts and Data Scientists who are expected to be proficient in more general data management and analysis tools (Excel, Tableau)
CHANGE: To find the most demanded skills for the top 3 most popular data roles, I filtered out those positions by which ones were the most popular, and got the top 5 skills for these top 3 roles. This query highlights the most popular job titles and their top skills, showing which skills I should pay attention to depending on the role I'm targeting.
View my notebook with detailed steps here: 3_Skill_Trend.ipynb
from matplotlib.ticker import PercentFormatter
df_plot = df_DS_US_percent.iloc[:, :5]
sns.lineplot(data=df_plot, dashes=False, legend='full', palette='tab10')
plt.gca().yaxis.set_major_formatter(PercentFormatter)
plt.show()
Bar graph visualizing the trending top skills for data scientists in the US in 2023
- Python continues to dominate the data science landscape, appearing in over 70% of job postings. This consistency highlights its role as the core programming language for analytics, modeling, and machine learning across industries. Its stability suggests that Python is no longer a “preferred” skill — it’s an expected baseline for data scientists.
- SQL remains an essential complement, second only to Python, reflecting how data scientists are still deeply involved in data extraction, cleaning, and querying tasks. The steady demand shows that practical database knowledge remains crucial even in an era of advanced AI tools.
- R’s moderate but declining presence indicates a gradual shift away from traditional statistical environments toward more unified, Python-based ecosystems. However, its continued use suggests niche applications in research, academia, and specialized analytics where R’s statistical libraries excel.
- SAS and Tableau occupy smaller, stable portions of the skill market. SAS maintains relevance in legacy enterprise systems and regulated sectors (like finance or healthcare), while Tableau’s steady demand reflects ongoing emphasis on data visualization and communication — though newer tools (e.g., Power BI) may be influencing its slower growth.
Box plot visualizing the salary distribution for the top 6 data job titles
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There's a significant variation in salary ranges across different job titles. Senior Data Scientist positions tend to have the highest salary potential, with up to $600K, indicating the high value placed on advanced data skills and experience in the industry.
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Senior Data Engineer and Senior Data Scientist roles show a considerable number of outliers on the higher end of the salary spectrum, suggesting that exceptional skills or circumstances can lead to high pay in these roles. In contrast, Data Analyst roles demonstrate more consistency in salary, with fewer outliers.
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The median salaries increase with the seniority and specialization of the roles. Senior roles (Senior Data Scientists, Senior Data Engineer) not only have higher median salaries but also larger differences in typical salaries, reflecting greater variance in compensation as responsibilities increase.
fig, ax = plt.subplots(2, 1)
# Top 10 Highest Paid Skills for Data Scientists
sns.barplot(data=df_DS_top_pay, x='median', y=df_DS_top_pay.index, ax=ax[0], hue='median', palette='dark:b_r')
ax[0].legend().remove()
# Top 10 Most in Demand Skills for Data Scientists
sns.barplot(data=df_DS_skills, x='median', y=df_DS_skills.index, ax=ax[1], hue='median', palette='light:b')
ax[1].legend().remove()
plt.show()
Two separate bar graphs visualizing the highest paid skills and most in demand skills for data scientists in the US.
- High-paying skills like Asana, Airtable, Watson, and Unreal offer salaries exceeding $200K, but are niche or less common in typical data science roles — showing that rarity drives compensation.
- Tools such as Ruby on Rails, RedHat, and Hugging Face suggest a growing value for software engineering and AI-model deployment expertise within data science.
- In contrast, most in-demand skills (Python, SQL, R, SAS, Tableau) align with core data manipulation and analysis — reflecting what employers consistently require.
- These foundational tools are widely used but lower-paid, indicating market saturation: high supply of professionals keeps salaries moderate.
- TensorFlow, Spark, and Hadoop bridge both lists slightly, showing how machine learning and big-data engineering remain lucrative hybrid skill areas.
- Overall, the charts reveal a trade-off between specialization and demand — generalist skills ensure employability, while specialized or emerging technologies command premium pay.