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 higlights 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.py
fig, ax = plt.subplots(len(job_titles),1)
for i, job_title in enumerate(job_titles):
df_plot = df_skills_perc[df_skills_perc['job_title_short'] == job_title].head(5)[::-1]
sns.barplot(data=df_plot, x='skill_percent', y='job_skills', ax=ax[i], hue='skill_count', palette='dark:b_r')
plt.show()
The chart presents an overview of the most requested skills in US job postings for three key data-related roles: Data Analyst, Data Engineer, and Data Scientist. The insights below summarize the key findings:
- SQL is the most in-demand skill for Data Analysts, appearing in 51% of job postings. This underscores the critical importance of SQL for data querying and manipulation in this role.
- Excel follows with a 41% demand, highlighting its continued relevance for data analysis, reporting, and visualization tasks.
- Tableau and Python are also significant, being requested in 28% and 27% of postings, respectively. This indicates the need for proficiency in both data visualization and programming/scripting.
- SAS is less in demand, with 19% of job postings requiring it, which might suggest a trend towards open-source tools over traditional proprietary software.
- SQL and Python dominate the skill set for Data Engineers, with 68% and 65% of job postings respectively. This reflects the necessity of these skills for managing and transforming large datasets.
- AWS is also highly sought after, appearing in 43% of postings, highlighting the growing importance of cloud infrastructure and services in data engineering.
- Azure and Spark are equally demanded, with each appearing in 32% of postings. This indicates the need for proficiency in various cloud platforms and big data processing frameworks.
- Python is the most critical skill for Data Scientists, required in 72% of job postings, emphasizing its essential role in data analysis, machine learning, and statistical modeling.
- SQL follows at 51%, showing its continued importance for database management and data extraction tasks in this role.
- R is requested in 44% of job postings, indicating its significance in statistical computing and graphics.
- SAS and Tableau are each demanded in 24% of postings, pointing to a need for both statistical software and data visualization skills, albeit to a lesser extent compared to Python and SQL.
- SQL is consistently in high demand across all three roles, confirming its status as a fundamental skill in the data industry.
- Python is increasingly important, particularly for Data Scientists and Data Engineers, reflecting the language's versatility and dominance in data-related tasks.
- Cloud platforms (AWS and Azure) and big data frameworks (Spark) are crucial for Data Engineers, showing the industry's shift towards scalable and distributed data processing.
- R and SAS remain relevant but are less dominant compared to open-source alternatives like Python.
These insights can guide individuals seeking to enter or advance in these data-related roles, highlighting the most valuable skills to develop based on current job market demands.
from matplotlib.ticker import PercentFormatter
df_plot = df_DA_US_percent.iloc[:, :5]
sns.lineplot(data=df_plot,dashes=False,legend='full', palette='tab10')
plt.gca().yaxis.set_major_formatter(PercentFormatter(decimals=0))
plt.show()Trending Top Skills for Data Analyst in the USBar graph visualizing the trending top skills for data analysts in the US 2023.
SQL: Consistently the most requested skill, though it saw a slight decline from 60% to around 50% by year-end. Excel: Stable demand early in the year (~45%) with a dip mid-year, but recovering to 40% in December. Python: Steady growth, rising from ~30% in mid-year to 35% by December, reflecting its increasing importance. Tableau: Consistent demand (~30%), highlighting its ongoing relevance in data visualization. Power BI: Less demanded (~20%), but its consistent presence suggests growing importance, especially in Microsoft ecosystems. Trends: Mid-Year Dip: General decline in skill demand from June to September, with SQL and Excel particularly affected. Year-End Recovery: Excel and Python saw increased demand towards the end of the year, indicating seasonal or renewed interest.