This project explores the data analyst job market using Python as the primary analytical tool. The goal is to uncover:
- 💰 The highest-paying jobs for data analysts
- 🔥 The most in-demand skills across different data roles
- 📈 Skills that trend over time throughout the year
- 💡 The optimal skills to learn — balancing high demand with high pay
By leveraging Python's powerful data analysis libraries including Pandas, Matplotlib, and Seaborn, I transformed raw job posting data into actionable insights. These findings can help guide career development decisions, inform skill-building strategies, and provide a clearer picture of the evolving data analytics landscape.
The inspiration for this project came from a desire to answer:
What skills should I prioritize to maximize both employability and salary potential in data analytics?
To explore this, I focused on:
- The most demanded skills for the top 3 most popular data roles
- Trends of in-demand skills for Data Analysts
- Salary potential of jobs and skills for Data Analysts
- The most optimal skills to learn for Data Analysts
The dataset, provided by Luke Barousse's Python Course, includes job postings with title, company, location, salary, schedule type, posting date, and associated skills.
While the dataset is global, this project focuses exclusively on US positions, enabling a comprehensive analysis of trends, demand, and compensation in the US data analytics landscape using Python’s data manipulation and visualization capabilities.
This project showcased modern Python data analysis tools and libraries:
- Python – the backbone of the analysis, used for data manipulation, analysis, and visualization
- Pandas – essential for data manipulation, cleaning, and analysis
- Matplotlib – used for creating foundational data visualizations
- Seaborn – provided enhanced statistical visualizations
- Jupyter Notebooks – interactive environment for combining code, visualizations, and narrative
- Visual Studio Code – development environment for managing notebooks and scripts
- Git & GitHub – version control and project hosting
- Python 3.11 or higher
- Conda (recommended) or pip
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Clone the repository
git clone https://github.com/NadiaRozman/Python_Data_Analysis_Project.git cd Python_Data_Analysis_Project -
Set up the environment
Option 1: Using Conda (Recommended)
conda env create -f environment.yml conda activate python_data_analysis
Option 2: Using pip
pip install -r requirements.txt
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Launch Jupyter Notebook
jupyter notebook
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Open and run the notebooks in the
notebooks/directory in the following order:1_EDA_Data_Science_Jobs.ipynb2_Skills_Demand.ipynb3_Skills_Trend.ipynb4_Salary_Analysis.ipynb5_Optimal_Skills.ipynb
Each Jupyter notebook in this project tackled specific questions about the data analyst job market:
To identify the most in-demand skills for the top three data roles, I filtered job postings for Data Analysts, Data Engineers, and Data Scientists, then calculated the likelihood of each skill appearing in job postings.
View my notebook with detailed steps here: 2_Skills_Demand.ipynb
Key Insights:
Bar graph visualizing the likelihood of skills being requested in US job postings for Data Analysts, Data Engineers, and Data Scientists.
All analyses and visualizations are based on the US-focused subset of the global dataset.
- SQL is the most fundamental skill across all three roles, appearing in 48% of Data Analyst jobs, 66% of Data Engineer jobs, and 48% of Data Scientist jobs.
- Python is highly versatile and critical, especially for Data Scientists (70%) and Data Engineers (64%), while appearing in 28% of Data Analyst positions.
- Data Engineers require more specialized technical skills like AWS (41%), Azure (35%), and Spark (31%), reflecting the infrastructure-focused nature of the role.
- Data Analysts rely heavily on traditional tools: Excel (39%) and Tableau (26%) are core requirements alongside SQL.
- Data Scientists show strong demand for programming (Python 70%, SQL 48%, R 40%) and some analytics tools (SAS 22%, Tableau 20%).
To understand how skill demand evolves throughout the year, I filtered the dataset for data analyst positions and examined monthly trends for the top five skills.
View my notebook with detailed steps here: 3_Skills_Trend.ipynb
Key Insights:
Line graph visualizing the trending top skills for data analysts in the US throughout 2024.
All analyses and visualizations are based on the US-focused subset of the global dataset.
- SQL remains the most consistently demanded skill throughout the year, peaking around August at nearly 70%, though it shows a gradual decline toward year-end, settling around 53%.
- Excel shows the most dramatic growth, starting at 30% in January and surging to 48% by October, then dropping to 34% in November before rising to 42% in December.
- Python maintains relatively stable demand between 28-42%, with a notable spike in September (42%) before declining to 28% by December.
- Tableau demonstrates consistent demand around 23-31% throughout most of the year, with slight fluctuations and ending at 23% in December.
- Power BI shows the most stable trend line, hovering between 18-30%, with a gentle upward trajectory and slight dip toward year-end at 20%.
The data suggests seasonal variations in skill demand, with SQL and Excel showing the most volatility, while visualization tools (Tableau and Power BI) maintain more consistent demand patterns.
To understand salary distributions and identify which skills command the highest pay, I analyzed median salaries across different data job titles and examined compensation for specific skills.
View my notebook with detailed steps here: 4_Salary_Analysis.ipynb
Key Insights:
Box plot visualizing the salary distributions for the top 6 data job titles.
All analyses and visualizations are based on the US-focused subset of the global dataset.
- Senior Data Scientist and Senior Data Engineer roles command the highest median salaries, with ranges extending beyond $200K, showcasing the premium placed on seniority and specialized technical expertise.
- Data Scientist positions show wide salary variance with many outliers reaching $250K+, indicating significant pay differences based on experience, company, and specialization.
- Senior Data Analyst positions show a more concentrated salary range around $100K-$150K with fewer extreme outliers compared to engineering and science roles.
- Data Analyst entry-level positions have the most concentrated salary distribution, primarily clustering between $75K-$125K, with some outliers extending to $175K.
- All roles show positive skew with outliers on the higher end, suggesting top performers and specialized positions can command significantly higher compensation.
Two bar graphs: Top graph shows the highest paid skills; bottom graph shows the most in-demand skills for data analysts.
All analyses and visualizations are based on the US-focused subset of the global dataset.
Highest Paid Skills:
- Specialized and niche tools dominate the top-paying skills: Unreal ($230K+), Blazor, Fortran, MariaDB, and NoSQL all command salaries around $160K-$180K.
- Skills like Zoom, GraphQL, Yarn, and Atlassian offer strong compensation ($140K-$155K), reflecting the value of collaboration tools and modern development practices.
- Even Watson (IBM's AI platform) appears in the top 10, indicating AI/ML skills command premium pay.
Most In-Demand Skills:
- Python, AWS, and SQL lead demand, with Python and AWS slightly ahead, each appearing in roughly equal proportions of job postings (~$100K median salary).
- Tableau remains highly demanded among analysts, positioned similarly to SQL and R in frequency.
- Power BI, SAS, and Excel round out the core toolkit, all showing demand levels around $85K-$95K median salary.
- Surprisingly, PowerPoint and Word still appear in demand lists, though at lower salary levels (~$77K), reflecting communication skill requirements.
To identify the most strategic skills to learn, I combined demand data with salary information, looking for skills that offer both strong job prospects and competitive compensation.
View my notebook with detailed steps here: 5_Optimal_Skills.ipynb
Key Insights:
Scatter plot visualizing the most optimal skills (high-paying and in-demand) for data analysts in the US.
All analyses and visualizations are based on the US-focused subset of the global dataset.
- SQL and Python are the highest-value skills, appearing in approximately 50% and 32% of job postings respectively, while offering strong salaries around $97K-$100K. These represent the optimal combination of ubiquity and compensation.
- Tableau strikes an excellent balance, appearing in about 28% of jobs with a competitive salary around $98K, making it a highly strategic visualization skill to master.
- R offers strong salary potential (~$97K) while appearing in roughly 18% of positions, positioning it as a valuable complementary skill to Python.
- Power BI balances moderate demand (
18% of jobs) with solid compensation ($93K), making it a practical alternative or complement to Tableau. - Excel, despite appearing in approximately 50% of positions (tied with SQL), offers lower compensation (~$83K), suggesting it's a necessary baseline skill but doesn't drive premium salaries.
- SAS shows moderate demand (
15%) with respectable pay ($92K), indicating value for analysts in specific industries (finance, healthcare, pharmaceuticals). - Word and PowerPoint represent baseline communication skills with minimal salary impact (~$77K), appearing in <10% of positions—necessary but not differentiating.
Scatter plot with color coding distinguishing between programming skills (orange) and analyst tools (blue).
All analyses and visualizations are based on the US-focused subset of the global dataset.
When we separate programming skills (orange) from analyst tools (blue), a clear pattern emerges:
- Programming skills (Python, SQL, R, SAS) cluster in the higher salary range ($92K-$100K) and show varying demand levels, with SQL and Python being most prevalent.
- Analyst tools (Tableau, Power BI, Excel, PowerPoint, Word) span a wider salary range ($77K-$98K), with Tableau leading the pack in both demand and compensation among tools.
- The most optimal career strategy combines foundational programming (SQL + Python) with strong visualization capabilities (Tableau or Power BI).
- Excel remains ubiquitous but should be viewed as a baseline requirement rather than a salary differentiator.
Through this project, I significantly enhanced both my technical Python skills and analytical thinking:
- 🐍 Advanced Python Programming – mastered Pandas for complex data manipulation, filtering, and transformation operations.
- 📊 Data Visualization – created publication-quality visualizations using Matplotlib and Seaborn with custom styling and color schemes.
- 🔍 Analytical Thinking – developed the ability to formulate business questions and translate them into data analysis workflows.
- 🧹 Data Cleaning – learned techniques for handling missing data, inconsistent formats, and filtering datasets for specific analyses.
- 💻 Jupyter Notebooks – became proficient in creating clear, reproducible data analysis workflows with markdown documentation.
- 📈 Statistical Analysis – applied statistical concepts like percentiles, medians, and distributions to understand salary data.
- 🔄 Version Control – used Git and GitHub effectively to manage project iterations and showcase my portfolio.
Here's a summary of what the analysis revealed:
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SQL is non-negotiable – appearing in nearly 50% of Data Analyst positions and across all data roles, it's the universal foundation skill.
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Programming separates good from great – Python and R command higher salaries ($97K-$100K) compared to tool-only skillsets.
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Skill demand is seasonal – SQL peaks mid-year while Excel surges in Q4, suggesting strategic timing for skill development and job searching.
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Specialization drives premium pay – niche skills like Unreal, Blazor, and MariaDB command $160K+ salaries, though at lower demand.
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Visualization is table stakes – Tableau and Power BI appear in 18-28% of jobs, making them essential for modern analysts.
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Senior roles pay significantly more – Senior Data Scientists and Engineers command $150K-$200K+ compared to $75K-$125K for entry-level analysts.
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The optimal path balances demand and pay – Focus on SQL + Python + Tableau/Power BI for the best combination of job opportunities and compensation.
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Cloud skills are emerging – AWS appearing in top-demand lists signals the growing importance of cloud platforms in analytics.
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Communication tools have limited impact – While Word and PowerPoint appear in job postings, they don't drive salary differentiation.
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Wide variance exists within roles – Data Scientist salaries range from $100K to $300K+, emphasizing the importance of specialization and experience.
This Python data analysis project revealed the dynamic nature of the data analyst job market and the critical skills needed to succeed. The analysis confirmed that while SQL forms the absolute foundation, advancing into programming (Python) and visualization tools (Tableau) offers the greatest balance of employability and compensation.
📌 Foundation Skills (Start Here):
- SQL – appears in 50% of positions, ~$97K median salary
- Excel – appears in 50% of positions but focus on it as baseline, not differentiator
📌 Differentiating Skills (Build These Next):
- Python – 32% of jobs, ~$100K salary, highest versatility
- Tableau or Power BI – 18-28% of jobs, $93K-$98K salary
- R – 18% of jobs, ~$97K salary, strong for statistical analysis
📌 Advanced Specialization (For Premium Roles):
- Cloud platforms (AWS) – growing demand
- Statistical tools (SAS) – industry-specific value
- Niche technical skills – command premium but lower demand
📌 Career Progression Path:
- Entry-level: Master SQL + Excel + one visualization tool → $75K-$100K
- Mid-level: Add Python + advanced visualization → $90K-$120K
- Senior-level: Specialize in programming + cloud/big data → $120K-$200K+
- Time your learning – Consider seasonal trends when planning skill development
- Stack complementary skills – SQL + Python + Tableau is more valuable than any single skill
- Don't chase niche skills early – Focus on high-demand foundational skills first
- Invest in programming – Python and R consistently command higher salaries
- Build a portfolio – Projects like this demonstrate practical application of skills
This project demonstrates how Python's powerful data analysis ecosystem can transform raw data into career-guiding insights. The skills I developed here—from data manipulation to visualization to analytical thinking—are directly applicable to real-world data analyst roles.
📂 Project Structure
Python_Data_Analysis_Project/
│
├── images/
│ ├── 1_Likelihood_of_Skills_Requested_in_US_Job_Postings.png
│ ├── 2_Trending_Top_Skills_for_Data_Analysts_in_the_US.png
│ ├── 3_Salary_Distributions_of_Data_Jobs_in_the_US.png
│ ├── 4_Highest_Paid_and_Most_In_Demand_Skills_for_Data_Analysts_in_the_US.png
│ ├── 5_Most_Optimal_Skills_for_Data_Analysts.png
│ └── 6_Most_Optimal_Skills_for_Data_Analysts_with_Tech.png
│
├── notebooks/
│ ├── 1_EDA_Data_Science_Jobs.ipynb
| ├── 2_Skills_Demand.ipynb
│ ├── 3_Skills_Trend.ipynb
│ ├── 4_Salary_Analysis.ipynb
│ └── 5_Optimal_Skills.ipynb
│
├── requirements.txt
├── environment.yml
└── README.md
🔗 Connect with me
- GitHub: @NadiaRozman
- LinkedIn: Nadia Rozman
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