EDA and Visualizations on Data Science Salaries using Python

Introduction:

In the ever-evolving field of data science, understanding salary trends and industry insights is crucial for both aspiring and experienced professionals. In this article, I will take you through my first Exploratory Data Analysis (EDA) and visualization project using Python, where I explored data science salaries in 2023. By analyzing the dataset and creating compelling visualizations, I gained valuable insights into the salary landscape and identified key trends shaping the industry.

Libraries used:

Pandas, NumPy, Matplotlib, Seaborn

Exploratory Data Analysis (EDA):

The first step in my analysis was to gain a broad overview of the dataset. I examined the distribution of salaries across different experience levels, employment types, Remote Work Ratio, Job Titles and their corresponding Salary in US Dollars.

1. Used the info() and describe() function to get an overview of the dataset, including the column names, data types, and the number of non-null values.
2. Removed duplicates using drop_duplicates() function to remove any duplicate records from the dataset.
3. To explore the uniqueness and distribution of values in categorical variables, I used the nunique() function.
4. Applied transformation steps to ensure consistent and meaningful representation of certain variables in the dataset using replace() function.

Top 10 Data Science Job Titles in 2023

In the analysis of job titles in 2023, I focused on identifying the top 10 job titles in work year 2023 within the dataset. According to the analysis, the most prevalent job title is "Data Engineer," followed closely by "Data Scientist" in the second position and "Data Analyst" holds the third position. I used seaborn barplot to visualize the dataset and gain insights into the job titles in 2023.

Experience Level in 2023

To analyze the experience levels in 2023, I used Seaborn countplot to examine the distribution of different experience levels. Upon analysis, it was observed that the 'Senior Level' experience had the highest count, indicating a significant presence of experienced professionals in the job market during 2023.

Employment Type Analysis:

It's worth noting that a significant number of individuals in the dataset are employed full-time, with a majority of them holding senior positions. This indicates a strong presence of experienced professionals in the job market. Moreover, the analysis suggests a decline in the prevalence of freelancing among data science professionals in 2023 and majority of the part-time workers are entry-level candidates. This suggests that part-time positions may serve as a starting point for individuals entering the data science field.

Top 15 Average Salaries by Job Titles

In terms of average salaries across different job titles, the analysis reveals that the highest-paying roles in the dataset is "Data Science Tech Lead" and confirms our expectation that individuals employed at the executive level tend to have higher average salaries.

The trend of cloud computing has greatly influenced the demand and compensation for cloud data architects. This demand for specialized skills and knowledge in cloud computing contributes to the higher salaries observed for "Cloud Data Architect", making it the second highest paid profession in the dataset.

Average Salary by Employment Type

In 2023, a notable trend can be observed in average salaries based on employment types. Full-time employees tend to have higher average salaries, with freelancers being the second closest in terms of compensation. On the other hand, contract positions, which experienced a surge in popularity during the pandemic in 2021, have gradually declined and are now associated with lower average salaries.

The higher average salaries for full-time employees can be attributed to factors such as job stability, benefits, and the commitment of full-time positions. Employers often offer more competitive compensation packages to attract and retain top talent for these roles.

Freelancers, while not enjoying the same benefits and job security as full-time employees, often have the flexibility to choose their projects and negotiate their rates. This flexibility can lead to relatively higher compensation compared to other employment types.

In contrast, the decline in average salaries for contract positions suggests a shift in the job market dynamics. The increased availability of full-time roles and the stabilization of the job market post-pandemic might have reduced the demand and bargaining power for contract positions, resulting in comparatively lower compensation.

Salary Trend over the Years

The average salary for data-driven jobs has been consistently increasing over the years, indicating a growing demand for skilled professionals in this field. This trend is particularly evident with a significant jump observed between 2021 and 2022. This trend presents promising career prospects for individuals in data-driven careers.

Remote Job Ratio by Work Year

The analysis indicates that in 2023, the on-site working model surpassed remote work in popularity. This suggests a shift back towards more in-person work arrangements. As the pandemic situation improved and vaccination efforts progressed, organizations may have gradually transitioned back to on-site work environments.

Conclusion:

In conclusion, our analysis of data science salaries in 2023 revealed several key trends:

1. Job Titles: The top job titles in 2023 were data engineer, data scientist, and data analyst, indicating the continued demand for professionals in these roles.
2. Experience Levels: Senior-level positions had the highest count among data-driven professionals, highlighting the importance of experience in the field.
3. Employment Types: Full-time employees had higher average salaries compared to freelancers and contract positions, indicating the value placed on job stability and benefits.
4. Remote Work: Remote work gained popularity during the pandemic but was surpassed by on-site work in 2023, suggesting a shift back to traditional work environments.
5. Salary Trends: Average salaries for data-driven jobs showed a consistent increase over the years, reflecting the growing demand for skilled professionals in this field.

A special shoutout goes to Evren Ozkip for being an incredible source of inspiration. Their expertise and code examples served as a guiding light, that have allowed me to gain hands-on experience and strengthen my Python skills.

Link to GitHub Repository: