Dashboard Link : PowerBI DS Survey Breakdown.pdf
With over 600 survey responses from data professionals, this report aims to provide comprehensive insights into the current landscape of data careers. Key objectives include understanding job roles, analyzing career paths and salary ranges, identifying popular programming languages and tools, evaluating job satisfaction levels across various aspects, uncovering patterns in satisfaction by demographics, exploring career priorities and preferences, and assessing the impact of education and diversity on career progression. By leveraging this extensive dataset, the report will enable informed decision-making for organizations and individuals navigating the rapidly evolving data workforce.
Step 1: Data Collection - Gathered over 600 survey responses from data professionals across roles, industries, and demographics.
Step 2: Data Preparation - Imported data into Power BI, cleaned data, standardized formats, created calculated columns/measures.
Step 3: Page Organization - Organized report into multiple pages for different aspects like job roles, career paths, etc.
Step 4: Visualization Creation - Created relevant visualizations (bar charts, pie charts, line charts, etc.) for each page based on data type.
Step 5: Data Modeling - Established relationships between data tables for comprehensive analyses and cross-filtering.
Step 6: Formatting and Labeling - Ensured proper formatting, labeling, and styling of visualizations for clarity and aesthetics.
Step 7: Interactivity and Filtering - Implemented slicers, filters, and bookmarks for data exploration and drill-down capabilities.
Step 8: Cross-Page Interactions - Linked slicers, filters, and bookmarks across pages for seamless user experience.
Step 9: Insights and Storytelling - Interpreted visualizations and identified key insights, trends, and patterns related to data professionals.
Step 10: Report Formatting - Organized pages and visualizations in a logical and visually appealing layout.
Step 11: Deployment and Sharing - Published final report to Power BI service or shared with stakeholders for distribution/collaboration.
Step 12: Continuous Improvement - Gathered feedback, updated and enhanced report based on new requirements or data sources. In our dataset,
Some parameters were assigned value others, representing those had open text option for the respondent to fill any other option, which wasn;t listed..
All these values have been ignored while calculating and has been replaced by 'Others' for the sake of simplicity
A card visual was used to represent count of customers.
Snap of % of respondent who find it difficult to break into the data field
A gauge visual was used to represent satisfaction.
A three page report was created on Power BI Desktop.
Following inferences can be drawn from the dashboard;
Average age group of Survey takers = 29
Programming language of choice = Python/R
Top countries in the survey = United States of America, India
Top Industry of the survey takers = Tech
1.1) 41% survey takers, switch there jobs and came into data field
1.2) 24.76% respondent, think that it was difficult to break into the field
2.1) Top 3 earning data professions are : Data Scientist, Data Engineer and Data Architect
2.2) Data professionals with PhDs or Master's degrees commanded salaries approximately twice as high as those with Bachelor's or lower educational qualifications.
3.1) 90% of the survey takers believe that Python and R are most most in demand skills for the data community
3.2) 38% of the data professionals are placed in Tech and Finance industry
4.1) Respondents have a neutral take on job satisfaction aspects like: Management, coworkers, upward movement and learning opportunity
4.2) Salary ability to remote work and Work life balance are the most crucial aspects for the job lookers
4.3) Salary aspect has the least satisfaction score with an average of 4.7
4.4) People in there mid 20's or early 40's are more satified with there current jobs.