Data Professional Survey Breakdown

Step 1: Data Preparation

First, we need to download the survey report dataset from GitHub and import it into Power BI. The dataset is provided in an excel (.xlsx) format. After importing the dataset, we need to clean and transform the data to make it suitable for analysis. We opened the dataset in the Power Query and performed tasks like removing empty columns, creating calculated columns, cleaning the columns data to make it more meaningful and removing the manually added options in the “Other” field in all columns where it was found.

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Step 2: Data Visualization

Once the data is prepared, we can start creating visualizations in Power BI. We can add different types of charts and graphs to explore the data. For example,

• Cards to showcase the total number of respondents and their average age.

• Gauge charts to show the average of happiness levels of the participants with respect to their work/life balance and current salary.

• A bar chart to show the average salary range of survey takers as per their current job title.

• A stacked bar chart to show the distribution of respondents by their favorite programming language.

• A pie chart to show the distribution of respondents by the difficulty faced to break into the field of data.

• A tree map to show the distribution of participants by country.

We can also create interactive visualizations like filters and slicers to allow users to explore the data in different ways. Note that, the visualizations are interactive and we can click on each of the components to dive deeper into the relationships between different visuals.

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Step 3: Insights and Analysis

Now that we have created visualizations, we can start analyzing the data and gaining insights. Some interesting insights that can be gained from this survey report dataset include:

• There were a total of 630 respondents and majority of them are in the 25-34 age group.

• The most common job roles reported are data analyst, data scientist, and business analyst but the highest salary is enjoyed by a data scientist.

• The majority of respondents earn between $50,000 and $100,000 per year, with data scientists and machine learning engineers earning the highest salaries.

• About 43% of the respondents found it neither easy nor difficult to break into the data field.

• The most favorite programming language is Python and it is most popular among the data analysts.

• The maximum participants belonged to the U.S. followed by India.

 

Step 4: Report Sharing

Finally, we can share our Power BI report with others to allow them to explore and analyze the data. We can publish the report to the Power BI service or export it to a PDF or PowerPoint file.

In conclusion, the Power BI report we created using the survey report dataset provides valuable insights into the demographics, job roles, industries, salaries, and data tools and technologies used by data professionals. The report allows users to explore the data in an interactive and informative way, making it an excellent tool for organizations and individuals in the data industry.