HR Analytics Intern

Create and clean a fictional HR dataset containing at least 30 employee records.

You can use Excel, Google Sheets, Python, or another suitable tool.

Include non-sensitive sample fields such as employee ID, department, job role, experience range, employment type, joining month, performance category, and location.

Do not use real employee information.

Intentionally include common data-quality issues such as duplicate records, missing values, inconsistent department names, incorrect capitalization, extra spaces, or inconsistent date formats.

Identify each issue and clean the dataset using appropriate methods.

Prepare a before-and-after summary showing how many records or fields were corrected.

Create a short data dictionary explaining what each column represents and what format should be used.

Document the steps followed during the cleaning process so another person can understand and reproduce your work.

You may use spreadsheet formulas or Python-based data processing if you are comfortable with them.

Submit the original sample dataset, cleaned dataset, data dictionary, and a short data-cleaning report.

The objective is to understand why data quality is important before performing HR analytics.

Perform an employee turnover analysis using a fictional HR dataset.

You may use the cleaned dataset from the previous task or create a new sample dataset containing at least 30 records.

Include fictional employee status information such as active or exited, department, role category, experience range, and joining or exit month.

Calculate simple metrics such as total employees, number of exits, department-wise exits, and an example turnover percentage using a clearly stated calculation method.

Compare at least three departments and identify which department shows the highest and lowest turnover in your sample.

Create two or three simple charts using Excel, Google Sheets, or another suitable tool.

Interpret each chart in plain language and avoid making conclusions beyond what the sample data supports.

Identify two possible areas that an HR team could investigate further, such as workload, career development, communication, or employee engagement.

Do not use real employee records or personal information.

Submit the dataset, calculations, charts, and a one- to two-page analysis report.

Clearly state that the dataset is fictional and intended only for learning purposes.

Study and calculate common HR performance metrics using fictional workforce data.

Select at least five metrics such as employee turnover, absenteeism, training participation, recruitment conversion, time-to-hire, employee engagement score, or retention rate.

For each metric, explain its purpose, basic calculation method, and what an HR team can learn from it.

Create a fictional dataset or use sample values that allow you to calculate the selected metrics.

Prepare a spreadsheet containing the input values, formulas, calculated results, and a short interpretation for each metric.

Make sure the calculation method is clearly documented so another student can reproduce your results.

Create one or two simple charts to visually present the most useful metrics.

Do not use actual employee or company data.

The objective is to understand how HR analytics converts workforce information into measurable indicators.

Submit the spreadsheet and a short report explaining which metrics you consider most useful for monitoring workforce trends and why.

Keep your conclusions limited to the fictional data and clearly mention any assumptions made during the calculations.

Create a basic HR analytics dashboard using a fictional employee dataset.

You may use Excel, Google Sheets, Power BI, or another dashboard tool available to you.

The dashboard should display useful workforce information such as total employees, department distribution, employee status, experience categories, training participation, and one or two additional HR metrics.

Use charts and summary values to make the information easy to understand.

Add clear titles and labels to every visualization.

If your selected tool supports filters or slicers, add at least one useful filter such as department or employment type.

After creating the dashboard, write a short analysis containing at least five observations from the sample data.

Based on those observations, provide two or three practical recommendations for the fictional HR team.

Avoid making unsupported conclusions and clearly mention that the dataset is fictional.

Submit the dashboard file or screenshots along with the underlying dataset and a short insight report.

The purpose of this task is to develop the ability to convert raw HR data into a visual format that can help managers understand workforce trends.