HR Data Analyst

Objective: The goal of this task is to introduce you to the fundamental steps of HR data collection and cleaning. You are required to work with publicly available HR-related data or simulate a realistic HR dataset. You will learn how to import, clean, and validate data, ensuring that the dataset is structured and ready for subsequent analysis.

Expected Deliverables:

• An exported file (e.g., CSV, Excel, or Jupyter Notebook) that contains your cleaned dataset along with documented scripts or formulas showing the cleaning process.
• A short report (in PDF or Word format) documenting your methodology, including how you identified and handled missing or incorrect data, and steps taken during the cleaning process.

Key Steps:

1. Identify and obtain HR-related data from publicly available sources or create a simulated dataset containing employee records, performance metrics, and other HR dimensions.
2. Perform data cleaning procedures such as handling missing values, removing duplicates, correcting data types, and standardizing formats.
3. Document every step you perform and include commented code (if using Python, R, or similar tools) explaining your approach.
4. Validate the clean dataset by summarizing key statistics and ensuring data consistency across different attributes.

Evaluation Criteria:

• Clarity and thoroughness of the data cleaning documentation.
• Correctness and completeness of the cleaned dataset.
• Quality and reproducibility of the code or methods used.
• Overall presentation and organization of the deliverables.

This task is designed to take approximately 30-35 hours as you explore real-world HR data preparation challenges. Thorough documentation and clear explanation of each step will be crucial for successful completion.

Objective: This task focuses on applying statistical techniques to explore HR data. You will analyze the dataset prepared in Week 1 (or another relevant dataset) to identify key patterns, trends, and anomalies. The aim is to strengthen your ability to conduct descriptive and inferential statistical analysis and draw actionable insights to support HR decision-making.

Expected Deliverables:

• A report file (PDF, Word, or Jupyter Notebook) including your analysis findings, scripts, and visualizations (charts, graphs, and tables).
• An exported analysis file (such as an Excel file or Notebook) containing raw statistical outputs and analyzed data summaries.

Key Steps:

1. Conduct descriptive statistical analysis on your dataset focusing on measures of central tendency, variability, and distribution.
2. Use appropriate visual tools (such as histograms, box plots, or scatter plots) to depict data trends.
3. Apply basic inferential statistics to test hypotheses or identify relationships, such as correlation analysis between employee performance and other HR metrics.
4. Document each step of your analysis, including the rationale for your choice of statistical methods and interpretation of results.

Evaluation Criteria:

• Appropriateness and correctness of applied statistical techniques.
• Clarity and quality of visualizations and graph interpretations.
• Depth of insight in your analysis and conclusions.
• Overall structure, documentation, and reproducibility of the work.

This task is intended to require about 30-35 hours of work, combining both coding and analytical writing. The focus is on demonstrating your analytical skills and the ability to transform raw data into actionable insights for HR management.

Objective: In this week’s task, you will concentrate on converting your statistical findings into compelling visualizations and a comprehensive report. The objective is to practice data storytelling by presenting complex data in a user-friendly and visually appealing format that can be understood by HR professionals with or without a technical background.

Expected Deliverables:

• An interactive or static dashboard file (using softwares like Tableau, Power BI, or Python-based visualization libraries) that showcases the key HR metrics and trends identified in your analysis.
• A detailed report (PDF or Word) explaining your visualizations, the insights derived, and recommendations based on the data.

Key Steps:

1. Select the most relevant charts and graphs from your previous analysis that best represent HR data trends.
2. Design and develop the dashboard or visualization set using a tool of your choice, ensuring clarity and accessibility of information.
3. Write an accompanying report that details the rationale behind your visual design, the choice of metrics, and the conclusions drawn from each visualization.
4. Ensure your visualizations have proper legends, labels, and annotations to aid understanding.

Evaluation Criteria:

• Visual appeal and clarity of the dashboard.
• Depth and clarity of explanations provided in the report.
• Ability to effectively communicate data insights to a non-technical audience.
• Overall coherence and practical usability of the visualizations in an HR decision-making context.

This assignment is designed to take around 30-35 hours and will challenge your ability to blend data analysis with creative visual representation, a key skill in HR analytics.

Objective: The final weekly task is to extend your analytical skills by applying predictive analytics techniques to common HR problems such as attrition, recruitment success, or performance forecasting. In this task, you will develop a simple predictive model based on HR metrics using publicly available data or simulated datasets. The focus is on applying machine learning methods and understanding their practical applications in HR management.

Expected Deliverables:

• A comprehensive report (PDF, Word, or Jupyter Notebook) that includes your predictive modeling process, from data preprocessing to model evaluation.
• The final model file or scripts (Python, R, or similar) along with visualizations such as ROC curves, confusion matrices, or other model evaluation metrics.

Key Steps:

1. Select a relevant HR question such as predicting employee turnover or forecasting performance trends using key HR metrics.
2. Prepare your data by filtering and transforming variables to create inputs suitable for predictive modeling.
3. Choose an appropriate machine learning algorithm (e.g., logistic regression, decision trees) and split your data into training and testing sets.
4. Evaluate your model using appropriate metrics and include visualizations that explain its performance.
5. Document your decisions, model tuning, and validation processes, providing justification for each step taken.

Evaluation Criteria:

• Soundness of the predictive model and the appropriateness of the chosen algorithm.
• Thorough documentation of the entire modeling process including data preparation, model training, and evaluation.
• Effectiveness and clarity of visual aids used to explain model performance.
• Quality and depth of the final report and submitted code/script files.

This task is allocated approximately 30-35 hours of work, offering you a chance to apply analytical and predictive skills that are highly valued in HR Data Analytics. The complete deliverable will reflect your ability to combine data engineering skills with advanced analytics in the HR domain.