B.Sc. (Data Science) Human Resource Analytics Practical Syllabus - Mumbai University 2026

This is the Fourth Year BSc Data Science Honours syllabus under NEP 2020, phased in one year at a time, from the academic year 2024-25. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.

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Syllabus for Human Resource Analytics Practical

Major elective · Semester 8 · Fourth Year BSc Data Science Honours · 2 credits · 50 marks

Module I

1. Analyze employee turnover rates and identify factors contributing to attrition
Collect historical employee data, including tenure, performance ratings, salary, and job satisfaction.
Calculate employee turnover rates for different departments and job roles.
Conduct statistical analysis to identify correlations between turnover and variables such as salary, job satisfaction, and performance.
Generate visualizations (e.g., charts, graphs) to present the findings and propose recommendations to reduce turnover. 2. Develop a user-friendly HRIS dashboard for monitoring and analyzing HR metrics
Identify key HR metrics to be displayed on the dashboard (e.g., headcount, recruitment pipeline, training hours).
Design the layout and interface of the HRIS dashboard using appropriate programming languages and tools.
Integrate data from various HR systems and databases to populate the dashboard in real-time.
Implement interactive features, such as drill-down capabilities and data filters, to facilitate data exploration and analysis 3. Analyze training effectiveness and identify skill gaps in the organization
Collect training data, including participant demographics, training modules, pre/post-assessment scores, and performance metrics.
Perform statistical analysis to evaluate the impact of training on employee performance.
Identify areas of improvement and recommend targeted training programs based on identified skill gaps.
Develop a visualization or report summarizing the training needs analysis results. 4. Develop an HR scorecard to measure HR effectiveness and align HR strategies with organizational goals
Identify key HR performance indicators aligned with the organization's strategic objectives.
Collect relevant data for each HR indicator, such as employee satisfaction surveys, training investment data, and performance metrics.
Calculate HR metrics and indicators, such as turnover rate, training ROI, and employee engagement index.
Design a dashboard or report to present the HR scorecard and analyze trends over time. 5. Use predictive analytics to forecast employee attrition and develop retention strategies
Gather historical HR data, including employee demographics, performance metrics, compensation, and employee exit data.
Build a predictive model (e.g., logistic regression, decision tree) to predict employee attrition.
Validate the model's accuracy and evaluate its performance using appropriate evaluation metrics.
Generate actionable insights and recommendations to proactively address potential attrition risks.

Module II

1. Use predictive analytics to forecast employee attrition and develop retention strategies
Gather historical HR data, including employee demographics, performance metrics, compensation, and employee exit data.
Build a predictive model (e.g., logistic regression, decision tree) to predict employee attrition.
Validate the model's accuracy and evaluate its performance using appropriate evaluation metrics.
Generate actionable insights and recommendations to proactively address potential attrition risks. 2. Measure and analyze employee engagement levels within the organization
Collect employee engagement survey data, including responses to survey questions related to job satisfaction, work environment, and organizational culture.
Calculate engagement scores and identify key drivers of engagement.
Conduct a sentiment analysis on employee feedback to understand areas of improvement.
Present the findings and propose strategies to enhance employee engagement based on the analysis. 3. Develop a program to automate repetitive HR processes, such as leave management or performance appraisal
Identify the HR process to be automated and define the required functionalities.
Design and implement a web-based application or script to streamline the process using appropriate programming languages and frameworks.
Integrate the application with relevant HR systems and databases to ensure data accuracy and consistency.
Test and validate the automated process, considering different scenarios and user inputs. 4. Analyze the effectiveness of the organization's performance management system and provide insights for improvement.
Collect performance evaluation data, including performance ratings, goal achievement metrics, and feedback.
Analyze the distribution of performance ratings across different departments or job roles.
Identify trends and patterns in performance data and assess the fairness and consistency of the evaluation process.
Propose recommendations for enhancing the performance management system based on the analysis results. 5. Analyze the organization's compensation structure and compare it to industry benchmarks.
Gather salary data for different job roles and levels within the organization.
Perform a salary analysis, including measures like average salary, salary distribution, and salary competitiveness.
Conduct benchmarking by comparing the organization's salary data with industry standards or competitor data. 10 Text Books 1. Ulrich, D. & Brockbank, W., The HR Value Proposition. Harvard Business School Press 2016 2. How to measure HRM by Jac Fitz-enz 2002 11 Reference Books 1. Predictive Analytics for Human Resources by Jac Fitz-enz, John Mattox II, Wiley 2014 2. Making Human Capital Analytics Work: Measuring the ROI of Human Capital Processes and Outcomes. By by Jack Phillips,Patricia Pulliam Phillips- 2014 12 Semester End Examination: 100%

Text Books

1 Ulrich, D. & Brockbank, W., The HR Value Proposition. Harvard Business School Press 2016
2 How to measure HRM by Jac Fitz-enz 2002
1 Predictive Analytics for Human Resources by Jac Fitz-enz, John Mattox II, Wiley 2014
2 Making Human Capital Analytics Work: Measuring the ROI of Human Capital Processes and Outcomes. By by Jack Phillips,Patricia Pulliam Phillips- 2014

Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 2024 25 DS SEM I & II NEP NEP 2020 syllabus, in force from 2024-25 Read full PDF Read

PDF 2023 24 BSc Data Science Sem V & VI Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read

PDF 2021 22 BSc Data Science Sem III & IV Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read

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