Certificate Programme in Human Resource (HR) Analytics 
Last date for registration: TBD
End Date : TBD
The Programme will be delivered in Hybrid mode.
Hybrid delivery implies that a part of the cohort is physically present in the classroom while the remaining students participate in the programme virtually. Keeping in mind the circumstances that may prevail from time to time, IIMB reserves the right to modify the in-person mode of delivery to Live, Online mode as the need arises.
Overview
Data without direction may result in misguided insights. Understanding the problems and nuances of Human Resource (HR) functions can help obtain actionable data, develop relevant, tightly knitted value-adding insights that extend beyond introductory data analytics understanding, and solve organizations’ people-related issues. This course is designed to help the participant understand workforce analytics’ application to the HR function’s various domains. The program also explores emerging technology trends in HR, including Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP), their implications for the changing workforce, and the HR function.
This course on HR analytics is targeted for HR practitioners seeking to develop hands-on insights from real data available in HR, HR leaders and functional heads who seek to appreciate the possibilities of utilizing data in their organizations for predictive and prescriptive strategic recommendations related to talent management. It is also relevant for data analytics experts to understand HR function’s nuances to help sharpen their insights. The course uses individual, group, and organizational level data across HR functions to understand what impacts people’s behaviour in the workplace.
Pedagogy
The course’s pedagogy will primarily be a mix of exercises, activities, readings, cases, experiential content, and discussions. The course will require active involvement and integrating data and domain relevant contexts to derive meaningful value from the emerging data insights. Prior preparation, active engagement, and sharing are crucial to learning in this course. Participants will also take up an Action Learning Projects with Live data set from their organizations.
Mode of delivery for this programme will be a mix of Live, Online sessions and asynchronous (self-paced) Online MOOCs.
Key Benefits/Takeaways
HR functions and organizations are sitting on goldmines of data but lacking the ability to translate into insights that add value to the organization.
This program helps participants build predictive HR models to solve people function issues, reduce risk, uncover new opportunities, increase their profitability and organizations’ competitive advantage.
Understanding of various statistical models used in predictive and prescriptive analytics and their applications of various HR and industry contexts.
Who Should Attend
HR practitioners, HR leaders and functional heads who seek to utilize the power of data to solve people function issues, reduce risk, uncover new opportunities, increase their profitability and organizations’ competitive advantage.
Middle to Senior level HR leaders
Programme Content
Module 1 - Introduction to HR Analytics
Future of work and application of analytics in HR. Deriving value from data and examples and use cases of analytics in HR.
Module 2- Foundations of Data Science
Data Science along with Artificial Intelligence (AI) and its various components such as Statistical Learning (SL), Machine Learning (ML) and Deep Learning Algorithms (DL) are recognized as main drivers of organizational value creation.
In this module we will start with basic concepts in probability such as joint and conditional probabilities. After covering basic probability concepts, we move on to random variables, discrete and continuous probability distributions, sampling, estimation and central limit theorem. We will discuss various hypothesis tests and how they are used in feature selection.
Participants will also complete an asynchronous (self-paced) Online MOOC - Foundations of Data Science in this module.
Module Content
- Introduction to Probabaility: Frequency approach to probability estimate, marginal, joint and conditional probabilities, Bayes’ Theorem.
- Random Variables: Discrete and Continuous random variables, Discrete distributions: Binomial and Poisson distribution, Continuous distributions: Exponential and Normal.
- Sampling and Estimation: Sampling procedures such as random, stratified and cluster sampling, Estimation of distribution parameters, Maximum likelihood estimate, Sampling distribution and Central Limit Theorem (CLT).
- Hypothesis Testing: Parametric and non-parametric testing, one sample tests: Z and T-test, Hypothesis tests for proportion, Two-Sample tests, Non-parametric tests: Chi-Square goodness of fit test and Chi- Square test of Independence.
Module 3 - Story Telling Through Data
An important component of analytics is the ability of the data scientist to effectively communicate the same to the top management and other stakeholders. Data visualization is an important skill that every data scientist should posses which will assist them to communicate the insights effectively.
Participants will also complete an asynchronous (self-paced) Online MOOC - Story Telling Through Data in this module.
Module Content
- Visualization Introduction: Seven stages of data visualization, Edward Tufte’s visual encoding, Effectiveness of Visual Encoding.
- Visualization of Numerical Data: Single measure variability, multiple measure variability; visualizing relationships; Data on Maps.
- Visualization of Text Data.
- Story telling framework: Linear and non-linear story telling framework.
Module 4 - Predictive and Prescriptive Analytics
Predictive Analytics model predicts occurrence of future events such as customer churn and employee attrition based on
historical data. Prescriptive analytics are used in arriving at best decisions for a given set of managerial objectives under various constraints such as workforce allocation.
Primary objective of this module is to learn how machine learning and operations research techniques can be used for analyzing talent management related problems.
Module Content
- Supervised Learning Algorithms:
- Simple and multiple linear regression, model assumptions and model building, Model disgnostics: R-squared, significance of individual parameters (t-test), Significance of overall model (F-test), Feature Selection (partial F-test), Residual Analysis.
- Logistic Regression (LR): Solving classification problems using logistic regression, LR model diagnostics, significance of individual parameters (Wald’s test), Feature Selection (Likelihood ratio test), Sensitivity, specificity, precision and F-score.
- Decision Tree Learning: Classification and Regression Tree (CART), Chi-square automatic Interaction Detection (CHAID).
- Ensemble Methods: Random forest and boosting algorithms.
- Prescriptive Analytics Techniques: Linear programming, Data Envelopment Analysis.
Case Studies:
1. HR Analytics at ScaleneWorks: Behavioural Modeling to Predict Renege
2. Leveraging Artificial Intelligence in a Skilling Ecosystem
3. Predicting employee attrition at Kramerica Industries
Module 5 - Application of Analytics to HR Data
Identification of project idea and objectives, group formations and initial proposal submission will happen in this module.
Analysis of datasets on recruitment, compensation, performance, engagement, competency analysis, and learning data: linkages with individual level and organizational level outcomes.
Determining appropriate recommendations from analytics for creating value.
Use cases of HR analytics in the workplace.
Discussion on Organizational Network Analysis, application of AI, ML and NLP in HR domain and emerging technologies such as Blockchain- implementation possibilities,limitations and ethical concerns.
Case Studies:
1. Talent Acquisition Group at HCL Technologies: Improving the Quality of Hire through Focused Metrics
2. GROW: Using Artificial Intelligence to Screen Human Intelligence
3. Edge Networks: making HR Intelligent
4. Amber by inFeedo: The CEO’s virtual assistant revolutionizing employee engagement
5. Coinmen Consultants LLP: Adopting a Technology Based Learning Culture
Module 6 (Optional)- Machine Learning Using Python
The module will provide a strong foundation in Machine Learning using Python by providing real-life case studies and examples. This module starts with an introduction to Python language and covers topics ranging from, descriptive analytics and basic statistics and probability to advanced machine learning concepts such as regression, classification, clustering and recommender systems. All the topics include real-world examples and provide step-by-step approach on how to explore, build, evaluate, and optimize machine learning models.
Participants will also complete an asynchronous (self-paced) Online MOOC - Machine Learning Using Python in this module.
Module 7- Project Presentation & Certification
Selection Process
Participants will be shortlisted based on their profiles by Programme Directors.
Evaluation Process
There will be three individual assignments for modules 2, 4 and 5. In addition to the assignment, the participants are expected to work on a group project based on live data to solve business problems using HR analytics.
Programme Directors
Dr. U Dinesh Kumar is a Professor of Decision Sciences and Chairperson of Data Centre and Analytics Lab (DCAL) at IIM Bangalore and holds a Ph.D. in Mathematics from IIT Bombay.
Dr Dinesh Kumar introduced Business Analytics elective course in 2008 to the PGP students at IIM Bangalore and started one of the first certificate programmes in Business Analytics in India in 2010.
He has over 20 years of teaching and research experience and has published more than 70 research articles in leading academic journals.
Thirty eight of his case studies on Business Analytics based on Indian and multinational organizations have been published at the Harvard Business Publishing’s case portal. Nine of his case studies are best sellers at Harvard Business Publishing’s case portal. His case studies are used by more than 220 Institutions across 61 countries across the world. He has authored 3 books, his recent book is titled, “Business Analytics– The Science of Data Driven Decision Making”, published by Wiley in 2017 which was an Amazon Best Seller.
Dr Dinesh Kumar has carried out predictive and prescriptive analytics consulting projects for organizations such as The Boston Consulting Group (India) Private Limited, Hindustan Aeronautics Limited, Qatar Airways, Mission Hospital, Manipal Hospitals, Scalene Works, Wipro Limited, UNIBICand the World Health Organization etc.
Dr Dinesh Kumar conducts corporate training programmes in Analytics and has trained more than 1000 professionals in the field of analytics.
With 28 years of work experience, Dr. Debolina has worked as CHRO for 6 years in 2 multi-national firms. Her last assignment was as CHRO was with Schneider-Luminous as VP-HR, Admin,and CSR. She is also a member of the Board of IIM Indore since 2017 and of an NGO, SSISM which is focused on rural education. She has had in-depth experience across all facets of HR functions across multiple locations, cultures, and organizations (MNC, private sector, and entrepreneurial start-up). Her industry experience spans heavy engineering and electrical switchgear, IT software services, alcobev, and apparel retail.
As an ACC-level ICF certified executive coach, with multiple certifications in the facilitation of behavioral training and certified in psychometric tools, she has facilitated coaching and training sessions for mid-management and leadership level executives. Debolina has substantial experience in dealing with large international stakeholders in organization growth initiatives, change management, mergers & acquisition, and working in multi-cultural environments.
Debolina has completed her Fellow Programme in Management (Industry) from IIM, Indore, PG from IIM, Bangalore, and BTech from College of Engineering, Pune. Apart from her substantial industry experience, Debolina has also published several case studies with Harvard Business Review and academic research articles over the last 4 years with top journals.
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Programme Fee
Rs. 4,40,000/- + GST (as applicable) per participant. The fee is payable in three installments as per indicated schedule.
The payment schedule is as follows:
Rs. 1,00,000/- + GST Confirmation fee
Rs. 1,20,000/- + GST I installment on or before September 4, 2021
Rs. 1,10,000/- + GST II installment on or before October 31, 2021
Rs. 1,10,000/- + GST III installment on or before December 31, 2021
Please Note: *Please add GST at prevailing rates to the programme
Note: The programme fee should be received at the Executive Education Office, before the programme commencement date.
In case of withdrawals, the fee will be refunded only if a request is received at least 15 days prior to the start of the programme.
Online Classes
Live, Online sessions will be conducted over the Zoom platform. Each session will be of 75-minute duration. To overcome the problems of screen fatigue, there will be a break of 15 minutes after every session.
Any supporting textbooks will be mailed to the participant in advance. The Institute’s Learning Management System (LMS) shall be hosting cases and any additional material.
The digital mode is well-suited for data science explorations. For instance, it is easier for participants to share their screens with the instructor for further discussion and troubleshooting, as compared to performing the same tasks in the setting of a campus classroom. Recordings of the classes shall be made available to the participants on a limited use basis. On the administrative side, it is more efficient for our program offices to arrange talks and seminars from professionals who are located across the globe.
Technology Requirements
Given the online orientation, participants must be equipped with the requisite level of Internet connectivity, on a 3 GB daily plan. You will be required to turn your video on! Computers must be recent and powerful, and capable of processing large volumes of data. We recommend laptop models with 8 GB RAM and at least 50 GB of free storage. From time to time, we will suggest installing software packages, most of which are free and open source. We strongly recommend using personal laptops that meet the specifications, without having to worry about restrictions on office issued laptops.
Sample Certificate
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*Certificate image for reference to potential participants only and may change at the discretion of Executive Education Programmes
How To Apply
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- Please logon to online registration portal http://iimberpsrv.iimb.ernet.in/prod/sfeep.home for registering and applying online
- The programme is listed under the “Long Duration Programme” Tab.
Do feel free to get back to us if you should have any clarification.
Email: mathen.mathew@iimb.ac.in Mobile: +91-8951281613