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INTRODUCTION TO HR ANALYTICS 

Modern organizations are ramping up on data science, recruiting talents by making huge  investments for their work engagement. These firms are accelerating their digital transformation to  deploy smart technologies around AI and big data to improve their talent management systems. In a  world of work that is increasingly virtual (and perhaps even only virtual), the volume of data  available to understand and predict employees’ behaviours will continue to grow exponentially,  enabling more opportunities for managing through HR Analytics. As such, people analytics is a  deliberate and systematic attempt to make organizations more evidence-based, talent-centric, and  meritocratic, which, one would hope, should make them more effective. 

Course Objective: 

∙ Understand how HR and analytics have evolved and are transforming people management  

around the world. 

∙ Understand the role of HR Analytics and build skills to conduct a HR Analysis through  

measuring staffing utility. 

Course Outcomes: 

At the end of the course, the student will be able to 

CO1: Describe the role of descriptive and prescriptive analytics in HR analytics. 

CO2: Explain the recent developments in decision based framework for staffing measurement. 

CO3: Analyse the different utility models for taking staffing decisions. 

CO-PO Mapping 

Course Delivery Methods

Eligibility 

Student should score 50% (30 Marks) & 80% attendance to be eligible for the certification Syllabus: (Total Hours Required – 30) 

Module 1: HR Analytics: Analytics-Nature-Evolution of Human Capital Metrics-Steps in Analytics Role of Descriptive analytics & Prescriptive analytics in HR analytics – HR Analytics Frameworks:  LAMP framework, HCM: 21 Framework& Talent ship Framework, Environmental scanning: The Big  Picture-The value of statistical analysis-The importance of risk assessment, Predictive management  

Module 2: Staffing Utility : Concept and Measures A Decision-Based Framework for Staffing  Measurement, Overview: The Logic of Utility Analysis, Utility Models and Staffing Decisions, The  Taylor-Russell Model, The Naylor-Shine Model, The Brogden- Cronbach- Gleser Model 

Module 3: Absenteeism and Separation: Cost of Absenteeism – Direct Costs and the Incidence,  Causes, Consequences, Categories of Costs, Analytics and Measures for Employee Absenteeism,  Strategies to reduce absence, positive Incentives, Paid Time Off (PTO) 

Module 4: Employee Turnover: Separations, Acquisitions, Cost, and Inventory, Voluntary Versus  Involuntary Turnover, Functional Versus Dysfunctional Turnover, Pivotal Talent Pools with High Rates  of Voluntary Turnover, Involuntary Turnover due to Dismissals and Layoffs, computing Turnover 15  rates, training cost, performance difference between separating employees and replacements, cost  of lost productivity and lost business ,Promotion and succession planning analytics, Compliance  analytics 

Module 5: Employee Health Wellness and Welfare: Logic of Workplace Health Programs (WHP),  Analytics for Decisions about WHP Programs, Measures: Cost Effectiveness, Cost-Benefit, and  Return-on-Investment Analysis, Cost-Effectiveness Analysis, Cost Benefit and Return-on Investment Analysis, Employee Assistance Programs (EAPs) Future of Lifestyle Modification, WHP,  and EAPs 

References 

1. Wayne F. Cascio, John W. Boudreau, Investing in people: Financial Impact of Human Resource  Initiatives, Pearson Education, New Jersey, US  
2. Tracey Smith, HR Analytics, The what, Why and How, 1e Create Space Independent Publishing  Platform  
3. Laurie Bassie, Rob Carpenter: HR Analytics Handbook, Mc Bassi & Company; 1st paperback  edition, Brooklyn ,US  
4. Jac Fitz-Enz, The New HR Analytics: Predicting Economic Value of Your Company’s Human Capital  Investments. New York, NY: AMACOM.

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Introduction to Fintech

Resource Persons:

Dr Dhanya Alex* (dhanyaalex@fisat.ac.in)

Ms.Sangeeta G** (sangeetasg9@gmail.com)

Ms. Rohini S***(rohinisoman@gmail.com)

Introduction: 

The emerging technologies, paired with massive changes in regulations, have driven an  unprecedented transformation of finance around the world. This course is designed to explore FinTech  fundamentals and help make sense of this wave of change as it happens. 

Course Objectives: 

• Understand how finance and technology have evolved and are transforming finance around  the world 
• Discuss major technological trends, including cryptocurrencies, Blockchain, AI and Big Data Course Outcomes: 

At the end of the course, the student will be able to  

CO 1: Describe how Artificial Intelligence, Big Data, Crypto currencies and Block chain is changing the  Financial World. 

CO 2: Explain the recent developments in digital financial services. 

Co 3: Analyse the progress of FinTech Regulations. 

CO – PO Mapping: 

Course Delivery Methods:

Assessment Tools: 

Quiz (5 numbers – 50 marks) Assignment (1 number – 10 marks) Total 60 marks Eligibility: 

Student should score 50% (30 marks) & 80% attendance to be eligible for the certification. Syllabus: (Total Hours Required – 30) 

Module 1: FinTech: Introduction– FinTech Evolution: Infrastructure, Collaboration between Financial  Institutions and Start-ups –FinTech Typology – Emerging Economics: Opportunities and Challenges – Introduction to Regulation Industry (6 Hours) 

Module II: Payments, Crypto currencies and Blockchain – Introduction – Individual Payments –Digital  Financial Services – Mobile Money – Regulation of Mobile Money – SFMS – RTGS – NEFT –NDS Systems  – Crypto currencies – Legal and Regulatory Implications of Crypto currencies –Blockchain – The  Benefits from New Payment Stacks (6 Hours) 

Module III: Digital Finance and Alternative Finance -Introduction – Brief History of Financial Innovation  – Digitization of Financial Services – FinTech & Funds- Crowd funding– Regards, Charity and Equity – P2P and Marketplace Lending – New Models and New Products – ICO (6 Hours) 

Module IV: FinTech Regulation and RegTech -Introduction – FinTech Regulations Evolution of RegTech  – RegTech Ecosystem: Financial Institutions – RegTech Ecosystem Ensuring Compliance from the Start:  Suitability and Funds – RegTech Startups: Challenges –RegTech Ecosystem: Regulators Industry –Use  Redesigning Better Financial Infrastructure (6 Hours) 

Module V: Data & Tech – Introduction– Data in Financial Services –Application of Data Analytics in  Finance – Methods of Data Protection – How AI is Transforming the Future of FinTech –Digital Identity  – Change in mindset: Regulation 1.0 to 2.0 (KYC to KYD) – AI & Governance – New Challenges of AI and  Machine Learning – Challenges of Data Regulation (6 Hours) 

References: 

• Susanne Chishti and Janos Barberis, “The FINTECH Book: The Financial Technology Handbook  for Investors, Entrepreneurs and Visionaries”, John Wiley, 1st Edition, 2016 
• Theo Lynn, John G. Mooney, Pierangelo Rosati, Mark Cummins, “Disrupting Finance: FinTech  and Strategy in the 21st Century”, Palgrave, 1st edition, 2018 
• Abdul Rafay, “FinTech as a Disruptive Technology for Financial Institutions”, IGI Global,  January, 2019 

* Dr. Dhanya Alex, Associate Professor, FISAT Business School 

** Sangeeta G, US Tax Associate, KPMG Global Services 

*** Rohini S, US Tax Associate 2, Big Four

A healthy mentoring system exists in the department. Three faculty mentors are assigned for each B.Tech class of sixty students and one faculty mentor for the M.Tech class of 24 Students. Number of students per mentor is approximately 20.

The following faculty members are assigned as mentors for the students during the academic year 2023-24 in the Department of Civil Engineering.

BTech 2020 Admission

CE | Beena B R, Anna Rose Varghese, Sreerath S

BTech 2021 Admission

CEA | Reshma Prasad, Anu Roy, Abin Thomas CA

CEB | Rinu J Achison, Panjami K , Abhijith R

BTech  2022 Admission

CEA | Preethi M, Abhiya Abbas Mundol

CEB | Keerthi Sabu , Sowmya V Krishnankutty

BTech  2023 Admission

CEA | Neeraja Nair, Sharon Jacob, Eldhose P Jacob

CEB | Jawahar Saud S, Nincy Jose, Dr. Ayswarya E P

SECM  2022 Admission | Dr. Kavitha P E

SECM  2023 Admission | Dr. Asha Joseph

ADD ON COURSE (2021-22) 

 Customer Analytics 

Course Facilitator: Dr. Jose Varghese 

(josevarghese@fisat.ac.in) 

Introduction: 

Customer analytics is a process by which data from customer behavior is used to help make  key business decisions via market segmentation and predictive analytics. This information is  used by businesses for direct marketing, site selection, and customer relationship management. 

Course Objectives: 

• Create a single, accurate view of a customer to make decisions about how best to

acquire and retain customers. 

• Gather deeper understandings of customers’ buying habits and lifestyle preferences

Course Outcomes: 

At the end of the course, the student will be able to:- 

CO 1: Acquire knowledge related to the various terminologies and techniques associated  with customer analytics 

 CO 2: understand the segmentation process 

 CO 3: Analyze how the markets prices are determined 

CO – PO Mapping: 

Course Delivery Methods:

Eligibility: 

Student should score 50% (30 marks) & 80% attendance to be eligible for the certification. 

Syllabus: (Total Hours Required – 30) 

Module 1- Marketing Management Process and Customer Analytics 

The Marketing Management Process and its link to Customer Analytics and Customer Insights – Correlation – Simple linear regression – Trend – seasonality- Exponential smoothing 

Module 2 – Pricing 

Non-linear pricing strategies for profit maximization – price skimming and sales – optimal pricing – price bundling – demand curve and the willingness to pay 

Module – 3 – Customer Insights 

Conjoint analysis – product attributes and levels – full profile conjoint analysis – choice based conjoint  analysis – random utility theory 

Module 4 – Customer value 

Lifetime customer value, – relation between spending, customer acquisition and customer retention – Market basket analysis – RFM analysis 

Module 5 – Market Segmentation 

Cluster analysis – collaborative filtering – classification trees for segmentation – Application of  Customer Analytics in Advertising, Retailing and Internet & Social Marketing 

References: 

1. Winston, Wayne L. (2014), Marketing Analytics: Data-Driven Techniques with Microsoft Excel, 1st  ed. Wiley. 
2. Winston, Wayne L. (2014), Marketing Analytics: Data-Driven Techniques with Microsoft Excel, 1st  ed. Wiley. 
3. Malhotra, Naresh (2015), Marketing Research – An Applied Orientation, 7th ed., Pearson  EducationVandana Ahuja. Digital Marketing. Oxford University Press India, 2015 
4. Damian Ryan. Understanding Digital Marketing: Marketing Strategies for Engaging the Digital  Generation (3rd Edition). Kogan Page Publishers, 2014.

The Department of Computer Applications offers various skill enhancing add-on courses to empower the students thereby supplementing the MCA curriculum to bridge the gap between industry and academia. These add-on programs are offered with the help of internal as well as external resources. Students who successfully complete these courses are issued with certificates.

List of Courses

(2023 – 2024)

1. Add-On Course on AWS Fundamentals

• Course Code: 21MCAAOC401
• Resource Person: Internal
• Beneficiaries: Fourth Sem MCA Students
• Instructional Strategies: Lectures, Tutorials, MCQs, Assignments/Activities.
• Course Objectives: The Course is intended for final year students aiming to help them to build and  validate their skills so they can get more out of the cloud. This AWS Fundamentals Course is designed to teach the core concepts needed to work effectively within AWS.

2. Self-Paced Course on Academic Typesetting Using LaTeX

• Course Code: 21MCAAOC301
• Resource Person : External
• Beneficiaries: Third Sem MCA Students
• Instructional Strategies: Video Lectures, Tutorials, MCQs, Assignments/Activities.
• Sample Video Link : https://youtu.be/c5dnMPrdits
• Course Objectives: The Course is intended for final year students aiming to help them in preparing the main project report, mini project report and the seminar report using LaTeX. For preparing the presentations, the beamer package is also introduced through the course.

3. Add-On Course on Statistical Programming Using R

• Course Code : 21MCAAOC201
• Resource Person : Internal
• Beneficiaries: Second Semester MCA Students
• Instructional Strategies: Lectures, Assignments/Tutorials.
• Course Objectives: The Course is intended for second semester MCA students aiming to introduce the R Programming language. R is an open-source programming language that is widely used as a statistical software and data analysis tool. R provides various facilities for carrying out machine learning operations like classification, regression and also provides features for developing artificial neural networks which will be beneficial for the students while doing mini or main projects.

4. Add-On Course on Data Mining Tools

• Course Code : 21MCAAOC201
• Resource Person : Internal
• Beneficiaries: Second Semester MCA Students
• Instructional Strategies: Lectures, Assignments/Tutorials.
• Course Objectives: The Course is intended for second semester MCA students aiming to introduce the Data Mining concepts and commonly used tasks/algorithms. The implementation of concepts/algorithms using Weka, R Programming language have also been introduced which will be beneficial for the students to do mini/main projects in Data Mining.

5. Add-On Course on Review of C Programming

• Course Code: 21MCAAOC101
• Resource Person: Internal
• Beneficiaries: First Semester MCA Students
• Instructional Strategies: Lectures, Assignments/Tutorials.
• Course Objectives: This is a basic course and the student is expected to review and understand the programming concepts which will in turn help them in learning other Computer Languages in the curriculum.

6. Add-On Course on Basics of Accounting

• Course Code: 21MCAAOC102
• Resource Person: Internal
• Beneficiaries: First Semester MCA Students
• Instructional Strategies: Lectures, Assignments/Tutorials.
• Course Objectives: This course is intended to help the students to learn the basic concepts and functions of accounting and its role in the performance of an organization and to understand how to record the day-to-day operations of an organization in standard format. Learners shall have a broad view of terms like journal, ledgers and final book of accounts.

Class advisors of previous semesters: 2020-21

PROGRAMME OUTCOMES (PO)

Computer Application Graduates will be able to:

1. Computational Knowledge: Apply knowledge of computing fundamentals, computing specialization, mathematics, and domain knowledge appropriate for the computing specialization to the abstraction and conceptualization of computing models from defined problems and requirements.
2. Problem Analysis: Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines.
3. Design /Development of Solutions: Design and evaluate solutions for complex computing problems, and design and evaluate systems, components, or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations.
4. Conduct Investigations of Complex Computing Problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
5. Modern Tool Usage: Create, select, adapt and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations.
6. Professional Ethics: Understand and commit to professional ethics and cyber regulations, responsibilities, and norms of professional computing practices.
7. Life-long Learning: Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional.
8. Project Management and Finance: Demonstrate knowledge and understanding of the computing and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
9. Communication Efficacy: Communicate effectively with the computing community, and with society at large, about complex computing activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give and understand clear instructions.
10. Societal and Environmental Concern: Understand and assess societal, environmental, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practices.
11. Individual and Team Work: Function effectively as an individual and as a member or leader in diverse teams and in multidisciplinary environments.
12. Innovation and Entrepreneurship: Identify a timely opportunity and use innovation to pursue that opportunity to create value and wealth for the betterment of the individual and society at large.

The target to be achieved for each course includes two parameters:

1. Expected Proficiency /Knowledge (EP) :-

It is the grade secured by more than 50% of the total number of students in the previous university examinations.

2. Expected Attainment (EA) :-

Eg: From table 3.2.2.1, the Previous Attainment based on previous University Examinations is 71.35% students can score the Expected Proficiency of C grade. Expected Attainment is fixed as 30% improvement over a six year period considering 5% improvement every year.

3. A table showing the procedure for fixing the target for the course Solid State Devices (EC 203) is shown below:

Measuring COs attained through University Examinations

Target may be stated in terms of percentage of students getting more than the university average marks or more as selected by the Program in the final examination. For cases where the university does not provide useful indicators like average or median marks etc., the program may choose an attainment level on its own with justification.

Table 3.2.2.3: Indirect attainment and university attainment

Table 3.2.2.4: Sample of Expected Course outcome attainment calculation

(Indirect assessment and University examination)

Table 3.24: Course Outcome marks

Measuring COs attainment through Internal Examination

DIRECT ASSESEMENT

Table 3.2.2.5(A): Measuring COs attainment through Internal Examination of EC203 (SSD)

Table 3.2.2.5(B): Sample of CO attainment calculation

Course Outcome Attainment:

Table 3.2.2.6: Sample of CO attainment calculation for a course

CO attainment (each) = 70% of direct assessment + 20% of university assessment + 10% of indirect assessment.

Eg: For CO1, attainment = 0.7*98.95 + 0.2* 75.79 + 0.1*96.84 = 94.1

Attainment level for CO1 is 3. (High attainment level)