DBA & Masters Dual Degree Program

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Awarding Institution : Dunster Business School Switzerland

Henry Harvin Role : Admission Partner

Assessment Requirement : Yes by Dunster Business School Switzerland

Course Info

Earn a prestigious dual-degree program: DBA from Dunster Business School, Switzerland, and a Master’s from FLISM, USA | Elevate your leadership with technical expertise sharp | Choose from multiple high-impact masters specializations including Data Science, HR, AI, Cybersecurity, and more.

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Accreditations & Affiliations

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About DBA & Masters Dual Degree Program

DBA & Masters Dual Degree Program

The DBA & Masters Dual-Degree Program is an elite, research-driven leadership journey tailored for experienced professionals looking to elevate their academic and executive career. Delivered by Dunster Business School, Switzerland, and the Florida Institute of Sciences & Management (FLISM), USA, this program blends doctoral-level research with industry-relevant managerial expertise.

 

Available Master’s Specializations

Participants can choose from a wide range of in-demand Master’s programs, including:

  • Master’s in Data Science & Business Analytics

  • Master’s in Human Resource Management

  • Master’s in Machine Learning & AI

  • Master’s in Cybersecurity

  • Master’s in Finance & Risk Management

  • Master’s in Lean Six Sigma & Operations

  • Master’s in Marketing Strategy & Digital Growth

  • Master’s in Business Intelligence & Performance

  • Master’s in Design Thinking & Innovation

  • Master’s in Strategic Project Management

  • Master’s in Instructional Design

  • Master’s in Entrepreneurship & Start-up Management

  • Master’s in International Business & Trade

 

Who Should Attend?

  • Senior Professionals and CXOs

  • Entrepreneurs & Business Owners

  • Management Consultants

  • University Lecturers & Researchers

  • Directors and Functional Heads

Eligibility Criteria

Candidates must hold a Master’s degree and demonstrate a minimum of 7–10 years of professional experience. Strong research intent is highly encouraged.

Program Structure

Program Institution Duration Component
DBA Dunster Business School, Switzerland 2–4 Years Dissertation + Research Projects
Masters (Specialization) FLISM, USA 1–2 Years Course Modules + Capstone Project

Both programs are designed to be completed concurrently within 24–36 months.

AGOTA™ Framework

Agota™ Framework is a versatile 10-in-1 program that includes various aspects of competency development and career development.

  • Training: 1-2 Years of Two-Way Live Online Interactive Sessions
  • Projects: Facility to undergo projects in Managerial Economies, Recruitment and Selection, Business Analytics  & more
  • Masterclass: Access to 52+ Masterclass Sessions for essential soft skill development
  • Student Engagement & Events: Access to all student engagement & events throughout the program
  • Membership: 24 Months of Gold Membership of School of Insternational Studies  
  • E-Learning: Free access to E-learning Portal and future updates. Get access to PPTs, Projects, Quizzes, self-paced Video based learning, question bank, library, practice tests, final assessment, a forum, and doubt sessions.
  • Dual Degree: Get Dual Degree in DBA By Dunster Business School, Switzerland and Masters (Specialization) by FLISM, USA 
  • Internship: Get a guaranteed Internship with Henry Harvin® and in top MNCs like J.P. Morgan, Accenture & many more via Forage
  • Entrepreneurship Mentorship: Mentorship from Young Successful Entrepreneurs to set up a sustainable & scalable Business from scratch at both Freelance & Entrepreneur level
  • Placement: Get 3 in 1 Placement support through Placement Drives, Premium access to the Job portal & Personalized Job Consulting

Trainers at Henry Harvin®

  • Most respected industry experts with 18+ years of working experience and recognized by numerous organizations over the years for their work
  • They have delivered 550+ keynote classes for the DBA and Masters 
  • Have delivered 650+ lectures and are currently empaneled as domain experts School of International Studies

Alumni Status

Become a part of the Elite School of International Studies of Henry Harvin® and join the 4,60,000+ large Alumni Network Worldwide.

 

Gold Membership Benefits

  • Avail 24 Months Gold Membership of Henry Harvin® School of International Studies that includes E-Learning Access through recorded Videos, Games, Projects, CPDSPe Studies
  • Access to Masterclass Sessions
  • Earn the Prestigious Alumni Status and become one of the reputed 4,60,000+ Alumni across the globe.
  • Guaranteed Internship with Henry Harvin®️ or partner firms
  • Weekly 10+ job opportunities offered.
  • Experience Industry Projects during the training

Learning Benefits

  • Gain ability to formulate, implement, and evaluate business strategies
  • Get an understanding of competitive analysis and strategic positioning
  • Gain proficiency in financial statement analysis, budgeting, and financial forecasting
  • Understanding of corporate finance principles and investment strategies
  • Learn enhanced leadership skills, including team building, motivation, and conflict resolution
  • Learning about marketing principles, consumer behavior, and market research
  • Learn skills in developing and implementing marketing strategies and sales plans
  • Get exposed to the practical skills of talent management, employee relations, and organizational behavior
  • Understand entrepreneurial processes, innovation management, and new venture creation
  • Be able to improve business communication skills, including writing, presenting, and interpersonal communication
  • On completion of the course, become eligible for various Job Roles

Know the complete offerings of our DBA & Masters Dual Degree Program

Key Highlights

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196 Hours of Instructor-Led Sessions
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64 Hours of Live Interactive Doubt Solving Sessions
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32 Hours of Live Master Sessions by Industry Experts
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288 Hours of Self-Paced Learning
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Comprehensive Support System: 24x7 End-to-End Assistance
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Complete degree in 1-2 year with fast-track options
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Industry-Aligned: Developed in consultation with top MNCs to meet market needs
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150 Guided Hands-On Real World Challenges
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Recognized Degree: Equivalent to on-campus Doctorate credentials
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World-Class Pedagogy: Delivered by expert faculty with cutting-edge teaching methods.
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Get 24 Months of Gold Membership of Henry Harvin® School of International Studies
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Earn a Dual Degree from an International University, Dunster Business School and FLISM

Disclosure

This program is supported and facilitated by Henry Harvin Education.

The certificate/degree (if applicable) is awarded solely by Dunster Business School Switzerland, subject to eligibility and successful completion of assessments.

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Agota™ Framework

It is a trajectory that offers complete growth of an individual incorporating the two most significant focus areas of contemporary learning:The Competency Development and The Career Development .

Competency development is about building capabilities that not only meet current job demands but also anticipates the future needs. It includes:

  • 1: Two-Way Live Training Two-way Live Online Interactive Classroom Sessions

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    2: Projects Facility to undergo various projects along with the course.

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  • 3: Convocation Hybrid convocations are conducted both online and offline, offering networking opportunities, practical exposure, and on-stage felicitation to celebrate your achievements.

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    4: Masterclass Access to 52+ Masterclass Sessions for essential soft skill development

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  • 5: Membership Get Gold Membership of Henry Harvin®

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    6: E-Learning Access Free access to the E-learning Portal and future updates. Get access to PPTs, Projects, Quizzes, self-paced Video-based learning, a question bank, a library, practice tests, final assessment, a forum, and doubt sessions.

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Career Development lays focus on the essentials for acquiring a good career or diving into a highly competent one. It includes:

  • 7: Hallmark Certification + License Distinguish your profile with global credentials and showcase expertise with our Hallmark Completion certificate with Professional License

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    8: Internship Support Get Internship Support with Henry Harvin® and in top MNCs like J.P. Morgan, Accenture & many more via Forage & 100X Suite.

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  • 9: Entrepreneurship Mentorship Mentorship from Young Successful Entrepreneurs to set up a sustainable & scalable Business from scratch at both Freelance and entrepreneur levels

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    10: Placement Support Get 3 in 1 Placement support through Placement Drives, Premium access to Job portal & Personalized Job Consulting

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Curriculum For DBA & Masters Dual Degree Program

  • icons-carri33Doctorate of Business Administration (DBA)

    Module 1: Thesis management

    • Research
      a. Scope and Significance
      b. Types of Research
      c. Research Process
      d. Characteristics of Good Research
      e. Identifying Research problem
      f. Meaning of Sampling Design
      g. Steps in sampling
      h. Criteria for good sample design
      i. Types of Sample Design
      j. Probability and non-probability sampling methods
      k. Meaning of Measurement
      l. Types of scales

      Review of Literature
      a. Data Collection
      b. Types of Data
      c. Sources of Data Collection
      d. Methods of Data collection
      e. Constructing questionnaire
      f. Establishing, reliability and validity
      g. Data processing
      h. Coding, Editing and tabulation of data
      i. Meaning of Report writing
      j. Types of Report
      k. Steps of report writing
      i. Precautions for writing report
      m. Norms for using Tables
      n. Charts and diagram
      o. Appendix: - Index, Bibliography.

    Module 2: General Research Methodology

    • Meaning and importance of Research
    • Types of Research
    • Selection and formulation of Research Problem
    • Meaning of Research Design
    • Need of Research Design
    • Features of Research Design
    • Inductive, Deductive and Development of models
    • Developing a Research Plan
    • Exploration, Description, Diagnosis, Experimentation
    • Determining Experimental and Sample Designs
    • Analysis of Literature Review
    • Primary and Secondary Sources
    • Web sources
    • Critical Literature Review
    • Hypothesis
    • Different Types of Hypothesis
    • Significance
    • Development of Working Hypothesis
    • Null hypothesis
    • Research Methods: Scientific method vs Arbitrary Method
    • Logical Scientific Methods: Deductive, Inductive, Deductive-Inductive
    • Pattern of Deductive
    • Inductive logical process
    • Different types of inductive logical methods.

    Module 3: Quantitative Research Methods

    • Introduction to Quantitative Research
    • Part 1:

      a. Session Overview
      b. RQ Hypothesis Course Context Video
      c. What is Quantitative Research?
      d. Ethics of Quantitative Research
      e. Session Summary


      Part 2:

      f. Session Overview
      g. Introduction to the Scientific Method of Research
      h. Comparing Descriptive, Predictive and Prescriptive Research
      i. Inductive and Deductive Approaches to Quantitative Research
      j. Constructing Models
      K. Session Summary

    • Exploring Quantitative Research Design
    • Part 1:

      a. Session Overview
      b. Fundamentals of Research Design
      c. Components of a Research Design
      d. Characteristics of a Research Design
      e. Session Summary


      Part 2:

      f. Session Overview
      g. Research Design for Experimental Research Studies
      h. Research Design for Quasi Experimental Studies
      i. Research Design for Non-Experimental Research Studies
      j. Evaluating Quantitative Research Design
      k. Session Summary

    • Data Collection for Quantitative Research
    • Part 1:

      a. Session Overview
      b. Defining Surveys
      c. Exploring Survey Methods
      d. Session Summary


      Part 2:

      e. Session Overview
      f. The Process of Questionnaire Development
      g. Designing a Questionnaire
      h. Designing Rating Scales
      i. The Art of Asking Questions
      j. Session Summary


      Part 3:

      k. Session Overview
      l. Tips to Conduct Effective Surveys
      m. Ethics of Using Technology in Surveys
      n. Session Summary

    • Measurement and Sampling
    • Part 1:

      a. Session Overview
      b. What is Measurement?
      c. True Score Theory, Estimating Measurement Errors
      d. Evaluating Validity of Measures
      e. Evaluating Reliability of Measures
      f. Session Summary


      Part 2:

      g. Session Overview
      i. Basic Concepts of Sampling
      j. Problems and Blases in Sampling
      k. Probability Sampling
      l. Non-Probability Sampling
      m. Session Summary


      Part 3:

      n. Session Overview
      o. Determining the Sample Size
      p. Sampling Distribution and Statistical inference
      q. Demonstrations on Sampling
      r. Session Summary

    • Constructing Statistical Models
    • Part 1:

      a. Session Overview
      b. Significance of Comparing Means for Analysis
      c. What is ANOVA?
      d. Types of ANOVA
      e. Calculating and Interpreting One-Way ANOVA
      f. Session Summary


      Part 2:

      g. Session Overview
      h. Building a Statistical Model
      i. Effect of Moderating and Mediating Variables
      j. Demonstration on Mediation and Moderation
      k. Session Summary

    • Enhancing Statistical Models
    • Part 1:

      a. Session Overview
      b. What is Factor Analysis?
      c. Conducting Factor Analysis
      d. Demonstration on R: Factor Analysis
      e. Interpreting Factor Scores
      f. Session Summary


      Part 2:

      g. Session Overview
      h. What is Factorial ANOVA?
      i. Dealing with Interaction Effects in Factorial ANOVA
      j. Calculating and Interpreting Factorial ANOVA
      k. Session Summary

    • Multivariate Analyses
    • Part 1:

      a. Session Overview
      b. Multivariate regression
      c. MANOVA
      d. Logistic Regression
      e. Structural Equation Modeling
      f. Tree Structured Methods
      g. Conjoint Analysis
      h. Session Summary


      Part 2:

      i. Session Overview
      j. Time Series
      k. Cluster Analysis
      l. Session Summary

    • Writing a Quantitative Research Paper
    • Part 1:

      a. Session Overview
      b. Introduction to Formatting the Research Project for Quantitative Research
      c. Components of a Quantitative Research Paper
      d. Writing the Summary, Background and Purpose of Quantitative Research
      e. Writing the Literature Review
      f. Detailing your Research Design/Methodology
      g. Curating your Results, Analysis and Supplimentary Findings
      h. Outlining your Conclusions and Reccomendations
      i. Making Appendices
      j. Session Summary


      Part 2:

      k. Session Overview
      l. Writing Different Types of Quant Papers
      m. Guidelines for Fine Tuning your Research Presentation
      n. Session Summary

    Module 4: Qualitative Research Methods

    Introduction to Qualitative Research

    • Key Elements of Qualitative Research
    • Writing Qualitative Research Question
    • Qualitative Research: Framework
    • Steps to Write a Qualitative Research Paper
    • Ethics for Qualitative Research and IRB
    • Introduction to Design Strategies
    • Data-Collection and Analysis Strategies
    • Introduction to research design
    • Major aspects of research design

    Data Collection in Qualitative Research

    • Sources of Evidence: A Comparative
    • Assessment (Forms-Strengths-Weaknesses)
    • Principles of Data Collection
    • Sampling
    • Reliability and Validity

    Interviews and Focus Groups
    Introduction to Data Analysis

    • An Introduction to Data Analysis
    • First Cycle Coding (Description +Demo)
    • Second Cycle Coding (Description +Demo)
    • Jottings and Analytic Memoing (Description +Demo)
    • Assertions and Propositions (Description +Demo)
    • Within Case and Cross-Case Analysis (Description +Demo)

    Data Display and Exploration

    • Matrix and Networks
    • Timing, formatting
    • Extracting Inferences and Conclusions
    • Exploring Fieldwork in Progress
    • Exploring Variables
    • Exploring Reports in Progress

    Data Analysis Process - Next Steps

    • Describing Participants
    • Describing Variability
    • Describing Action
    • Ordering by time
    • Ordering by process
    • Explaining Interrelationship-Change
    • Explaining Causation
    • Making Predictions

    Verifying Conclusions

    • Tactics to achieve integration among diverse pieces of data
    • Tactics to sharpen understanding by differentiation
    • Tactics of seeing relationships in data abstractly
    • Tactics to assemble a coherent understanding of data
    • Tactics for testing or confirming findings
    • Standards for quality of conclusions

    Writing Report and New Technologies

    • Other methods in Qualitative Research
    • Audiences and Effects
    • Different aspects / apa
    • An Introduction to Mixed Methods Research

  • icons-carri33Masters in Data Science

    Module1: SQL

    • SQL Overview
    • SQL Manipulation
    • JOIN; Inner, Left, Right, Full Outer, and Cross JOIN
    • String Functions
    • Mathematical Functions
    • Data-Time Functions
    • Hunting Tips

    Module 2: Power BI

    • Business Intelligence (BI) Concepts
    • Microsoft Power BI (MSPBI) Introduction
    • Connecting Power BI with Different Data Sources
    • Power Query for Data Transformation
    • Data Modelling in Power BI
    • Reports in Power BI
    • Reports & Visualization Types in Power BI
    • Dashboards in Power BI
    • Data Refresh in Power BI
    • End to End Data Modelling & Visualization

    Module 3: Python Programming

    • Python Basics
    • Python Programming Fundamentals
    • Python Data Structures
    • Working with Data in Python
    • Working with NumPy Arrays
    • Case Study
    • Project
    • Dataset

    Module 4: R Programming

    • R Basics
    • Working with Data in R
    • R Programming Fundamentals
    • Data Structures in R
    • Handling Data in R

    Module 5: CRISP ML(Q)

    • Project Management Methodology

    Module 6: Data Types and Data Processing

    • Nominal
    • Ordinal
    • Interval
    • Ratio
    • Data Cleaning techniques

    Module 7: Statistics

    • Descriptive
    • Inferential
    • Hypothesis Testing

    Module 8: EDA

    • Business moments, Graphical representation, Feature Engineering
    • Case Study

    Module 9: Mathematical Foundation

    • Optimization
    • Derivatives
    • Linear Algebra
    • Matrix Operations

    Module 10: Clustering

    • Hierarchical Clustering
    • K Means Clustering

    Module 11: Dimension Reduction

    • PCA,SVD

    Module 12: Association Rules

    • Market Basket Analysis
    • Association Rules Intuition
    • Association Rules Applications
    • Association Rules Terminology
    • Association Rules Performance Measures

    Module 13: Recommendation Engine

    • Intro to personalized strategy
    • Similarity measures
    • User-based collaborative filtering
    • Item-to-item collaborative filtering
    • Recommendation engine vulnerabilities

    Module 14: Text Mining and NL

    • Text Mining Importance
    • BOW, Terminology and Preprocessing
    • Textual Data cleaning
    • DTM and TDM
    • Corpus level
    • positive and negative word clouds
    • Social media web scraping

    Module 15: Naive Bayes

    • Probability
    • Joint probability
    • conditional probability
    • Naive Bayes formula
    • Use case

    Module 16: KNN

    • Nearest Neighbour Classifier
    • 1- Nearest Neighbour classifier
    • K- Nearest Neighbour Classifier
    • Controlling complexity in KNN
    • Euclidean Distance

    Module 17: Decision Tree

    • What is a Decision Tree
    • Building a Decision Tree
    • Greedy Algorithm
    • Building the Best Decision Tree
    • Attribute selection- Information gain

    Module 18: Ensemble Techniques

    • Ensemble Primer
    • Voting
    • Stacking
    • Bagging, and Random Forest
    • Boosting Models

    Module 19: Confidence Interval

    • Intro to Normal Distribution
    • Probability Calculation for normally distributed data
    • Normal QQ plot
    • Central Limit Theorem
    • Confidence Interval

    Module 20: Hypothesis Testing

    • Hypothesis Testing
    • Flowchart- Y is continuous2 sample T-Test
    • One Way ANOVA
    • Flowchart- Y is discrete
    • 2 proportion Test
    • Chi-Square Test

    Module 21: Regression Techniques

    • Simple Linear
    • Multiple Linear
    • Logistic Regression
    • Multinomial Regression
    • Ordinal Regression
    • Advance Regression

    Module 22: SVM

    • SVM Hyperplanes
    • Best fit Hyperplane
    • Kernel Tricks
    • Multiclass Classification using SVM

    Module 23: Survival Analytics

    • Intro to Survival Analytics
    • Applications
    • Time to event, Censoring, Kaplan Meier Survival Function

    Module 24: Forecasting

    • TimeSeries vs Cross-Sectional Data
    • Time Series Dataset
    • Forecasting Strategy
    • Time Series Components
    • Time Series Visualizations
    • Time Series Partition
    • Forecasting Methods
    • Forecasting Errors
    • Seasonal Index

    Module 25: ANN

    • Neural Network Primer
    • Perceptron and Multi-Layered Perceptron Algorith
    • Activation Function
    • Error Surface
    • Gradient Descent Algorithm

    Module 26: CNN

    • Image Net Challenge
    • Parameters Explosion and MLP
    • Convolutional Networks
    • Convolutional Layers and Filters
    • Pooling Layer
    • Practical Issue
    • Adversaries

    Module 27: RNN

    • Traditional Language Models
    • Wny not MLP
    • Recurrent Neural Network
    • RNN types
    • CNN+RNN
    • Bidirectional RNN
    • Deep Bidirectional RNN
    • RNN vs LSTM
    • Deep RNN vs Deep LSTM's

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