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Doctor of Business Administration in Artificial Intelligence and Machine Learning

Application closes on 4th Jul 2026

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Doctor Of Business Administration in Artificial Intelligence and Machine Learning

Application closes 4th Jul 2026

What's new?

Build expertise in Generative AI

Learn Generative AI using tools like ChatGPT and Hugging Face to build LLM-powered applications.
Modules on MLOps and Multimodal AI

With our Artificial Intelligence course, master MLOps for seamless model deployment alongwith Multimodel AI to integrate and process diverse data types effectively

Program Outcomes

Transform business and drive growth with AI

Drive business transformation as a strategic AI leader

Gain strategic insights to manage and execute AI projects effectively
Build innovative AI-powered products and services to drive growth
Boost your career with industry-relevant credentials
Demonstrate mastery and earn the title of 'Dr.'

Earn dual certificates

Ranked #1 among Top 10 Online DBA Degrees of 2024

Forbes
Top Tier 1 ranking for DBA

Global DBA Ranking by CEO Magazine
Accredited by the HLC

Accreditation agency recognized by US Dept. Edu.

Key program highlights

Why choose the DBA in AI & ML program

Top ranked DBA by Forbes

Ranked among top 10 online DBA degrees of 2024 by Forbes for academic quality and industry relevance.
Hands-on projects followed by thesis

Work on numerous real world projects followed by capstone projects and a final dissertation with dedicated guidance from top faculty and industry experts.
WES recognized and HLC accredited

Enhances your professional credibility and academic opportunities.
Alumni status from Walsh College

Earn alumni status from Walsh College upon program completion.
Expert mentorship and support

Engage with AI experts for project guidance, get 1:1 support, weekly sessions, and dedicated program manager assistance.
Earn 60 Semester credit hours in this program

Complete a rigorous, research-driven DBA in AI & ML with 60 Semester Credit Hours (SCH), qualifying you for senior leadership roles and advanced professional growth.
A cohort of experienced leaders

87% of participants are senior professionals, 45% in leadership roles—bringing diverse expertise from sectors like tech, finance, healthcare, and more.
Powerful Global Network

Network with professionals from leading firms like Amazon, Microsoft, and JPMorgan, and unlock career opportunities beyond the classrooms.

Generative AI

Prompt Engineering

Machine Learning

Research Methodology

Academic Writing & Publication

Deep Learning

Neural Networks

Business Intelligence Using AI

Agentic AI

AI Strategy & Ethics

Generative AI

Prompt Engineering

Machine Learning

Research Methodology

Academic Writing & Publication

Deep Learning

Neural Networks

Business Intelligence Using AI

Agentic AI

AI Strategy & Ethics

This program is ideal for

DBA in AI & ML empowers leaders to drive innovation, transformation and research-led impact.

View Batch Profile

Senior professionals

Elevate your career with advanced leadership skills, applied research capabilities, and AI-driven business strategies
Domain experts and functional Leaders

Integrate AI/ML into functional areas like marketing, finance, operations, and HR to solve complex business problems
CXOs and business heads

Strengthen your strategic edge and guide your organization’s AI transformation with global insights and doctoral-level expertise
Technology leaders

Lead AI initiatives and innovation teams with a deep understanding of technical architectures and their business impact

Learn with self-paced videos

Our pedagogy is designed to ensure career growth and transformation

Learn with self-paced videos

Learn critical concepts from video lectures by faculty & AI experts
Engage with your mentors

Clarify your doubts and gain practical skills during the weekend mentorship sessions
Work on hands-on projects

Work on projects to apply the concepts & tools learnt in the module
Get personalized assistance

Our dedicated program managers will support you whenever you need

Get an exclusive preview of the course

Explore faculty videos and mentorship sessions. Get insights into relevant case-studies and projects.

Curriculum

This DBA in AI & ML syllabus, developed by esteemed experts from Walsh College and Great Learning, offers comprehensive insights into AI and Machine Learning. A unique research-led curriculum which allows you to work with dedicated guidance from top faculty and industry experts to help you master the AI & ML domain.

Year 1

Course: Introduction to AI Landscape

This preparatory module will introduce you to the world of data and AI, provide an overview of how problems are solved in the industry using data and AI, and give you a fundamental understanding of the hands-on tools needed to build a strong foundation for Generative AI applications.

Course 01: Python Foundations

Course 02: Data Manipulation and EDA

In this module, you will learn to read, explore, manipulate, and visualize data to tell stories, solve business problems, and deliver actionable insights and recommendations by performing exploratory data analysis using some of the most widely used Python packages.

Course 03: Linear Regression, Decision Tree, K-means Clustering

This module is designed to help you build an understanding of the concept of learning from data, develop linear and non-linear models to capture relationships between attributes and known outcomes, and discover patterns in and segment data with no labels.

Course 04: Bagging, Boosting and Model Tuning

This module focuses on exploring how to combine the decisions from multiple models using ensemble techniques to improve performance and make better predictions, while applying feature engineering and hyperparameter tuning to build generalized, robust models that optimize associated business costs.

Course 05: Neural Network

This module helps you implement neural networks to synthesize knowledge from data, understand different optimization algorithms and regularization techniques, and evaluate factors that improve performance, enabling you to build generalized and robust neural network models to solve business problems.

Course 06: Attention Mechanism, Transformers Transformers, LLM, Prompt engineering & RAG

This module helps you get introduced to the world of Natural Language Processing (NLP), gain a practical understanding of text embedding methods, and learn how different transformer architectures power Large Language Models (LLMs). You will explore how Retrieval-Augmented Generation (RAG) integrates information retrieval to improve the accuracy and relevance of LLM responses, and design and implement robust NLP solutions using open-source LLMs combined with prompt engineering techniques.

Course 07: AI Agents Workflow, Planning, Reasoning and Evaluating AI Agents

This module introduces you to the shift from traditional automation to Agentic AI. You will learn how to build intelligent agents using LangChain, equip them with dynamic tool-use capabilities, integrate memory into AI agents, and understand the mechanics of planning, multi-step reasoning, and the ReAct framework to enable agents to decompose and solve complex, multi-stage tasks. Finally, you will learn to evaluate AI agents to develop reliable AI solutions enhanced with human oversight.

Course 08: Model Deployment & Containerization

This module helps you understand the role of model deployment in realizing the value of an ML model and teaches you how to build and deploy an application using Python.

Course 09: Generative AI Code Creation, Image and Speech Recognition using GenAI

This asynchronous module helps you explore how to solve business problems by generating code using Generative AI tools, examine the capabilities of text-to-image and image-to-text GenAI tools like DALL·E through business use cases, and understand the speech recognition capabilities of audio-to-text GenAI tools like Whisper in practical business applications.

Course 10: Computer Vision & CNN

This module introduces you to the world of computer vision, helps you understand image processing and various methods for extracting informative features from images, and guides you in building Convolutional Neural Networks (CNNs) to uncover hidden patterns in image data and solve image classification problems at your own pace.

Course 11: Probablity & Hypothesis Testing

This module helps you perform statistical analysis using Python to evaluate the reliability of business estimates through confidence intervals and hypothesis testing. You will learn to analyze data distributions, test assumptions before committing resources, and make informed decisions based on data-driven evidence.

Course 12: SQL & DB

This module helps you understand the core concepts of databases and SQL, gain hands-on experience writing simple SQL queries to filter, manipulate, and retrieve data from relational databases, and use advanced SQL techniques such as joins, window functions, and subqueries to solve real-world data problems and extract actionable business insights.

Course 13: Capstone

This module introduces you to recommendation systems and guides you in building models that leverage past product purchase and satisfaction data to deliver high-quality, personalized recommendations.

Advance Module

Business Stats, SQL, Advanced Machine Learning and MLOps, Neural network, Computer vision & Capston projects

Year 2

Term 1

IT 721 : APPLIED RESEARCH TOPICS IN DEEP LEARNING THEORY & PRACTICAL APPLICATIONS: In this course, you will master CNNs, RNNs, LSTMs, autoencoders, and state-of the-art generative models like GPT, PaLM, CLIP, and DALLÂ·E and gain the industry-critical skills of transfer learning, prompt engineering, and RAG & LoRA fine-tuning to create domain-specific AI systems ready for real-world impact.Database storage technologies have transformed into complex systems that support knowledge management and decision support systems. This course takes a look at the foundations of database storage technologies. Students will learn about database storage architecture, types of database storage systems (legacy, current and emerging), physical data storage, transaction management, database storage APIs, data warehousing, governance and big data systems. The student will tie this all together to see how database storage technologies apply to data analytics. Introduction to Deep Learning Neural Networks & Backpropagation CNN, RNN, LSTM Autoencoders & Generative Models Transfer Learning Prompt engineering basics Foundation Models GPT, PaLM, CLIP, DALLÂ·E RAG, LoRA IT 547 : DATA STORAGE TECHNOLOGIES: Database storage technologies have transformed into complex systems that support knowledge management and decision support systems. This course takes a look at the foundations of database storage technologies. Students will learn about database storage architecture, types of database storage systems (legacy, current and emerging), physical data storage, transaction management, database storage APIs, data warehousing, governance and big data systems. The student will tie this all together to see how database storage technologies apply to data analytics. Upon successful completion of this course, you will be able to: Evaluate different database storage technologies. Compare systems used in data analytics. Investigate legacy, current, and emerging systems. Assess database storage solutions through hands-on labs.

Term 2

BTC 771 : AI STRATEGY FOR LEADERS: The course integrates real-world case studies from industry leaders such as Tesla, Amazon, JPMorgan Chase, and Microsoft, providing students with insights into AI successes and challenges. Through case study analyses, discussions, and practical assignments, students will develop leadership strategies for AI integration, ensuring responsible and effective AI adoption in their organizations. AI Fundamentals and Business Applications AI Vision and Strategy Development Ethical AI Leadership Building AI-Ready Teams AI Tools and Technologies Data Governance and Compliance Measuring AI Impact and Risks The Future of AI Leadership QM 625 : Mathematics of Artificial Intelligence & Machine Learning This course introduces and explains the fundamental mathematical concepts that form the backbone of artificial intelligence and deep learning. It emphasizes a strong understanding of linear algebra and analytic geometry, which are essential for building and optimizing AI models. Learners will explore how these mathematical principles directly apply to modern algorithms and neural networks. By the end of the course, participants will gain the analytical skills needed to interpret and implement AI techniques effectively. Course Learning Outcomes: Upon successful completion of this course, students will be able to: Analyze the role of scalars, vector spaces, tensors, matrices, derivatives, and gradient descent in AI and ML. Apply fundamental linear algebra and its mathematics to solve problems in AI and ML. Evaluate and select the most effective optimization methods and their mathematics for specific AI and ML problems. Differentiate between the roles of probability in predicting future events and statistics in analyzing past events for their applications in AI and ML.

Term 3

QM 625 : CAPSTONE PROJECT: The Capstone Project provides the opportunity for integrating program learning within a project framework. Each student identifies or defines a professionally relevant need to be addressed that represents an opportunity to assimilate, integrate or extend learning derived through the program. The student will work with the Capstone Project Mentor to develop a proposal. After review and approval by the Capstone Project Mentor, the student will be authorized to complete the project.The student will present the completed project at the end of the semester.Upon successful completion of this course, you will be able to: Demonstrate the knowledge gained from the previous courses in the program. Write a formal research paper or conduct a detailed project. Apply the objectives of research to a practical information technology problem. Create a project plan to successfully present a solution/goal to the stated problem. Use research tools for an applied research paper or project. Evaluate the validity and reliability of statistics and other forms of research. IT 720 : APPLIED RESEARCH IN NATURAL LANGUAGE PROCESSING: (Only for Doctorate learners) This course is designed to provide students with advanced knowledge and practical skills in natural language processing (NLP) research and applications. Students will delve into cutting-edge techniques, methodologies, and tools used in NLP, with a focus on applied research and real-world use cases. Through a combination of lectures, hands-on projects, and literature review assignments, students will explore topics such as text classification, sentiment analysis, named entity recognition, machine translation, question answering, and more. Emphasis will be placed on understanding the underlying algorithms, evaluating model performance, and conducting empirical studies to address real-world NLP challenges. Upon successful completion of this course, students will be able to: Evaluate and apply advanced natural language processing models and architectures to solve domain-specific language tasks. Design, implement, and optimize NLP models using state-of-the-art algorithms, evaluation metrics, and experimental design principles. Critically analyze the ethical, societal, and fairness implications of NLP technologies, and propose responsible solutions. Develop NLP systems that extract, generate, and interpret structured and unstructured data for diverse real-world applications. Conduct applied NLP research by formulating hypotheses, implementing models, and interpreting results to advance the field.

Term 4

RES 712 : QUALITATIVE AND EXPLORATORY RESEARCH METHODS This course explores non-statistical forecasting and other qualitative research methods. Qualitative research methodologies have become more prevalent in research as a viable and valid form of inquiry, especially as they pertain to human behavior in organizations. Qualitative research techniques examined include ethno methodology; grounded theory; and phenomenological research. Nonparametric statistical analysis will also be examined. By the conclusion of this class, you will gain a solid foundation regarding the qualitative research approach and its various traditions along with their theoretical and applied constructs. This will allow you to prepare a qualitative problem for research as well as structure a valid qualitative research design for conducting the actual research itself (i.e., your doctoral dissertation or future research problems in your area of interest or specialization). RES 713 : QUANTITATIVE RESEARCH METHODS I DATA MANAGEMENT AND NON-EXPERIMENTAL: This course is a combination of quantitative research methods, multivariate statistics, and forecasting. The course assumes the doctoral student has had a graduate level statistics/quantitative methods course covering parametric statistics and hypothesis testing. DOCTORAL RESIDENCY I: This course is the first of three residencies. The residencies occur simultaneously with coursework throughout the student's doctoral journey. The intent of a residency experience is to provide students with a chance to connect directly with faculty/mentor and fellow students within the doctoral program. Students will attend information sessions, meet with faculty/mentors regarding subject matter and research methodology experts, and present their problem/purpose statement to a review board for feedback and direction. Outcome: Finalisation of project problem and purpose statements.

Year 3

RES 714 : QUANTITATIVE RESEARCH METHOD II

This course is designed to build an advanced body of knowledge (BOK) that will allow students to utilize an extensive array of complex statistical models, tools, and software applications in the analysis of numerical data. Additionally, students will be able to use these advanced techniques to perform predictive analytics. This course is designed to build upon the non-experimental methods and techniques explored in RES 713. The comparative method will be explored along with the issue of ecological inference, aggregate vs. group assessment, and data reduction. Students will then assess the three main traditions associated with the experimental approach-pre-experiment, quasi-experiment, and the "true" experiment. Advanced Non-experimental and Experimental Quantitative Research Methods Quasi-experimental Design and Analysis True Experimental Design and Analysis Research across Time and Space Explanation, Prediction and Simulation Big Data, AI, ML and other inductive Methods and Approaches

DOCTORAL RESIDENCY II

Transition from coursework to dissertation research. Develop your dissertation proposal, gain ethical research approval, and begin collecting data for your study.

DOCTORAL RESIDENCY III

Advance your dissertation research and writing. Analyze your data, draft your dissertation chapters, and receive ongoing feedback from your dissertation committee.

DISSERTATION COURSES

This dissertation program provides the structure and resources to complete your doctoral research and successfully defend your thesis. Dissertation I - Chapter 1: Develop a strong foundation for your dissertation. Learn to write a compelling introduction, craft a clear research question, and define your methodology. Dissertation II - Chapter 2: Understand the literature review. Explore relevant research, identify theoretical frameworks, and demonstrate your understanding of the existing scholarship. Dissertation III - Chapter 3: Focus on your research methodology. Refine your data collection plan, discuss data analysis techniques, and ensure ethical research practices. Dissertation IV- Chapter 4: Analyze your research data. Learn to interpret findings, draw conclusions, and identify potential limitations. Dissertation V - Chapter 5: Write your discussion and conclusion. Integrate your findings with existing literature, discuss the study's significance, and outline future research directions.

Master in-demand AI & ML tools

Get AI training with 27+ tools to enhance your workflow, optimize models, and build AI solutions

Python
SQL
NumPy
Pandas
Seaborn
Scikit-Learn
Keras
TensorFlow
Transformers
ChatGPT
OpenCV
SpaCy
LangChain
Docker
Flask
Whisper
ML Flow
Github
Gemini
Dall.E

Get certificates from the world's leading institutions

Earn Doctoral and Master's Degrees from the World's Leading Institution

Earn a World-Class Doctorate

Benefit from HLC accreditation and WES recognition, enhancing the credibility and international acceptance of your qualification.
Prestigious Credentials

Earn Doctoral and Master's Degrees from the World's Leading Institution.
Lifetime Alumni Status

Gain full alumni status from Walsh College and join an elite network of AI consultants, C-suite leaders, and entrepreneurs.

* Image for illustration only. Certificate subject to change.

Meet your faculty

Javed Katibai

Chassis System Architect - General Motors

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James Gerrity

PhD, Adjunct Associate Professor

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Dr. Dave Schippers

VP and Academic Dean

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Thomas Petz

CIO/COO, Assistant Professor of IT

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Christopher Heiden

Program Lead at IT, Associate Professor of Business Information Technology

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Michael Rinkus

DBA, Lawrence Technological University

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Drew Smith

PhD, Pacifica Graduate Institute

Know More
Abbas Raftari

Adjunct Instructor

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Kurt Godden

Senior Analytics Scientist - Ford Motor

Know More

Get industry ready with dedicated career support

1:1 mentorship from industry experts

Get 1:1 career mentorship from our industry experts to prepare for jobs in AI and ML
Interview prep with experts

Participate in mock interviews and access our tips & hacks on the latest interview questions of top companies
Resume & profile review

Get your resume/cv and LinkedIn profile reviewed by our experts to highlight your AI & ML skills & projects
Access to Great Learning Job Board

Apply directly to top opportunities from leading companies with Great Learning Job Board

Course Fees

The course fee is USD 12,550

Invest in your career

Benefit from HLC accreditation and WES recognition
Earn alumni status from Walsh College upon program completion
Master AI & ML to solve complex, data-driven business problems
Earn a ‘Dr.’ title & get recognised as a specialist in your field

Take the next step

00 : 00 : 00

Apply to the program now or schedule a call with a program advisor

Unlock exclusive course sneak peek

Application Closes: 4th Jul 2026

Talk to our advisor for offers & course details

Application process

Our admissions close once the requisite number of participants enroll for the upcoming batch . Apply early to secure your seats.

1. Fill the application form

Apply by filling a simple online application form.
2. Interview Process

Go through a screening call with the Admission Director’s office.
3. Join program

An offer letter will be rolled out to the select few candidates. Secure your seat by paying the admission fee.

Eligibility

Applicants must hold a 3 or 4-year bachelor’s degree or equivalent in any discipline with a minimum of 60% marks from a UGC-recognized university or institution. The medium of instruction must be in English.
No GRE/GMAT or any English proficiency test scores are required.

Limited seats available

Get recognised as a specialist in your field with a 'Dr.' title

Batch Start Date

Online · To be announced

Admissions Open

Apply Now

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Frequently asked questions

Program Details

Faculty, Curriculum and Projects

Eligibility, Admissions, and Fees

Career-Related Queries

Program Details

What is the DBA in Artificial Intelligence (AI/ML) program about?

The Doctor of Business Administration (DBA) in Artificial Intelligence and Machine Learning is a doctoral-level program designed to help professionals understand, apply, and lead AI and ML initiatives in business contexts. The program combines rigorous academic foundations with real-world applications, enabling learners to use AI and ML for data-driven decision-making and organizational leadership

Are the Master's Degree and Doctorate WES-approved?

Yes. Walsh College is recognized by the World Education Services (WES). Students can showcase their educational accomplishments with a verified WES report that is accepted and respected by licensing boards, academic institutions, and employers throughout the US and Canada.

What is the duration and mode of the DBA in the Artificial Intelligence and Machine Learning program?

The program is 3 years long and offered 100% online.

How is the Walsh DBA better than other doctorate degrees in Business Administration?

#1 Ranking for Best Online DBA Programs by Forbes; Awarded Top-Tier Global DBA Ranking by CEO Magazine ; WES recognized; Accredited by The Higher Learning Commission (HLC), a regional accreditation agency recognized by the U.S. Department of Education.

Why should you choose this DBA course in AIML?

The program is ranked among the Top 10 Best Online DBA Degrees, is 100% online, and integrates advanced AI and ML with business strategy and applied research. It is designed for professionals seeking doctoral-level expertise with real-world relevance

What is the structure of this program?

The Doctor of Business Administration (DBA) in Artificial Intelligence and Machine Learning is delivered in a 2-phase structure, designed to build advanced AI and ML expertise before progressing to doctoral research.

Phase 1: Postgraduate Program in Artificial Intelligence and Machine Learning (AIML)
Learners begin with a Postgraduate program in AIML delivered by an academic partner institution.

Phase 2: Doctoral Program in AIML (DBA)
Upon successful completion of the postgraduate phase, learners progress to the Doctor of Business Administration in AIML offered by Walsh College.

Credentials Awarded:
Graduates earn a DBA in Artificial Intelligence and Machine Learning from Walsh College, along with a Postgraduate Certificate from the partner institution associated with the learner's pathway.

What kind of certificate will students receive upon completion?

Upon successful completion of the program, learners are awarded a Doctor of Business Administration (DBA) in Artificial Intelligence and Machine Learning from Walsh College, along with a Postgraduate Certificate from the academic partner institution associated with their learning pathway.

For detailed information specific to your enrollment pathway, learners are encouraged to contact their Program Advisor.

What is the deadline to enroll in this program?

Admissions for the upcoming batch will close once we reach the required number of participants. Seats are limited and given out on a first-come, first-served basis. We encourage you to apply early to secure your spot in the program.

What possible assistance is provided to students in the final year of the dissertation?

The final year comprises a Dissertation, and to ensure students complete it without any challenges or rejection at a later stage, a Walsh faculty member will be assigned as a guide to show you the right direction.

What support does Walsh College provide to learners of the DBA Program?

The Doctor of Business Administration in Artificial Intelligence and Machine Learning (Walsh College) provides the following support to its learners:

Flexible Learning: The program is 100% online, allowing learners to balance their studies with professional commitments.
Capstone Projects and Dissertation Guidance: Learners undertake capstone projects in Year 1 and Year 2 and work with a capstone mentor, followed by a thesis (dissertation) in Year 3 under the supervision of faculty/mentors.
Alumni Status: Graduates receive alumni status from Walsh College, which can foster networking and career support.

What are the learning outcomes?

Upon successful completion of the DBA program, you will be able to:

Interpret and implement relevant AI and ML models to create new knowledge paradigms.
Propel the field forward by expanding upon existing literature.
Apply new-age AI techniques to solve real-world business problems.
Develop research methodologies in alignment with philosophical paradigms.
Generate fresh insights and offer justified recommendations.
Upskill yourself as an ideal AI innovator with the latest skills to extract actionable insights for growth.
Work as subject matter experts in your respective organizations and lead innovation/R&D projects.

Why is the DBA in AIML a better choice compared to other Doctor of Business Administration programs?

Unlike many programs that offer only basic AI exposure and limited career support, this DBA uniquely combines an MS, DBA, and PG Certificate with career guidance from Year 1. Backed by recognition from the Higher Learning Commission and World Education Services, it ensures strong academic rigor and credibility, making it an exceptional choice for professionals seeking both AI expertise and career growth.

How many hours per week are required for the DBA program?

Learners are encouraged to review the program structure and discuss workload expectations with a program advisor.

Faculty, Curriculum and Projects

Who are some notable faculty members in this DBA program?

Name	Credentials/Background	Affiliation/Expertise
Dr. Abbas Raftari	Dual Ph.D., 25+ years at GM and Ford	Teaches advanced courses in Vehicle Safety, AI, and Data Security at Walsh College.
Kurt Godden	Senior Analytics Scientist	Ford Motor
Javad Katiibai	Assistant Professor - Quantitative Methods	Walsh College; Chassis System Architect - General Motors
Christopher Heiden	Program Lead at IT, Associate Professor of Business Information Technology	Walsh College
James Gerrity	Ph.D., Adjunct Associate Professor	Walsh College; Portfolio Associate and Business Service Manager - Morgan Stanley
Dr. Dave Schippers	VP and Academic Dean, Assistant Professor at Automotive Cybersecurity	Walsh College
Michael Rinkus	DBA, Lawrence Technological University; MA, Central Michigan University; BS, Wayne State University	Walsh College
Drew Smith	Ph.D. Pacifica Graduate Institute; MA, Pacifica Graduate Institute; BS, Rochester University	Walsh College

*Faculty list is indicative and subject to change.

What are the key subjects covered in the curriculum?

The program is structured across 3 years, progressing from foundational AI skills to advanced applications and research. For a detailed breakdown of modules and learning outcomes, learners are advised to refer to the official program brochure.

Are there hands-on projects included in the program?

Yes, the program includes Capstone Projects in Year 1 and Year 2, followed by a thesis in Year 3.

Is there a residency course component in the program?

Yes, the DBA curriculum includes Doctoral Residency I, II, and III courses.

What are the languages and tools covered in this program?

The languages and tools covered are Python, TensorFlow, NumPy, Matplotlib, Statsmodels, Keras, Scikit-learn, and Seaborn.

Eligibility, Admissions, and Fees

What are the eligibility requirements for this DBA program?

Applicants must hold a 3- or 4-year bachelor's degree (or equivalent) in any discipline from a UGC-recognized university.
The medium of instruction must be English.
No GRE/GMAT scores are required.

For clarification on individual eligibility, applicants are encouraged to contact their program advisor.

Are international learners eligible for this online DBA?

The program is delivered online and supports learners across regions. Eligibility and documentation requirements may vary, and applicants are advised to speak with a program advisor for details

Is this an online DBA suitable for working professionals?

Yes. The program is offered in a 100% online format and is designed to support professionals alongside their existing work commitments. The structure emphasizes flexible learning while maintaining doctoral-level academic rigor

What is the admission process?

Step 1: Apply online
Fill out a fast and easy application form. No additional tests or prerequisites are needed.

Step 2: Pre-screening
Our team will contact you by phone to confirm your eligibility for the program.

Step 3: Application assessment
The admissions team will assess your application and provide a timely response.

Step 4: Join the program.
If selected, you will receive an acceptance letter with instructions on how to pay and join the program.

Note: Admission to the program is subject to Walsh College acceptance.*

Does the applicant need a Master’s degree to start the DBA?

No. A Master’s degree is not required to enroll in the Doctor of Business Administration (DBA) in Artificial Intelligence and Machine Learning. Applicants are eligible with a 3- or 4-year bachelor’s degree (or equivalent) from a recognized institution, as outlined in the program eligibility criteria.

What is the DBA online program fee?

For the most up-to-date information on the course fee, please refer to the official program page here.

Career-Related Queries

What career advantages does a DBA in AI & ML offer compared to a PhD?

The DBA focuses on applied research, business leadership, and real-world AI implementation, making it suitable for senior professionals and industry leaders. In contrast, a PhD is typically more theory-oriented and academically focused.

What skills will I gain after completing the program?

The skills you will gain after completing the DBA program include:

Implementing AI and ML models
Applying AI techniques to solve business problems
Developing research methodologies
Generating insights
Implementing Strategic AI Leadership

Who typically enrolls in this program?

The cohort consists mostly of professionals from IT (47.6%), manufacturing (19.05%), finance, education, marketing, consulting, pharma, and other industries, with work experience ranging primarily from 3 to 15+ years.

*This data is indicative and may vary based on cohorts.

Will I receive alumni status after completion?

Yes, graduates receive alumni status from Walsh College.

Will I receive any career support after completing the program?

Yes, you will receive career support from Great Learning, India’s renowned ed-tech platform for professional development and higher education.

T&C valid*

Can I publish research papers during the DBA program?

Yes. The program includes capstone projects, applied research modules, and a doctoral dissertation, which support scholarly research and the development of publishable work (subject to approval, if required, before publishing any findings related to a company involved)

What are the career opportunities after I complete a DBA degree online?

Here are high-impact career roles typically pursued by graduates of a DBA in AI and ML: