Earn the most prestigious title in your career and develop leadership skills suited to today’s global business challenges. This online DBA program empowers you to innovate and lead at the highest levels.
Doctorate
Mar 31, 2025
36 Months
This course was designed to empower experienced professionals with advanced knowledge and research skills to enable them to drive innovation. Upon completion, learners will be awarded an DBA degree from Euro Asian, Geneva.
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Doctor Degree in Business Administration
1. Foundations of Data Science
Master probability distributions, statistical inference, hypothesis testing, Bayesian methods, regression analysis, multivariate statistics, time series analysis, and resampling techniques. Also, focus on statistical computing, data visualization, and understanding uncertainty to draw reliable insights from large, complex datasets.
Learn linear algebra concepts like vectors, matrices, eigenvalues, and singular value decomposition. In calculus, focus on derivatives, gradients, partial derivatives, optimization, and integrals. These are essential for machine learning algorithms, statistical modeling, and understanding large-scale data transformations and computations.
Focus on learning advanced data structures (trees, graphs, heaps, hash tables) and algorithms (searching, sorting, dynamic programming). Understand algorithmic complexity, optimization, and parallel processing. Emphasize real-world applications in big data, such as efficient data retrieval, clustering, and graph-based data mining.
Learn scientific computing tools like NumPy for numerical operations and Pandas for data manipulation. Focus on data structures, efficient computation, handling large datasets, data cleaning, and preprocessing. Mastering these supports advanced modeling, analysis, and algorithm development essential for your research.
2. Big Data Technologies
Understand Hadoop's architecture, HDFS for distributed storage, and MapReduce for parallel data processing. Study fault tolerance, scalability, data locality, and performance optimization. Explore Hadoop ecosystem tools like Hive, Pig, and HBase for efficient large-scale data analysis and real-world applications.
Understand the core architecture, components, and data processing models of Apache Spark and Flink. Learn their use in real-time and batch processing, fault tolerance, scalability, and performance. Focus on comparative analysis, use cases in big data analytics, and relevance to distributed computing for advanced data science research applications.
Understand the core architecture, components, and data processing models of Apache Spark and Flink. Learn their use in real-time and batch processing, fault tolerance, scalability, and performance. Focus on comparative analysis, use cases in big data analytics, and relevance to distributed computing for advanced data science research applications.
Understand data lake architecture, data warehousing models (e.g., Kimball, Inmon), ETL vs ELT processes, schema design, data governance, metadata management, scalability, real-time vs batch processing, integration with analytics tools, and how these systems support advanced data-driven decision-making.
3. Machine Learning & Artificial Intelligence
Understand key differences between supervised and unsupervised learning, including algorithms like regression, classification, clustering, and dimensionality reduction. Learn model evaluation, feature selection, and real-world applications. Focus on scalability for big data, handling high-dimensional data, and integrating learning methods with distributed computing frameworks relevant to advanced research in data science.
Learn neural network architectures (CNNs, RNNs, Transformers), backpropagation, optimization techniques, activation functions, regularization, and model evaluation. Understand deep learning frameworks (TensorFlow, PyTorch), large-scale data processing, and GPU acceleration. Explore applications in NLP, computer vision, and big data analytics. Focus on scalability, interpretability, and research trends in deep learning.
Learn reinforcement learning fundamentals, Markov decision processes, value and policy iteration, Q-learning, deep reinforcement learning, exploration vs. exploitation, reward engineering, scalability in big data environments, applications in recommendation systems, and integrating RL with neural networks and distributed computing systems.
Learn key model evaluation metrics (accuracy, precision, recall, F1-score, AUC-ROC), cross-validation techniques, bias-variance tradeoff, overfitting/underfitting issues, and hyperparameter tuning (grid/random search, Bayesian optimization). Understand model interpretability, performance benchmarking, and validation strategies essential for scalable, reliable, and ethical data-driven decision-making in big data and complex data science systems.
4. Data Mining and Pattern Recognition
Learn key clustering (e.g., k-means, DBSCAN) and classification techniques (e.g., decision trees, SVM, neural networks). Focus on algorithm selection, model evaluation, scalability in big data, real-world applications, and the theoretical foundations to develop innovative and efficient analytical solutions.
Focus on understanding algorithms like Apriori and FP-Growth, rule metrics (support, confidence, lift), handling large-scale datasets, pattern evaluation, scalability, and real-world applications in marketing, healthcare, and e-commerce for actionable insights and knowledge discovery from complex data.
Learn the theory and applications of dimensionality reduction techniques like PCA, t-SNE, and UMAP. Focus on feature selection, computational efficiency, handling high-dimensional data, visualization, model performance improvement, and preserving data structure for large-scale datasets in real-world, research-driven scenarios.
Learn about statistical methods, machine learning techniques, and deep learning approaches for outlier detection. Understand real-time detection in big data, anomaly detection in high-dimensional spaces, scalable algorithms, evaluation metrics, and applications in fraud detection, cybersecurity, and system health monitoring.
5. Advanced Statistical Modeling
Focus on understanding Bayesian inference concepts like prior, likelihood, posterior, Bayes' theorem, Markov Chain Monte Carlo (MCMC), model comparison, and hierarchical models. Learn how Bayesian methods apply to big data, uncertainty quantification, decision-making, and predictive modeling in complex data environments.
Learn time series decomposition, stationarity, autocorrelation, ARIMA, SARIMA, exponential smoothing, state space models, machine learning for forecasting, and handling high-frequency data. Understand seasonality, trends, anomalies, and their impact on predictions, especially for large-scale, real-time, and multivariate time series data.
Learn multivariate techniques like PCA, factor analysis, MANOVA, cluster analysis, and discriminant analysis. Understand dimensionality reduction, pattern recognition, correlation structures, and model assumptions. Focus on applying these to large datasets for insightful decision-making and predictive analytics in complex data environments.
Learn the theory behind Generalized Linear Models (GLMs), including link functions, exponential family distributions, model fitting, and regularization. Focus on applications in high-dimensional data, interpretability, diagnostics, and extensions like logistic and Poisson regression for real-world analytics and predictive modeling.
6. Big Data Analytics and Visualization
Learn data cleaning techniques, handling missing values, outlier detection, normalization, transformation, and feature engineering. Understand data integration, quality assessment, and preprocessing tools. Focus on scalable methods for big data, using frameworks like Hadoop and Spark for efficient data preparation at scale.
Learn about real-time data processing frameworks (e.g., Apache Kafka, Spark Streaming), stream data mining techniques, low-latency architectures, anomaly detection, scalability challenges, and applications in sectors like finance, healthcare, and IoT for informed decision-making.
Focus on learning advanced dashboard design, data storytelling, integration with big data sources, real-time data visualization, custom visuals, DAX (Power BI), Tableau calculations, and comparative analysis. Emphasize insights extraction, interactivity, automation, and scalability for research and decision-making.
Learn principles of effective dashboard design, emphasizing clarity, usability, and responsiveness. Understand user interaction patterns, data visualization best practices, and cognitive load management. Focus on integrating real-time big data, customizing interfaces for diverse users, and leveraging tools like Tableau or Power BI to support data-driven decision-making and actionable insights.
7. Cloud Computing for Data Science
Learn about the cloud platforms' data storage (S3, Blob, GCS), computing (EC2, Azure VMs, Compute Engine), big data tools (EMR, Databricks, BigQuery), ML services, scalability, cost optimization, and integration for large-scale data processing, analysis, and model deployment.
Focus on learning distributed file systems (e.g., HDFS), cloud storage architectures, data replication, consistency models, storage scalability techniques, data lakes, performance optimization, fault tolerance, cost-efficiency, and integration with big data tools like Hadoop, Spark, and NoSQL databases.
Learn about distributed computing frameworks like Hadoop, Spark, and Flink. Focus on parallel processing, fault tolerance, scalability, data partitioning, cluster management, and real-time analytics. Understand their architectures, programming models, and applications in large-scale data processing and machine learning workflows.
Learn about cloud security models, data privacy laws (e.g., GDPR), compliance frameworks (e.g., HIPAA, ISO 27001), encryption techniques, identity and access management, secure data storage, audit trails, risk assessment, and the impact of compliance on large-scale data processing and analysis.
8. Domain-Specific Applications
Learn about electronic health records, clinical decision support systems, genomic data analysis, biomedical signal processing, machine learning in healthcare, health data privacy, interoperability standards (HL7, FHIR), data integration, precision medicine, and ethical considerations in handling sensitive biomedical and health information.
Learn about financial modeling, predictive analytics, risk management, big data processing, algorithmic trading, machine learning in finance, real-time analytics, data visualization, decision support systems, and ethical considerations in financial data usage and privacy.
Learn about sentiment analysis, natural language processing (NLP), opinion mining, trend detection, social network analysis, topic modeling, misinformation detection, data preprocessing, real-time analytics, and ethical considerations in analyzing user-generated content across platforms.
Learn about the IoT architecture, sensor types, data acquisition, real-time data processing, and storage. Learn edge/fog computing, data cleaning, integration, and analytics techniques. Understand scalability, security, privacy issues, and use machine learning for predictive insights. Explore applications in smart cities, healthcare, and industry, linking sensor data to big data ecosystems.
Conduct original research to address a real-world business problem. Learn to formulate research questions, apply theoretical frameworks, and contribute to academic and professional knowledge. Every Learner will go through these following six simple steps to complete their Thesis with the help of a Professional Expert.
What Our Learners Have To Say About Us
Pursuing my Doctorate in Business Administration was more than just an academic pursuit—it was a transformational journey. The research support and global exposure helped me establish myself as a thought leader in strategic management.
Analyze how Amazon or Netflix navigated shifting market conditions through strategic foresight, innovative thinking, and effective change management. Examine key decisions, adaptations to technology and consumer behavior, and leadership in driving transformation. Highlight lessons in resilience, long-term vision, and innovation that enabled sustained competitive advantage.
Analyze Satya Nadella’s transformational leadership at Microsoft, focusing on how his leadership style influenced employee motivation and drove cultural change. Examine key initiatives, communication strategies, and leadership behaviors that reshaped the company’s vision, collaboration, and innovation. Evaluate outcomes through performance improvements, employee engagement, and organizational culture transformation.
Analyze how Apple maintained supply chain resilience during COVID-19, focusing on logistics optimization, risk management strategies, and supplier relationship management. Examine disruptions faced, Apple’s response, and lessons learned. Highlight how Apple adapted operations, diversified suppliers, and leveraged technology to ensure continuity and meet global demand during the pandemic.
In this case study, analyze Tesla’s approach to raising capital and taking financial risks. Evaluate its valuation methods, capital structure decisions, and strategic financial choices. Assess how these influenced growth, investor confidence, and market positioning, while considering implications for long-term sustainability and competitive advantage in the electric vehicle industry.
In this case study, analyze how Airbnb achieved rapid growth through disruptive innovation. Focus on its unique business model, how it scaled operations globally, and secured funding to fuel expansion. Examine key strategies, challenges faced, and the impact of innovation on the hospitality industry’s traditional dynamics.
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Analyze how Coca-Cola tailors its branding and marketing strategies to different regions using consumer psychology insights and data-driven approaches. Examine specific regional campaigns, cultural adaptations, and how consumer behavior influences branding decisions. Highlight the effectiveness of personalized marketing and the role of data in shaping Coca-Cola’s global yet local brand presence.
Analyze the Volkswagen emissions scandal by examining the ethical lapses, failures in compliance, and the role of the board. Evaluate how decisions were made, who was responsible, and how stronger governance could have prevented it. Recommend strategies to enhance ethical decision-making, regulatory compliance, and board accountability in corporate settings.
Frequently Asked Questions
This is a doctoral-level program for professionals who want to lead through research and
innovation. It blends academic depth with real-world impact, helping you turn workplace
challenges into meaningful, research-driven solutions.
Yes, absolutely. It's built with your schedule in mind. You can pursue this PhD alongside your
job, with flexible study hours and a structure that respects your work-life balance.
This is a blended program, primarily conducted online. You'll learn through a mix of live virtual
sessions, recorded lectures, guided mentorship, and independent research. No campus visits
required—unless you choose to attend optional events.
You’ll learn from globally recognized faculty—experienced researchers, tenured professors, and
industry experts. They’ll not only teach you but guide your research journey with real insight and
personalized attention.
Instead of a traditional thesis, you’ll work on a Practicum Research Project. It’s based on a real
issue from your work or industry. With your advisor’s help, you’ll research it rigorously and may
even publish it, depending on your goals.
Not at all. This PhD is designed for professionals, not career academics. You’ll be supported
through every research step—from forming questions to analyzing data—with practical
guidance tailored to your experience level.
Most learners complete the program in about 2.5 to 3 years, depending on how much time you
dedicate. The flexible design means you can move at your own pace, balancing study with your
personal and professional life.
Yes. The degree is awarded by Euro Asian University in Estonia, a recognized institution within
the European Higher Education Area. It holds academic value across Europe, the U.S., and
beyond.
Publishing is not required but highly encouraged. If your work has practical or academic value,
your advisor can guide you in submitting it to journals or presenting it at conferences.
The cohort includes senior executives, consultants, educators, entrepreneurs, and mid-career
professionals. Everyone brings unique experiences, making for rich peer discussions and
networking opportunities.
Whether you want to teach, lead strategic transformation, consult, or start your own research
firm, this PhD helps position you as a subject matter expert and decision-maker in your domain.
The application is simple. Share your academic and professional background, express your
research interests, and have a short conversation with our admissions team. From there, we’ll
guide you through every step
Our advisors are available around the clock to answer questions and support your educational journey. Connect with us today to explore how upGrad can help you meet your career goals.
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