Back
The Master's in Business Intelligence and Big Data at SUMMA University trains professionals capable of transforming large volumes of data into strategic knowledge for decision-making in modern organizations. The program develops advanced skills in data analysis, machine learning, and business intelligence, integrating approaches to data governance, advanced data mining, analytical visualization, and predictive modeling. All of this is addressed through applied use cases in key areas such as business management, marketing, and finance, ensuring practical training aligned with the needs of today's corporate environment.
Program Credit Hours
39 Credit hoursEstimated Completion Time
13 monthsThe Master of Science in Business Intelligence and Big Data strategically aligns with current trends in the digital business landscape, preparing graduates to turn data into actionable intelligence that drives organizational performance and transformation. With a strong emphasis on data-driven decision-making, the program develops expertise in modern data storage, processing, and visualization, as well as advanced data mining techniques—such as clustering, decision trees, and machine learning—to uncover patterns and generate predictive insights. Students strengthen technical foundations in data modeling, database design, and advanced SQL querying, while also learning to apply data science to key business domains such as marketing analytics, customer lifecycle analysis, and financial decision-making. The curriculum further addresses essential practices in data governance to support data quality, security, and availability, and explores how big data solutions can optimize business processes and enhance KPI management across diverse organizational contexts.
The Master of Science in Business Intelligence and Big Data prepares future professionals to design and implement robust data-driven strategies and business solutions, develop predictive models using machine learning frameworks such as TensorFlow and PyTorch, and communicate insights through storytelling and visualization to support strategic decisions. Students also gain exposure to managing and processing data in cloud environments and within secure mobile ecosystems, enabling them to respond to the operational realities of modern organizations. Aligned with the Social Learning Model MAS®, the program fosters adaptability to technological change, commitment to data-driven innovation, ethical data handling, and collaboration—competencies that support responsible leadership and impact in today’s rapidly evolving digital landscape.
The learning methodology is based on the "case method", a virtual course with interactive screens, virtual lectures, videos of the teacher, virtual review sessions and interactive exercises.
You will have weekly work planning and the personalized monitoring of an academic mentor. The teaching staff is made up of PhDs from the world of business and academia.
Business intelligence and big data have become essential capabilities for organizations seeking to improve performance through evidence-based decision-making, process optimization, and measurable results across finance, marketing, and operations. The Master of Science in Business Intelligence and Big Data prepares graduates to implement data governance practices that support data quality, security, and accessibility; apply advanced data mining and visualization techniques to generate actionable insights; develop and use machine learning models for business analytics and decision support; and design and manage relational and non-relational databases for efficient data processing.
Some professional opportunities include:
At the end of the course, the student will use the necessary tools and techniques in the storage, processing and analysis of large volumes of structured and unstructured information using the principles and practices of Data Governance, with the aim of achieving quality, security and accessibility of information, as well as making decisions based on data and maximizing the value of information in their organization.
Contents:
At the end of the course, the student will lead Big Data programs by developing use cases applied to business, in addition to establishing effective data strategies and employing data management, with the aim of converting an organization into a data-driven entity and showing the opportunities it offers.
Contents:
At the end of the course, the student will apply techniques of analysis and massive data processing in Big Data architectures, using work environments through supervised and unsupervised learning models, regressions and autoregressive time series, as well as decision trees and neural networks, in order to operate efficiently with large volumes of information in the digital age.
Contents:
At the end of the course, the student will evaluate the different programming languages in data science through practical work with the main Python libraries in data science and the analysis of Cloud Computing concepts, including cloud solutions such as Amazon AWS, Google Cloud and Microsoft Azure, in order to create websites, applications, programs and platforms with which different processes are streamlined.
Contents:
At the end of the course, the student will apply advanced Data Mining Techniques as well as business Data Analysis, experimenting with Data Mining Projects, from data preparation to evaluation of results, with the aim of applying models such as decision trees, regulations and neural networks to formulate informed hypotheses.
Contents:
At the end of the course, the student will apply business process monitoring and improvement techniques, using KPIs and data modeling, through the loading architecture in a DWH, ETL tools and transformation operations, with the aim of establishing more efficient and optimized data management in organizations.
Contents:
At the end of the course, the student will apply statistical modeling techniques in marketing use cases, using marketing data effectively through data science tools applied to marketing, in order to demonstrate the importance of ethics and privacy in the use of customer data in order to employ its correct handling.
Contents:
At the end of the course, the student will design data models for financial and management control departments, producing monitoring, in-depth and ad hoc reports for decision-making, through financial metrics, financial reports and report automation, with the aim of detecting areas for improvement in finance and in the efficiency and precision of financial decision-making.
Contents:
At the end of the course, the student will design data models using the Entity Relationship Model, applying normal forms in relational databases, demonstrating their ability to illustrate and practice key concepts in the creation and manipulation of structures, using calculation and verification techniques, with the aim of proposing innovative solutions in the field of data management.
Contents:
At the end of the course, the student will use structured data modeling and the SQL language through the design, creation and manipulation of data structures, the use of multiple tables and functions, in order to apply best practices in the implementation of relational databases, as well as achieve efficiency in the modeling of structured data.
Contents:
At the end of the course, the student will use artificial intelligence and machine learning in customer transformation and management, by analyzing relevant information sources, implementing programmatic advertising through DMP, in order to achieve business objectives efficiently and improve customer interaction in the context of digital transformation.
Contents:
At the end of the course, the student will use techniques and tools using non-relational databases and business solutions targeting this type of data. In addition to implementing distributed algorithms for scalability, applying feature engineering in event processing, building search engines and recommenders in order to apply innovative solutions in various business contexts related to unstructured information.
Contents:
"At the end of the course, the student will examine key metrics, detecting business opportunities and commercial actions, through the production of accurate and relevant reports, as well as the use of specific tools and methodologies, in order to establish clear objectives, facilitating strategic decision-making and business growth.
Contents:
"At the end of the course, the student will use the fundamental strategies and notions of data storytelling, through the application of tools such as Power BI and Tableau, as well as the transformation of information from various data sources, with the aim of using visual and compelling objects to implement a solid and persuasive narrative through the power of data.
Contents:
At the end of the training program, the student will develop a master's thesis in the area of Business Intelligence and Big Data, applying the knowledge acquired throughout the training program, carrying out an original work, analyzing relevant data and presenting significant conclusions.
Contents:
| Price per Credit: | US $168.05 |
| Total Price: | US $6554.00 / 39 credits |
| Registration Fee: | US $100.00 (non-refundable, one-time charge) |
| Graduation Fee: | US $110.00 |
| Return Check Fee: | US $40.00 |
| Official Transcript: | US $10.00 (each copy) |
| Withdrawal Processing: | US $25.00 |
| Books & Materials: | US $0.00 |
| Other costs: | US $0.00 |
8400 NW 36th St #450
Doral, FL 33166
USA
+1 (786) 885-5188
Mon – Fri from 8am until 5pm
Admissions: [email protected]
Academic Coordination: [email protected]
Registrar: [email protected]
Finance: [email protected]
Graduation Services: [email protected]
Download institutional