The MBA in Data Analytics at Presidential Graduate School, New Baneshwor, Kathmandu, is a two-year graduate business program affiliated with Westcliff University, USA. The program carries 60 credit hours across 20 courses.
The degree brings management study together with statistics, programming, database management, artificial intelligence and machine learning, big data analytics, data engineering, predictive analysis, and visualization. Students study how analytical methods can support business decisions while continuing with core MBA areas such as finance, marketing, organizational behavior, economics, leadership, information systems, strategy, communication, and research.
The program is suited to graduates who want business-management study with substantial quantitative and technology-based coursework rather than an MBA centered only on conventional management subjects.
| Particular | Details |
|---|---|
| Course | Master of Business Administration in Data Analytics |
| Short Form | MBA in Data Analytics |
| Level | Master's Degree |
| School | School of Business |
| College | Presidential Graduate School |
| Affiliation | Westcliff University, USA |
| Location | Thapagaun, New Baneshwor, Kathmandu, Nepal |
| Duration | 2 Years |
| Total Credits | 60 Credit Hours |
| Total Courses | 20 Courses |
| Graduation GPA | Minimum cumulative GPA of 3.0 |
| Main Areas | Analytics, statistics, Python, AI and machine learning, data engineering, databases and visualization |
Data analytics in business involves collecting, organizing, examining, interpreting, and communicating information so that managers can make better-supported decisions.
The MBA in Data Analytics combines this analytical work with graduate management study. Students do not study statistics or programming in isolation. The curriculum places analytical subjects alongside finance, marketing, managerial economics, organizational behavior, leadership, strategy, research, and information systems.
This combination matters because a technical result still has to be understood in relation to a business problem. A statistical model, database, visualization, or machine-learning output has value when its findings can be interpreted and connected with decisions involving customers, operations, markets, risk, resources, or organizational planning.
The degree therefore develops both the technical side of analytics and the management knowledge needed to use analytical findings within organizations.
Eligibility: Applicants should hold a bachelor's degree in any discipline from a recognized university in Nepal or another recognized university.
Applicants should have at least 45% or an equivalent undergraduate grade.
The program does not restrict entry to graduates of computing, statistics, or business. Students from different academic disciplines may apply when they meet the stated graduate admission requirement.
Admission includes the Presidential Aptitude and Attitude Test (PAAT) followed by an interview.
| PAAT Area | Assessment |
|---|---|
| Verbal and Reading | Verbal ability and critical reading |
| Quantitative | Quantitative ability |
| Reasoning | Logical reasoning |
| General Knowledge | General awareness and business/economics |
| Written Communication | Writing ability |
| Interview | Speaking, presentation, confidence and learning attitude |
The written PAAT includes multiple-choice questions and a writing section and has a stated duration of 2.5 hours.
The interview forms the second stage of the selection process.
Duration: 2 years
Credits: 60 credit hours
Courses: 20
Each course in the curriculum carries 3 credit hours. Students must complete the prescribed 60 credits with a cumulative GPA of at least 3.0.
The graduation structure consists of 48 credits of MBA core study and 12 credits of concentration study.
This structure gives students a substantial general-management base while using specialized courses to develop stronger analytical, programming, data-management, and predictive-analysis skills.
| Course | Credit Hours |
|---|---|
| Financial & Accounting Skills for Managers | 3 |
| Marketing Management | 3 |
| Organizational Behavior | 3 |
| Managerial Economics | 3 |
| Organizational Leadership | 3 |
| Managing Information Systems & Technology | 3 |
| Strategic Management in a Globalized Economy | 3 |
| Influential & Impactful Communication | 3 |
| Business Research Methodology | 3 |
| Statistical Analysis for Decision-Making Process | 3 |
| Big Data Analytics and Visualization | 3 |
| Virtualization and Storage | 3 |
| Python Programming | 3 |
| Applied Statistics for Optimization | 3 |
| Social Media and Web Analytics for Business | 3 |
| Artificial Intelligence and Machine Learning | 3 |
| Database Design and Management | 3 |
| Data Engineering | 3 |
| Regressions and Time Series Models | 3 |
| Applied Methods Capstone (Information Technology focused) | 3 |
The curriculum moves from general business management into more specialized analytical work without separating the two sides of the degree.
Statistical Analysis for Decision-Making Process provides part of the quantitative base of the program.
Managers regularly work with information involving customers, sales, operations, performance, markets, and financial activity. Statistical methods provide ways to examine that information systematically.
Applied Statistics for Optimization develops this analytical direction further. Students examine quantitative methods that can be used when comparing choices, evaluating results, or working with problems where resources and outcomes need to be assessed.
The curriculum therefore treats statistics as a practical decision-support discipline rather than only as a mathematical subject.
Python Programming introduces coding into the MBA curriculum.
Programming gives students a more direct way to work with data instead of relying entirely on pre-built reporting interfaces. It can also support later work involving statistics, machine learning, automation, and larger datasets.
Within this program, Python sits alongside management rather than forming part of a software-engineering degree. The purpose is to give students technical understanding relevant to analytical work and business intelligence.
This course also connects naturally with Artificial Intelligence and Machine Learning, Data Engineering, and the statistics subjects included in the degree.
Big Data Analytics and Visualization focuses on examining complex datasets and communicating findings in an understandable form.
Analysis alone is not enough for many management decisions. Results also need to be presented so that decision-makers can understand patterns, comparisons, and important changes.
Visualization therefore connects technical analysis with business communication.
The subject complements Statistical Analysis for Decision-Making Process and Social Media and Web Analytics for Business, where students encounter different kinds of information and different uses of analytical results.
Social Media and Web Analytics for Business applies analytical study to digital activity.
Businesses can generate substantial amounts of information through websites and social platforms. Studying this information can help students understand how digital interactions can be examined systematically.
The course adds a business-facing form of analytics to subjects concerned with databases, programming, statistics, and predictive modeling.
It is particularly relevant to the connection between analytics and marketing because online activity can form part of how organizations assess communication, audience behavior, and digital business activity.
Artificial Intelligence and Machine Learning introduces students to computational approaches that can identify patterns and support predictive analysis.
Within the MBA in Data Analytics, these topics are studied alongside statistics, Python, databases, data engineering, and business strategy.
The curriculum's learning outcomes include designing predictive models using machine learning, applied statistics, and time-series analysis to address business problems and examine possible market behavior.
This gives the AI component a clear analytical purpose within the degree rather than treating artificial intelligence as a separate management topic.
Regressions and Time Series Models provides focused study of methods used to examine relationships and changes in data.
Regression analysis can be used to study how variables relate to one another. Time-series analysis deals with observations collected over time and can help students examine trends and recurring patterns.
These methods connect closely with forecasting and predictive work.
Along with Applied Statistics for Optimization and Artificial Intelligence and Machine Learning, the course contributes to the program's quantitative and predictive-analysis component.
Analytics depends on more than statistical calculations. Information also has to be stored, organized, prepared, and made available for analysis.
Database Design and Management addresses the organization and management of data within information systems.
Data Engineering extends this work toward the processes and structures used to move, prepare, and manage data for analytical use.
Virtualization and Storage adds another infrastructure-related subject. Together, these courses give students exposure to the technical systems that support analytics behind reports, models, and visualizations.
One of the program's learning outcomes is the ability to design scalable data infrastructure and visualization pipelines for transforming large, unstructured datasets into useful business information.
The program remains an MBA despite its analytical concentration.
Students complete courses in Financial & Accounting Skills for Managers, Marketing Management, Organizational Behavior, Managerial Economics, Organizational Leadership, Managing Information Systems & Technology, and Strategic Management in a Globalized Economy.
These subjects give students a management base for interpreting analytical findings.
For example, a forecast may influence financial planning, a web-analytics finding may affect marketing decisions, and an operational analysis may have implications for employees or organizational strategy.
Analytics is therefore studied as part of business management rather than as a separate technical function.
Business Research Methodology develops skills for examining business questions systematically.
Students learn to work with research problems, evidence, analytical methods, and structured findings. Research skills also support the interpretation of quantitative results rather than relying only on software-generated outputs.
Influential & Impactful Communication provides a communication component within the degree.
This is relevant to analytics because findings frequently have to be explained to managers, clients, colleagues, or other stakeholders who may not work directly with statistical or technical methods.
The Applied Methods Capstone is an Information Technology-focused, 3-credit course.
It gives students an opportunity to combine management knowledge with analytical and technical study completed across the program.
Capstone work can draw on statistics, databases, programming, machine learning, visualization, data engineering, research methods, and business strategy.
The project component requires students to move beyond individual course topics and apply several forms of knowledge to a larger analytical problem.
By completing the program, students work toward skills in strategic business communication, advanced data analysis, business planning, analytical decision-making, predictive modeling, applied statistics, time-series analysis, database management, scalable data infrastructure, visualization, and strategic management.
The program also addresses ethical considerations in analysis and the communication of analytical information to business stakeholders.
Students develop these abilities through management courses, quantitative study, programming, analytics subjects, research, and capstone work.
The program can support preparation for work involving business analysis, data interpretation, reporting, analytical consulting, and organizational decision support.
Possible directions include Data Analyst, Business Intelligence Analyst, Data Consultant, Analytics Manager, and Strategy Analyst.
Career outcomes depend on a student's prior experience, analytical and technical ability, project work, employer requirements, and the responsibilities of individual positions. The degree does not by itself guarantee entry into a particular occupation.
Students attend the Presidential Graduate School campus in Thapagaun, New Baneshwor, Kathmandu.
The wider campus provides computer laboratories, research resources, the Center for Research, digital academic resources, the Writing Center, Career Services, workshops, guest sessions, and the Job Placement Cell.
For Data Analytics students, computing facilities can support programming and analytical work, while research resources can assist with assignments and capstone projects.
Career-related services cover areas such as résumé preparation, interview guidance, counseling, and professional-development sessions.
Duration: The program runs for two years.
Credits: Students must complete 60 prescribed credit hours.
The MBA in Data Analytics consists of 20 courses, each carrying 3 credit hours.
Eligibility: Applicants with a bachelor's degree in any discipline from a recognized university may apply when they meet the stated undergraduate academic requirement.
Yes. The admission process includes the Presidential Aptitude and Attitude Test (PAAT), followed by an interview.
Yes. Python Programming is included as a 3-credit course.
Yes. Artificial Intelligence and Machine Learning forms part of the curriculum.
Yes. Data Engineering is included alongside Database Design and Management, Virtualization and Storage, and Big Data Analytics and Visualization.
Yes. The curriculum includes Regressions and Time Series Models, Applied Statistics for Optimization, and Artificial Intelligence and Machine Learning.
Students must complete the prescribed 60 credits with a cumulative GPA of at least 3.0.
