The rapid expansion of digital technologies has resulted in an unprecedented growth in the volume, velocity, and variety of data generated worldwide. According to estimates by the International Data Corporation (IDC), the global datasphere is expected to grow from 64.2 zettabytes in 2020 to 181 zettabytes by 2025, illustrating the extraordinary scale and pace at which data are being generated and consumed. These are real time and instant data that may be obtained from different sources; such as web, scanners, social media, e-commerce, satellite images, and credit card transactions with a very huge volume at every matter of seconds resulting in the so-called “Big Data”. Industry estimates commonly suggest that approximately 80% of business enterprise data is unstructured, highlighting the growing challenge of extracting useful information from data that cannot be adequately handled by traditional structured-data approaches. This transformation has created a growing demand for professionals who can work across the full huge data lifecycle—from data collection and management to statistical analysis, predictive modeling, visualization, and business interpretation. Organizations increasingly require graduates who can combine statistical reasoning, computational skills, data science techniques, and business knowledge to transform diverse and complex datasets into actionable insights.

In response to these emerging demands, the Faculty of Economics at SIMAD University has introduced the Bachelor of Science in Data Science and Business Analytics. The programme integrates statistics, computing or technology, and domain of knowledge such as business, and economics to equip graduates with the competencies needed to address contemporary data challenges and contribute to effective strategic, operational, and evidence-based decision-making.

2. Programme Educational Objectives (PEOs)

Programme Educational Objectives describe the broad career and professional accomplishments that graduates are expected to attain. Graduates of the program will:

1. Apply rigorous statistical, mathematical, and computational methods to analyze complex data and business problems encountered in industry, government, and development contexts.
2. Build successful careers as data analysts, data scientists, or business-intelligence professionals across the public sector, private industry, financial services, telecommunications, and non-governmental organizations, in Somalia and internationally.
3. Design and support data-driven business and policy decision-making that accounts for Somalia’s and the wider region’s economic, social, and institutional context.
4. Pursue continuous professional and intellectual growth through certification, self-directed learning, or graduate study in data science, business analytics, statistics, economics, or related fields.
5. Practice their profession with high ethical standards, professional responsibility, and sound judgment regarding data privacy, security, and governance.

3. Program Learning Outcomes (PLOs)

Program Learning Outcomes describe what every graduate is expected to know and be able to do at the point of graduation. The ten PLOs below operationalize the five PEOs into assessable competencies. Accordingly, at the end of their studies, graduates of the program will be able to:

1. Apply probability theory, statistical inference, regression, and multivariate methods to formulate and solve data-driven problems.
2. Design, implement, test, and debug computer programs and data pipelines using Python and other modern programming tools.
3. Design, query, and manage relational databases and large-scale data-storage systems to support analytical workflows.
4. Build, validate, and deploy supervised and unsupervised machine-learning models for business, economic, and social data.
5. Apply distributed-computing and cloud-based platforms to store, process, and analyze large and complex datasets.
6. Create visualizations and written or oral reports that communicate data-driven findings clearly to both technical and non-technical audiences.
7. Apply economic and business principles to translate data into actionable insight for finance, marketing, supply-chain, and policy decisions relevant to the Somali and regional economy.
8. Evaluate the ethical, legal, privacy, and security implications of data collection, analysis, and algorithmic decision-making.
9. Conduct independent research using appropriate methodology and produce well-documented, reproducible analyses.
10. Demonstrate teamwork, professional responsibility, and a commitment to continuous learning within a rapidly evolving field.