# Bachelor of Science in Mathematics and Modeling processes (with Data Science and Analytics)

Mathematics is not just a system of numbers and calculations; it provides a framework for complex reasoning which is a necessity for full and meaningful participation in society today.

In the 21st century, mathematics and mathematical sciences forms a key factor in modern communication, transportation, science, engineering, technology, medicine, manufacturing, security and finance. These subjects of are instrumental in the recent development of data Science, Artificial Intelligence, information technology, engineering, medical diagnosis, human genome, animation as well as in the computational modeling of natural catastrophes, traffic systems, climate change, and spread of diseases and pollutants. Therefore, if approached tactfully and with a positive attitude, mathematics can lead to a rewarding career capable of elevating you to high level professions.

The goal of this program is to advance the application of mathematics and computational science to engineering, technology, industry, science, business and society.

What is mathematical modeling?

Mathematical modeling is the art of translating problems from real life phenomena into tractable mathematical formulations whose theoretical and numerical analysis provides insights, answers, and guidance useful for any particular phenomenon. It is now viewed as a branch of mathematics. Today most real life problems can be described and solved by mathematical models which demand mathematical expertise. Steps involved in solving such problems include mathematizing a real-world problem, formulating it as a mathematical model, solving the model using a range of computational techniques, using technology to simulating solutions and validating the results.

In a nutshell, mathematical modeling is the art of translating problems from real life phenomena into tractable mathematical formulations whose theoretical and numerical analysis provides insights, answers, and guidance useful for any particularphenomenon.

Mathematical modeling enables humans to research in scientific areas previously inaccessible to us.

What are the advantages of Mathematical Modeling?

Mathematical modeling offers several advantages in proble-solving, decision-making and understanding complex systems. Some key advantages include:

1. Prediction and Forecasting: Models can be used to make predictions and forecasts about future outcomes. Data and models are used to generate projections and estimate the potential impact of different scenarios. This helps in making informed decisions and planning for the future.
2. Cost and time Efficiency: Modelling allows exploration of different hypotheses, tests ideas, conducting experiments in a cost-effective and time-efficient manner.
3. Optimization: Mathematical models can be used to optimize systems and processes by identifying the best possible solutions given a set of constraints and objectives. Optimization models helps in resource allocation, scheduling, production planning, inventory management,and so on and leads to improved efficiency and cost savings.
4. Communication and visualization: Models can facilitate communication and collaboration among interdisciplinary teams. They provide visual representation of complex concepts, making it easier to convey ideas, share findings and discuss potential solutions.
5. Exploration of “what-if”Scenarios. Mathematical models allows explorationof hypothetical scenarios.
6. Provides a bridge between theoretical concepts and real-world data.
7. Mathematical modelling supports for decision-making. They enable policymakers, managers and stakeholders to assess the potential consequences of different actions and policies.This includes tactical decisions by mangers and strategic decisions by planners.

What are the Career Opportunities for Mathematical Modelers?

Mathematical modeling is applicable in all fields (including natural sciences and engineering disciplines, social sciences, Economics and Finance, Marketing and arts). Some of the career opportunities include:

1. Data Science and Analytics: Mathematical modelers are in high demand in the field of data science and analytics. They analyze datasets, build predictive models, and provide insights for decision-making. They may work in projects involving machine learning, optimization and simulation.
2. Research and Development:Mathematical modelers can work in companies, organizations or academic institutions. They can contribute in the development of new models, algorithms and methodologies to solve complex problems in areas such as engineering, Physics, Biology, Economics and Social Sciences.
3. Financial Modelling: Mathematical modelers play a crucial role in the finance industry. They develop mathematical models to assess risks, analyze financial markets, and optimize investment strategies. They can work for banks, financial institutions, investment firms, or insurance companies.
4. Operations Research: Mathematical modelers are often employed in the field of operations research, where they apply mathematical techniques to optimize processes and systems. They may work on problems related to supply chain management, logistics, scheduling, transportation, or resource allocation.
5. Energy and Environmental Modelling: Mathematical modelers can contribute to the development of models and simulations for energy systems, renewable energy integration, environmental impact assessments, and climate change analysis. They may work for government agencies, research institutions, or consulting firms.
6. Healthcare and Epidemiology: Mathematical modelers are involved in healthcare and epidemiology to understand the spread of diseases, predict outcomes, and design interventions. They develop models for disease transmission, vaccination strategies, healthcare resource allocation, and policy analysis.
7. Academia and Teaching: Mathematical modelers canpursue careers in academia as professors or researchers. They can teach courses related to mathematical modeling, computational mathematics, or applied mathematics, and conduct research in their areas of interest.
8. Consulting: Mathematical modelers with expertise in a specific domain can work as consultants, providing their modeling and analytical skills to businesses and organizations. They may work onprojects related to process optimization, risk assessment, forecasting, or decision support.

Entry requirements

The basic minimum entry requirement shall be the minimum entry requirement set for the public universities, which is a mean grade of C+ (plus) in KCSE. In addition, candidates are expected to have attained at least a grade C+ (plus) in Mathematics and grade C (plain)in each of the following subjects:

Cluster subjects

• Any TWO Group II subjects, and
• Any Group III subject, or Group IV subject, or Group V subject, or another Group II subject.

KCSE Subject Categorization

GROUP I: Compulsory Subjects i.e. Mathematics, English & Kiswahili.
GROUP II: Science Subjects i.e. Physics, Chemistry & Biology.
GROUP III: Humanity Subjects i.e. Geography, History & Government, and Religious Studies.
GROUP IV: Technical Subjects i.e. Computer Studies, Agriculture, Home Science etc.
GROUP V: Business Studies, Music, French, German etc.