Programme Description
Mathematicians who have a high level of competency in computing and expertise in analytical and structured thinking are involved in problem modelling and solving. Mathematicians are in great demand in broad areas of employment that include but not limited to modelling and simulations, information security, data analysis and programming. They can also work as statisticians to develop techniques to overcome problems in data collection and analysis. They use statistical methods to collect and analyse data to address real world problems. Large manufacturing companies need statisticians to determine and analyse their quality control processes. There is a high demand of graduates with a good background in Mathematics and Statistics in the Tanzania, one of the emerging economies in the world. Hence, BSc. in Mathematics and Statistics programme aims at meeting the required demand, both within and outside Tanzania.
Learning Outcomes
- Demonstrate mathematical problem-solving skills for certain types of problems and their variants in a variety of mathematical and statistical contexts.
- Attain professionalism in making use of computer software and packages like SPSS, R, MATLAB, MINITAB, MAPLE, C, C++, and EXCEL., as vehicles for mathematical and statistical exploration.
- Design and analyse raw data with appropriate treatment of errors and uncertainties and form conclusions based on the statistical analysis.
- Achieve transferable and attitudinal skills through organization and integrity of work, value for intellect, respect for truth and professional ethics.
- Develop advanced skills in innovation and engage in entrepreneurship in the field of specialization.
- Exhibit professional excellence in teaching, research, industry, and consultancy in the related fields.
Programme Structure
Year 1
Semester One
| Code |
Course Title |
Status |
Credits |
| MT 1101 |
Foundation of Analysis |
Core |
9 |
| MT 1102 |
Linear Algebra and Applications |
Core |
10.5 |
| ST 1100 |
Statistical Computing |
Core |
9 |
| ST 1101 |
Basic Statistics |
Core |
9 |
| CP 111 |
Principles of Programming |
Core |
9 |
| DS 102 |
Development Perspectives |
Core |
7.5 |
| LG 102 |
Communications Skills |
Core |
7.5 |
Semester Two
| Code |
Course Title |
Status |
Credits |
| MT 1201 |
Mathematical Analysis I |
Core |
9 |
| MT 1202 |
Ordinary Differential Equations |
Core |
9 |
| EN 126 |
Statistical Methods in Economics I |
Core |
7.5 |
| ST 1201 |
Probability Theory |
Core |
9 |
| ST 1202 |
Operation Research I |
Core |
9 |
| ST1203 |
Basic Demographic Models |
Core |
9 |
| CP 123 |
Introduction to High Level Programming |
Core |
9 |
Year 2
Semester One
| Code |
Course Title |
Status |
Credits |
| MT 2101 |
Mathematical Analysis II |
Core |
9 |
| MT 2102 |
Numerical Analysis I |
Core |
9 |
| MT 2103 |
Graph Theory and Network Optimization |
Core |
7.5 |
| ST 2101 |
Probability Distributions |
Core |
10.5 |
| ST 2102 |
Statistical Inference |
Core |
10.5 |
| ST 2105 |
Research Methods and Practices |
Core |
7.5 |
| EN 216 |
Statistical Methods in Economics II |
Core |
7.5 |
Semester Two
| Code |
Course Title |
Status |
Credits |
| MT 2201 |
Complex Analysis |
Core |
9 |
| MT 2202 |
Partial Differential Equations |
Core |
9 |
| ST 2201 |
Sampling Theory and Methods |
Core |
10.5 |
| ST 2202 |
Regression Analysis I |
Core |
10.5 |
| ST 2204 |
Operation Research II |
Core |
10.5 |
| ST 2205 |
Time Series and Forecasting |
Core |
10.5 |
| CP 213 |
Data Structures and Algorithms and Analysis |
Elective |
9 |
| MT 2100 |
MATLAB and Problem Solving |
Elective |
9 |
| MT 2204 |
Numerical Analysis II |
Elective |
7.5 |
| ST 2206 |
Actuarial Statistics |
Elective |
9 |
Year 3
Semester One
| Code |
Course Title |
Status |
Credits |
| MT 3101 |
Abstract Algebra |
Core |
9 |
| MT 3105 |
Introduction to Measure Theory |
Core |
9 |
| ST 3101 |
Design and Analysis of Experiments |
Core |
10.5 |
| ST 3102 |
Introduction to Multivariate Analysis |
Core |
10.5 |
| ST 3104 |
Categorical Data Analysis |
Core |
7.5 |
| ST 3105 |
Stochastic Process |
Core |
9 |
| MT 3109 |
Practical Training |
Core |
7.5 |
Semester Two
| Code |
Course Title |
Status |
Credits |
| MT 3201 |
Basic Functional Analysis |
Core |
9 |
| MT 3205 |
Financial Mathematics |
Core |
9 |
| ST 2204 |
Non-Parametric Methods |
Core |
9 |
| EME 211 |
Entrepreneurship and Small Business |
Core |
9 |
| MT 3104 |
Discrete Mathematics |
Elective |
7.5 |
| MT 3106 |
Introduction to Simulation |
Elective |
9 |
| MT 3107 |
Introduction Mathematical Modelling |
Elective |
7.5 |
| CP 329 |
Big Data Analysis |
Elective |
9 |
| ST 3201 |
Biostatistics and Epidemiology |
Elective |
10.5 |
| ST 3203 |
Financial Statistics |
Elective |
10.5 |
Special Programme Requirements
• The first Field Practical Training (FPT) will be conducted at the end of the first academic year where students will be required to attend PT at The University of Dodoma (in-house practical training sessions) for four weeks. Students will be given practical training on some software on computing laboratory. The cost for this training per each student is TZS 300,000 for four weeks. • The second Field Practical Training (FPT) will be conducted in different government and private institutions for eight weeks at the end of the second 314 academic year. The cost for this training per each student is TZS 600,000 for eight weeks. • There will be Special Project (ST 320 Statistics Project) from the beginning of Semester II of Year III. The cost for the Special Project per student is TZS 300,000. • Special Faculty requirement will be in Semester I of Year I. The amount is TZS 100,000.
