This course covers general concepts underlying techniques within the confines of analytical, physical, inorganic, and organic areas of Chemistry. Advanced treatment of topics such as solvent extraction, distribution ratios, and the pH effects of solution among others will be undertaken in this course.
The course will emphasize the construction and analysis of DNA/genomic libraries, preparation of synthetic oligonucleotide probes, purification and radiolabelling of DNA and hybridization. The course also covers also covers DNA amplification using Polymerase Chain Reaction and sequencing of the amplified DNA, Recombinant DNA technology involving site-directed mutagenesis as well as transformation and expression in vectors and hosts will be discussed. Detection and analysis of expressed proteins from cloned genes will also be considered.
Principles and practices of genetic engineering and recombinant DNA technology, isolation and purification of DNA and RNA, restriction enzyme, ligation, blotting, hybridization and autoradiography will be reviewed. Other topics will cover cloning in bacteria and eukaryotes, DNA mini-preps and electrophoretic analysis of library colonies. Plants regeneration such as somatic and embryogenesis and organogenesis as well as culture types are also covered. Some broad and transgenic animals and their applications, in vitro fertilization and embryo transfer, cloning and its potential applications will be discussed.
This course is designed to introduce students to basic concepts in mathematical modelling. It also equips the students with mathematical modelling skills with emphasis on using mathematical models to solve real- life problems. Topics to be covered in this course includes: methodology of model building, problem identification and definition, model formulation and solution, consideration of varieties of models involving equations like algebraic, ordinary differential equation, partial differential equation, difference equation, integral and functional equations, Single species models (exponential, logistic and, the Gompertz growth models), interacting species models: (predator-prey models, competing species models, cooperating species models, multi-species models), the SI, SIR, SIS, SIRS and SEIR epidemic models, the basic reproduction number R0: derivation, interpretation and application to stability analysis of disease-free and endemic equilibria, and case studies: Malaria, HIV-AIDS, TB.
This is a supervised research practicum course. It is designed to give students an opportunity to plan a small research and carry it through. Thus, the course provides flexibility for students to design, execute, analyze, present, critique, and revise research projects. The student is free to use any research design – quantitative, qualitative or a mixed method. The research does not need to be the eventual research to be conducted by the student though the freedom of this work leading to the student’s ultimate doctoral research is permitted. It is expected that each student will submit a 10 to 15 page report of their study at the end of the semester. Technically, for a typical research practicum, there is no or minimal teaching of new content. Consequently, only the following two topics will be covered to improve students’ writing skills: How to review a research paper and development of conceptual/theoretical framework for research.
This course provides an overview of the components considered vital for leadership effectiveness. It is designed to prepare postgraduate science teachers to play leadership roles in the education system. Students will demonstrate a better understanding of the principles of science teacher education and supervision. Students will describe, practise and synthesize systematic steps required for supervision. This course will cover topics such as principles of professionalism for science educators; history of supervision; supervisory behaviours; principles of communication, observations, relationships and expectations (CORE); and tasks in supervision.
The course will equip student with adequate theoretical background, content and statistical tools and techniques required for analyses of quantitative research data. For each of the statistical tools and techniques the objective is to provide opportunities for students to develop a conceptual understanding of what that statistical tool is, when to use it (including the underlying assumptions and how to test them), how to use it, and how to interpret the results. Students will be exposed to the use of Predictive Analytics Software (PASW) and Microsoft Excel to run the various analyses. Topics include: The Power of Statistical Test; Point-Biserial Correlation; Multivariate analysis of variance – MANOVA, Analysis of covariance – ANCOVA; Analysis of covariance – ANCOVA; Scale Construction- levels of measurement, factor analysis, cyclical scale refinement; Multiple regression analysis; Structural Equation Modelling; Cluster analysis; Effect Size and Post Hoc Analyses; Various non-parametric statistics: Mann-Whitney, Wilcoxon, Friedman & Kruskal Wallis, Logistic Regression and Kendall’s concordance will also be discussed.
This course provides an introduction to basic computer programming concepts and techniques useful for Scientists, Mathematicians and Engineers. The course exposes students to practical applications of computing and commonly used tools within these domains. It introduces techniques for problem solving, program design and algorithm development. MATLAB (approximately 24 lectures): Basic programming: introduction to the MATLAB environment and the MATLAB help system, data types and scalar variables, arithmetic and mathematical functions, input and output, selection and iteration statements. Functions: user defined functions, function files, passing information to and from functions, function design and program decomposition, recursion. Arrays: vectors, arrays and matrices, array addressing, vector, matrix and element-by-element operations. Graphics: 2-D and 3-D plotting. Other topics to be covered are coding in a High Level Language using MATLAB/OCTAVE. At least one Computer Algebra System (CAS): MAPLE, MAXIMA MATHEMATICA, DERIVE will also be covered.
The course will expose students to the theories that underpin the qualitative and mixed methods research paradigms. It aims at the development of the knowledge and skills of students to enable them conduct a variety of qualitative and mixed methods studies aimed at improving teaching and learning of science in schools and other educational settings. It is expected that at the end of the course students will write a research proposal for a study that could be the focus of their thesis. Topics to be covered include: Various qualitative research approaches such as case studies, content analysis, ethnography, phenomenology, teaching experiments, and grounded research theories; Sequential and concurrent mixed methods approaches; Validity and reliability. Development of qualitative instruments, as well as data collection methods, and analyses will also be explored both manually and the use of the NVivo software.
Computer architecture, programme language, programme development and algorithms, interfacing, numerical methods in computing, application of filter design, Fourier analysis, digital filtering, fast Fourier transform.
