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Electronics I

This is a foundation course in analogue Electronics and is meant to provide a comprehensive overview of the scope and dynamics of electricity and the fact that electronic refers to a extremely wide range of electrical technology.  Students will be introduces to the building blocks of electronics such as the semiconductor, power supplies, operational amplifiers, attenuators and transducers.  Students will learn the theory and mathematics that govern the workings of the components that make up an electronic system.

Course Code: 
PHY204
No. of Credits: 
2
Level: 
Level 200
Course Semester: 
Second Semester
Select Programme(s): 
Science

Electricity and Magnetism

This course is an extension of the electricity and magnetism basics introduced in PHY102.  It is designed to improve students understanding of electric and magnetic phenomena.  The course covers basic computation of electric and magnetic fields, calculation of electric potentials and their applications.  A.C. theory and electromagnetic waves and their related calculations are covered. Application of RCL circuit is discussed.  

Course Code: 
PHY202
No. of Credits: 
2
Level: 
Level 200
Course Semester: 
Second Semester
Select Programme(s): 
Science

Methods of Teaching Biology

This course requires student-teachers to scaffold learning; practice how to promote active engagement of the learner; expose and discuss common misconceptions; organize the syllabus into schemes of work and further into lesson notes; use assessment as a means of advancing learning; develop effective and interactive teaching techniques and styles; and use collaborative rich tasks to engage science students in co-operative small group work.

Course Code: 
ESC208
No. of Credits: 
3
Level: 
Level 200
Course Semester: 
Second Semester
Select Programme(s): 
Science

Curriculum Studies in Biology

This course is designed to offer students the opportunity to discuss the Senior High School curriculum in biology, including the basic principles of curriculum development.  Students will be exposed to the factors that influence the development, design, implementation and evaluation of curriculum. 

This course introduces students to the various theories of curriculum development. It covers the various factors that influence curriculum development. Students will learn how to select and organize learning experiences. Curriculum implementation and evaluation will be covered in this course. Students will be exposed to how to interpret the biology syllabus.

Course Code: 
ESC214
No. of Credits: 
3
Level: 
Level 200
Course Semester: 
Second Semester
Select Programme(s): 
Science
Degree Type: 
Master of ScienceDepartment of Mathematics
Programme Duration: 
2 years (Standard Entry)
About Programme: 

In the case of MSc. Programme, without dissertation, students must select courses to attain a minimum of 30 credits of coursework.

The M.Sc programmes are over a twelve-month period involving two semesters of course work.

Objectives 

  • To produce pure mathematics graduates who can undertake research work and create new concepts.

  • To produce applied graduates who can use mathematics as a tool to do research work in other disciplines such as physics, biology and economics.

  • To provide a solid foundation for students to pursue advanced and specialised courses in the mathematical sciences.

 

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  2. Ahlfors,  L. (1979).  Complex Analysis, McGraw-Hill.

  3. Allan, J. (2002). Advanced Engineering Mathematics, Harcourt/Academic Press, USA.

  4. Allen L, J.S. (2007). An Introduction to Mathematical Biology,  Pearson Education, New Jersey, USA

  5. Anderson, A. & May, R.  (1991). Infectious Diseases of Humans: Dynamics and Control,  Oxford University Press, London. United Kingdom.

  6. Anderson, D. R., Sweeney, D. J. & Williams, T. A. (1988). An Introduction to Management Science: Quantitative Approaches to Decision Making; 5 Ed., West Pub. Co., USA.

  7. Anton, H. & Rorres, C. (1988 ). Elementary Linear Algebra, Applications Version, John Wiley, New York, USA.

  8. Axler, S. (1997).  Linear Algebra Done Right, Springer. 

  9. Bak, J. & Newman, D. J. (2010). Complex Analysis, Springer-Verlag, New York.                             

  10. Betts, J. T. (2001). Practical Methods for Optimal Control Using Nonlinear   

      Programming, SIAM, Philadelphia, USA.

  1.  Berenstein, C. A. (1985). Complex Analysis; Springer-Verlag, New York.

  2. Bick, T. A. (1971 ). Introduction to Abstract Mathematics; Academic Press.

  3. Birkhoff, G. and Rota, G. (1989).  Ordinary Differential Equations; John Wiley and Sons.

  4. Boyce, W. E. & DiPrima, R. C. (2006).  Elementary Differential Equations And Boundary Value Problems, Prentice Hall, New Jersey, USA.

  5.   Brauer, F. (2006).  Some Simple Epidemic Models, Mathematical biosciences and  

  6. Brauer, F., Castillo-Chavez, C. (2012). Mathematical Models for Communicable 

  7. Brian D, Hahn, (2007). Essential MATLAB for Scientists and Engineers, Pearson Education, South Africa.

  8. Broman, A. (1970). Introduction to Partial Differential Equations; Dover, USA.

  9. Brown, J. & Churchill, R. (1996). Complex variables and applications, 7th Ed. 

  10. Brown, J. W. & Sherbert, D. R. (1984). Introductory Linear Algebra with Applications, PWS, Boston.

  11. Bryson, A. E. & Ho, Y. (1975).  Applied optimal control: Optimization, Estimation  

  12. Budak, B. M., & Fomin S. (1973). Multiple Integrals, Field Theory and Series; Mir Publishers, Moscow.

  13. Burden, R. & Faires, J. D.  (2006), Numerical Analysis, PWS Publishers

Diseases, SIAM, Philadelphia, USA.  

  1. Capinski, M. &  Kopp, E. (2005), Measure, Integral and Probability,   Springer-Verlage London Limited.                

  2. Christian, P., Nagy, J. G.  Dianne & O’Leary, D., (2006), Deblurring Images, Matrices, Spectra, and Filtering. SIAM , Philadelphia, USA.

  3. Churchill, R. V. & Brown, J. W (1990 ). Complex Variables and Applications; McGraw Hill Inc., USA.

  4. Coddington, E.A. & Levinson, N. (1983), Theory of Ordinary Differential Equations; Robert Krieger Publishing Company, Malabar, Florida.

  5. Courant, R., & John, F. (1974). Introduction to Calculus and Analysis; Vol. 2, John Wiley and Sons, USA.                         

  6. Daellenbach, H. G., George, J. A. & McNicke, D.C. (1983). Introduction to Operations Research Techniques; 2 Ed., Allyn and Bacon, Inc., USA.

  7. Datta, B. N.  (2009), Numerical Linear Algebra and Applications, SIAM, Philadelphia, USA.

  8. David, C. L. (2002). Linear Algebra and its Applications, Addison-Wesley, New York, USA.

  9. De-Lillo, N. J. (1982). Advanced Calculus with Applications; Macmillan Pub., USA.

  10. Diekmann, O. & Heesterbeek, J.A. P.  (2000). Mathematical Epidemiology of Infectious Diseases, John Wiley & Sons, West Sussex.

  11. Edwards, C. H. & Penny, D. E. (2005).  Elementary Differential Equations With Boundary Value Problems, Prentice Hall, New Jersey, USA

  12. Edwards,  C. H. & Penney, D. E. (1999). Calculus With Analytic Geometry: Early Trancendentals; 5 Prentice Hall Inc., USA.

  13. Eisberg, R.M. (2000). Fundamentals of Modern Physics, John Wiley & Sons Inc. New York.      

  14. Evans, C. L. (2010). Partial Differential Equations, American Mathematical Society.                 

  15. Fiacco, A. V. &  McCormock, G. P. (1990). Nonlinear Programming, SIAM, Philadelphia, USA.

  16. Fraleigh, J. B. (1989). A First Course in Abstract Algebra.

  17. Froberg E. (1968). Introduction to Numerical Analysis, Addison and Wesley, USA.

 Philadelphia, USA.                        

  1. Gallian, J. A.  (1990), Contemporary Abstract Algebra; D. C. Heath and Company.

  2.  Gerald, C. F. & Wheatley (2001)  Applied Numerical Analysis; Addison &Wesley, USA.

  3.  Gibarg, D. & Trudinger, N. S. (1983). Elliptic Partial Differential Equations of Second  Order; Springer-Verlag, New York.

  4. Goldstein, H. (1986).  Classical Mechanics, Addison-Wesley Publishing Company.                  

  5. Haaser, N. B. & Sullivan, J. A. (1991). Real Analysis; Dover.

  6. Halmos, P.R. (1960), Measure Theory; Springer-Verlag, New York.

  7. Hertcote , H. W. (2000). The Mathematics of Infectious Disease, SIAM Review,  Amsterdam, The Netherlands.

  8. Higham , D. J.  (2005). MATLAB Guide, SIAM, Philadelphia, USA.

  9. Hilberland, F. B. (1962). Advanced Calculus for Application; Prentice Hall, USA.

  10. Hillier, F. S. (2012). Introduction to Operations Research, McGraw Hill, Inc., USA.

  11. Hirsch, M. W, Smale, S. & Devaney, R. L. (2004).  Differential Equations,         Dynamical Systems & An Introduction to CHAOS, Elsevier Academic Press,  

  12. Hocking, L. M. (1991), Optimal Control: An Introduction to the Theory with Applications, Clarendon Press, London.

  13. Hungerford, T. W. (1974). Algebra; Springer-Verlag, New York.

 Vol 42, No. 4, December 2000, pp. 599—653.         

  1. Igor G., Nash, S. G. & Sofer A., (2009). Linear and Nonlinear  Optimization, SIAM, Philadelphia, USA.

  2. Kaufmann, J. E. (1987). College Algebra and Trigonometry; PWS Publishers, USA.

  3. Kirk , D. E., (2004), Optimal control theory: An Introduction, Dover Publications.

  4. Klages, R. & Howard, P. (2008),  Introduction to Dynamical Systems, (Lecture         Notes Version 1.2), Queen Mary University of London.

  5. Kofinti, N. K. (1997). Mathematics Beyond the Basic; Vol. 1, City Printers, Accra.

  6. Kolman, B. (1984). Introductory Linear Algebra with Applications; Macmillan Publishing Company.

  7. Kreyszig, E. (1978 ). Introductory Functional Analysis with Applications; John Wiley and Sons,  New York, U.S.A.

  8. Kudryavtsev, V. A. (1981). A Brief Course of Higher Mathematics; Mir Publishers, Moscow.  

  9. La Salle, J. P.  (1976), The Stability of Dynamical Systems, SIAM, Philadelphia, USA.

  10. Lang, S. (2012). Calculus of Several Variables, Springer-Verlag, New York.

  11.  Lenhart S., & Workman J. T., (2007), Optimal Control Applied to Biological         Systems, Chapman & Hall, New York, USA.

  12.  Lenhart, S., & Workman, J. T. (2007). Optimal Control Applied to Biological, John Wiley & Sons, New York, USA.   

  13.  Levine, I.N.  (1991). Quantum Chemistry, 4th Ed., Prentice Hill.

  14.  Levy, A. B.  (2009). The Basics of Practical Optimization, SIAM, Philadelphia,            USA.

  15. Levy, A. B. (2009). The Basics of Practical Optimization and Control, SIAM, Philadelphia, USA.

  16.  Linz, P. &  Wang, R. (2002). Exploring Numerical Methods: An Introduction to Scientific Computing Using MATLAB, Jones & Bartlett Publishers, London.

  17. Lipschuts, S.  (1975), General Topology; McGraw-Hill Book Company.

  18. Liu, J. H. (2003). A First Course in the Qualitative Theory of Differential Equations, Pearson Education, Inc., New Jersey.

  19.  Luenberger, D. G., (1996). Optimization by Vector Space Methods, John Wiley & Sons, New York, USA.    

  20. Marion, J.B. & Thornton, S.T. (1995).  Classical Dynamics of Particles and Systems, Saunder College Publishers.

  21. Marsden, J.E. (1970). Basic Complex Analysis; W.H. Freeman and Co.

  22. McCann, R. C. Introduction to Ordinary Differential Equations; Harcourt Brace Janovich, USA.

  23. McCoy, N. H. (1968). Introduction to Modern Algebra; Allyn and Bacon Inc.,

  24. Merzbacher, E.  (1986). Quantum Mechanics, 2nd Ed. John Wiley & Son Inc.

  25. Morash, R. P. (1987). A Bridge to Abstract Mathematics; Random House Inc., New York.

  26. Munem, M. A. (1989). After Calculus: Analysis; Collier Macmillan Pub. , London.

  27. Nicholson, K. W. (1986). Elementary Linear Algebra with Applications; PWS-KENT.

  28.  Ortega, J. M. (1990), Numerical Analysis, SIAM, Philadelphia, USA.

 Philadelphia, USA.

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  2.  Offei, D. N. (1969). Some asymptotic expansions of a third-order differential equations; Journal of London Mathematical Society, 44 71-87.

  3. Penny, J. & Lindfield, G.  (1995), Numerical Methods Using MATLAB, Ellis Horwood, New York.

  4. Petrovsky, I. G.(1954 ).  Lectures on Partial Differential Equations; Dover, USA.

  5. Pinchover, Y. & Rubinstein, J. (2005). An Introduction to Partial Differential Equation, Cambridge University Press.

  6. Piskunov, N. (1981). Differential and Integral Calculus; 4 Ed., Mir Publishers, Moscow.

  7.  Pliska, S. R.  (2002). Introduction to mathematical finance: Discrete time models, Blackwell Publishers Inc. 

  8.  Poole, D. (2014). Linear Algebra: A Modern Introduction, Dover, USA.

  9. Priestley, H. A. (2003). Introduction to Complex Analysis, 2nd  Ed., OUP.

  10. Redheffer, R. (1992). Introduction to Differential Equations; Jones & Bartlett Pub., Inc.

  11.  Roberts, A. J.  (2009), Elementary Calculus of Financial Mathematics, SIAM, Philadelphia, USA.

  12.  Rofman, J. J. (2015). Advanced Modern Algebra, American Mathematical Society.

  13. Roman, S. (2005), Advanced Linear Algebra, 2nd edn; Springer-Verlag, New York.

  14. Ross, S. L. (1984). Differential Equations; 3 Ed., John Wiley & Sons, USA.

  15. Rudin, W. (1974), Principles of Mathematical Analysis; McGraw-Hill Book Company.

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  20. Smith, K. L. (1988). College Mathematics and Calculus With Applications to Management, Life and Social Sciences; Brooks/Cole Publishing Co., California, USA.

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California, USA.

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 Addison-Wesley Pub., Reading, USA.

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Zill, G. D. (2012). A First Course in Differential Equations with Modelling Applications, John Wiley and Sons.

Career Opportunities: 

Not Published

Entry Requirements: 

POST-GRADUATE ADMISSION REQUIREMENT

  • For the M.Phil/M.Sc in Mathematics, a good first degree in Mathematics, preferably in First Class or Second Class Upper Division, is required. 

  • For the Ph.D in Mathematics, an M.Phil  in Mathematics is required. 

Population Genetics and Evolution

Students are introduced to Polygenes and the Hardy-Weinberg law. The latter is illustrated by sickle cell anaemia, melanism in moths, drug resistance, insecticide resistance and mimicry in butterflies.  The course also examines the concept of evolution and the distribution of organisms in time and space.  It also reviews the theories of evolution, natural selection and evidence of evolutionary processes: fossils, geographical distribution, comparative anatomy, vestigial structures, molecular biology and embryology.  The origin of Man and the future of Man on earth are also discussed.

Course Code: 
BIO208
No. of Credits: 
3
Level: 
Level 400
Course Semester: 
First Semester
Select Programme(s): 
Science

Becoming a Reflective Science Teacher

This course is aimed at helping students to combine practice and theory to become a reflective science teacher. It will enable students to become innovative and flexible in their teaching practice. It will also guide them to draw on knowledge and experience, and relate them to what they do in practice.

Course Code: 
ESC313
No. of Credits: 
3
Level: 
Level 400
Course Semester: 
First Semester
Select Programme(s): 
Science

Computer Applications in Science Education

The course provides trainees’ opportunities to develop their Technological Pedagogical Content Knowledge (TPACK) and skills to design, enact and evaluate ICT-based lessons using a variety of ICT tools that support different teaching and learning strategies. Students will also be able to use Predictive Analytics Software (PASW) in analyzing data.

This course emphasizes the following: Application of Microsoft Office Suits in science teaching; Using of ICT tools for active learning of science (Blogs, Mind mapping tools, Interactive Simulations); Demonstration of skills in ICT in the delivery of science lessons; and PASW.

Course Code: 
ESC345
No. of Credits: 
2
Level: 
Level 400
Course Semester: 
First Semester
Select Programme(s): 
Science

Assessment in Science Education

The course is to equip students with the skills in assessing the cognitive, affective and psychomotor domains of behaviours of their prospective science students. It will examine, among others, the general science assessment techniques, characteristics of good science tests, different types of science test items, and continuous assessment of science students. It will also take a critical look at the current modes of internal and external examinations in science in Ghana.

This course emphasizes the following: nature of assessment of students; goals and learning objectives of instruction; characteristics of tests in science; planning of classroom tests and assessments; construction and validation of science assessment instruments; providing  guidelines for assembling and administering classroom tests; item analysis; interpretation of test scores obtained from students; and development of science performance-based assessment instrument.

Course Code: 
ESC 311
No. of Credits: 
3
Level: 
Level 400
Course Semester: 
Second Semester
Select Programme(s): 
Science

Instructional Laboratory Experience (ON-CTP)

In this course, students learn specific skills in a non-threatening environment, get feedback from peers and supervisors. The specific teaching skills and practices include questioning techniques, use of the chalkboard and other audio-visual resources, systematic presentation and lesson closure. Also, opportunities are provided for students to observe good models of teaching through video presentations and demonstration of specific teaching techniques.

Course Code: 
ETP390
No. of Credits: 
3
Level: 
Level 400
Course Semester: 
Second Semester
Select Programme(s): 
Science

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