CCMAS Course Search
Browse BRIDGE's courses under the National Universities Commission's Core Curriculum Minimum Academic Standards (CCMAS) — Nigeria's unified benchmark curriculum for every accredited program. Search by course title, code, faculty or programme to see full descriptions, learning outlines and credit-hour loads.
4,624
Courses
10
Faculties
168
Programmes
Showing 801–810
of 4,624 courses
DER 601
3 Unit(s) (LH 45)
At the end of this course, students should be able to: 1. demonstrate theoretical knowledge of dermatology; 2. list clinical skills in dermatology; 3. recognise common skin diseases especially in our environment; and 4....
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Physiology of the skin and appendages. Histochemistry of the skin. Superficial and deep fungal
infection, bacterial infection of the skin including parasitic infection of the skin. Papulo-
squamous skin disease. Connective tissue diseases. Common sebaceous gland diseases,
Common non-infectious diseases of the hair. Dermatitis. Contact eczema. Toxic dermatitis.
Dyshidrotic eczema. Viral infection of the skin, sexually transmitted diseases (including AIDS).
Pigmentary skin disorders, external manifestation of systemic diseases. Management of skin
disease in the community. Taking of skin scraping, staining slide for fungus identification,
planting of scraping and swabs from the skin for culture, skin-snip for filarial diagnosis, read
slides for fungal infection and other common parasitic infections of the skin. Take urethral and
vaginal swabs correctly for microscopic and culture examination. Take skin biopsies correctly
and preserve for histopathology.
DER 603
1 Unit(s) (PH 45)
At the end of this course, students should be able to: 1. conduct clinical evaluation, ward rounds and calls according to the contents of DER 601.
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Clinical evaluation, ward rounds and calls according to the contents of DER 601 in first
semester.
COM 511
1 Unit(s) (LH 15)
At the end of this course, students should be able to: 1. state the role of statistics in human biology and medicine; 2. describe collection and organisation of data and scales of measurement; 3. enumerate measures of ce...
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Introduction into the role of statistics in human biology and medicine. Collection and
organisation of data, and scales of measurement. Presentation of data. Measures of central
tendency and location (mean, median, mode, normal curve, skewed curve). Measures of
variability (dispersion) and standard deviation (SD) standard error (SE). Probability theory and
binomial distribution. Introduction of students to t-test, z-test, chi-square test. The statistical
significance of a difference. Association, correlation and linear regression. Estimating
population values. Planning of health survey. Design and interpretation of clinical trials.
STA 111
3
Upon the completion of this course, the students should be able to: 1. explain the basic concepts of descriptive statistics; 2. present data in graphs and charts; 3. differentiate between measures of location, dispersion...
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Statistical data: types, sources and methods of collection. Presentation of data: tables chart and
graphs. Errors and approximations. Frequency and cumulative distributions, measures of location,
partition, dispersion, skewness and Kurtosis. Rates, ratios and index numbers.
STA 111
3
At the end of the course, students should be able to: 1. explain the basic concepts of descriptive statistics. 2. present data in graphs and charts. 3. differentiate between measures of location, dispersion and partition...
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Statistical data. Types, sources and methods of collection. Presentation of data. Tables chart
and graph. Errors and approximations. Frequency and cumulative distributions. Measures of
location, partition, dispersion, skewness and Kurtosis. Rates, ratios and index numbers.
STA 111
3
At the end of the course, students should be able to: 1. explain the differences between permutation and combination; 2. explain the concept of random variables and relate it to probability and distribution functions; 3....
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Permutation and combination. Concepts and principles of probability. Random variables.
Probability and distribution functions. Basic distributions: Binomial, geometric, Poisson, normal
and sampling distributions; exploratory data analysis.
STA 111
3
At the end of the course, students should be able to: 1. explain the basic concepts of descriptive statistics. 2. present data in graphs and charts. 3. differentiate between measures of location, dispersion and partition...
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Statistical data. Types, sources and methods of collection. Presentation of data. Tables chart
and graph. Errors and approximations. Frequency and cumulative distributions. Measures of
location, partition, dispersion, skewness and Kurtosis. Rates, ratios and index numbers.
STA 111
3
At the end of the course, students should be able to: 1. explain the basic concepts of descriptive statistics. 2. present data in graphs and charts. 3. differentiate between measures of location, dispersion and partition...
View learning outline
Statistical data. Types, sources and methods of collection. Presentation of data. Tables chart
and graph. Errors and approximations. Frequency and cumulative distributions. Measures of
location, partition, dispersion, skewness and Kurtosis. Rates, ratios and index numbers.
STA 111
3
At the end of the course, students should be able to: 1. explain the basic concepts of descriptive statistics. 2. present data in graphs and charts. 3. differentiate between measures of location, dispersion and partition...
View learning outline
Statistical data. Types, sources and methods of collection. Presentation of data. Tables chart
and graph. Errors and approximations. Frequency and cumulative distributions. Measures of
location, partition, dispersion, skewness and Kurtosis. Rates, ratios and index numbers.
200 Level
STA 111
3
At the end of the course, students should be able to: 1. explain the basic concepts of descriptive statistics. 2. present data in graphs and charts. 3. differentiate between measures of location, dispersion and partition...
View learning outline
Statistical data. Types, sources and methods of collection. Presentation of data. Tables chart
and graph. Errors and approximations. Frequency and cumulative distributions. Measures of
location, partition, dispersion, skewness and Kurtosis. Rates, ratios and index numbers.