Computer Mathematics – KASNEB Syllabus



This paper is intended to equip the candidate with the knowledge, skills and attitude that will enable him/her to apply computer mathematical approaches to solve business problems.


A candidate who passes this paper should be able to:

  • Perform binary arithmetic operations
  • Draw simple deductions and conclusions from given data
  • Use matrix algebra to solve real life problems
  • Solve basic linear equations
  • Relate probability and statistics to computing
  • Apply set theory in solving computing problems
  • Solve computer related problems using logic and truth table


Data representation and number systems

  • Computer codes: BCD, ASCII, EBCDIC
  • Bit, byte, nibble, word
  • Number systems; Decimal numbers, Binary numbers, Octal numbers, Hexadecimal numbers
  • Number conversions

Binary arithmetic

  • Addition, subtraction
  • Multiplication, division
  • Complements

Set theory

  • Introduction; definitions and purpose
  • Types of sets: Universal set, empty/null set, sub-sets, finite, infinite, power sets, partition
  • Description of sets; enumeration method and descriptive method
  • Operations: Union and intersection, complements, difference
  • Duality
  • Sets and elements
  • Venn diagrams
  • Ordered pairs, product sets, relations

Logic and truth tables

  • Introduction
  • Conjunction and disjunction
  • Negation
  • Proportions and truth tables
  • Tautology and contradiction
  • Logical equivalence

Elementary matrices

  • Introduction to matrices: definitions and importance of matrices
  • Matrix addition and subtraction
  • Dimensions/order of matrices
  • Types of matrices
  • Identity matrix
  • Matrix operations: addition, subtraction, multiplication, inversion of 2×2 matrices
  • Applications of matrices to business problems

Linear equations

  • Linear equations in one unknown
  • System of two linear equations in two unknowns

Elementary statistics

  • Sources of data: primary and secondary
  • Methods of collecting primary data: observation, interviews, questionnaires
  • Sampling methods; probabilistic and non-probabilistic
  • Data presentation: frequency tables and histograms
  • Measures of central tendency: arithmetic mean, mode, median
  • Measures of dispersion: range, mean deviation, standard deviation, variance, coefficient of variation

Introduction to probability

  • Definitions: events, outcome, experiment, sample space
  • Types of events: simple, elementary, mutually exclusive, mutually inclusive, dependent and independent
  • Laws of probability: addition and multiplication
  • Basic probability trees
  • Finite probability spaces and conditional probability

Emerging issues and trends

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