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INFO2101

Course Title: 
Probability and Statistics for Computing
Credits: 
3
Educational Level: 
II
Semester offered: 
II
Associated Programme: 
B.Sc. IT
Core Course: 
yes
Course Aims: 

This course introduces probability and statistics to students of Information Technology as well as the application of these concepts to the computing discipline. It examines the basic concepts of probability theory including counting and measuring and conditional probability and independence of events. It studies discrete, continuous, and joint random variables and functions of random variables. The course shows how to sum independent random variables, generate random numbers, and random event generation. It also discusses the Law of large numbers and the Central Limit Theory. The course also introduces linear and nonlinear regression, sampling distributions, confidence intervals, and hypothesis testing. The applications of these concepts to computing will be stressed throughout the course.

Syllabus: 
  • Describe the difference between stochastic and deterministic analysis;
  • Explain the purpose and nature of statistical sampling; Distinguish between the concepts of mean, median and mode, and discuss the drawbacks of each as a descriptive statistic;
  • Calculate the mean, median and mode of a given sample of data;
  • Calculate the standard deviation of a given sample of data;
  • Explain, with examples, the role of probability and statistics in IT;
  • Perform statistical analysis of a system’s performance;
  • Analyze a statistical analysis of a system’s performance and recommend ways to improve performance;
  • Randomness, finite probability space, probability measure, events;
  • Conditional probability, independence, Bayes’ theorem;
  • Integer random variables, expectation;
  • Formulation of hypotheses: null and alternate hypothesis;
  • Parametric and non-parametric tests and their applicability;
  • Criteria for acceptance of hypotheses, significance levels;
  • t-test, z-test, Chi-square test, and their applicability;
  • Correlation coefficients;
  • Linear and nonlinear regression models;
  • Stochastic versus deterministic analysis;
  • Purpose and nature of sampling, its uses, and applications;
  • Mean, median, mode, variance, standard deviation.
Course Assessment: 
  • Final exam (2 hours long)     60%
  • Coursework       40%
    • 3 assignments/quizzes     30% (10% each)
    • 1 in-course test (1 hour long)   10%

Students will be required to pass both the coursework and the final examination to pass the course.

Course Prerequisites: 

COMP1210 - Mathematics for Computing.

 

 

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