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Course objectives and learning outcomes:

On completion of this course, students should be able to: 

• Have a clear understanding of the difference between high and low level programming languages, their respective advantages and disadvantages, 

• Demonstrate knowledge and understanding of the fundamental relationships between basic elements of computer architectures, machine-level languages and high- level languages, 

• Write programs in assembler language (with an emulator of the 8086 processor, and then with NASM) and include them in high level language programs, 

• Install and use a Linux distribution on their computer, 

• Install and use a virtual machine on their computer.

Course objectives and learning outcomes:

This course is a programming language-oriented one. It is focusing on a new language for the students, Java, and all its related concepts and paradigm. As a new language for the 2nd year students, some topics close to some of Programming I and Programming II are needed to introduce this new language. This course is covering the following topics: syntax, basic methods, algorithms, data-types, object-oriented paradigm, console software, graphical user interface software. Even if this course is focusing onto programming language, some good software engineering practices will be developed.

Course objectives and learning outcomes:
• Students will develop an understanding of sorting algorithms.
• Students will develop an understanding of basic sorting algorithms: bubble sort, selection sort, insertion sort.
• Students will develop an understanding of integer sorting algorithm: radix sort.
• Students will develop an understanding of advanced sorting algorithms: merge sort, heapsort, quick sort.
• Students will develop an understanding of algorithm design techniques: divide and conquer, greedy method, dynamic programming.
• Students will develop an understanding of graphs, graph traversals and graph algorithms.

  1. Subject name

  2. Course code

  3. Study program / Faculty

  4. Name of institution

  5. Cycle (1st cycle = undergraduate,

    2nd cycle = MSc, 3rd cycle = PhD)

  6. Academic year / semester

  7. Name of instructor

  8. Course prerequisites

Probability and Statistics

Compulsory course core for all faculties (ISVMA, MIR, CSE, CNS and ITA) University for Information Science and Technology, St Paul the Apostle, Ohrid First cycle


Professor Hossein Peyvandi, PhD
Successful completion of final high school examinations and pass university English proficiency exam.

3rd year / 6th semester

Number of EKTS credits :

  1. Course objectives and learning outcomes: On completion of the course students should be able to
    l Design different statistical experiments to solve using computers and calculators.
    l Summarize different measures of data using different experimental types and picture illustrations.
    l Know and transfer different types of probability and also calculate their values.
    l Be fully aware of different probability distributions and know how to transfer their meaning into values.
    l Identify the normal distribution understand the measure of spread associated with this data type.
    l Be fully competent with relations between data and use different methods of approximation to other measures. l Fully understand data estimations from different sizes of data regarding population proportion and variance.
    l Apply hypothesis testing to different population sizes of data and make valid conclusions and explanations.

  2. Course content:

l The course is designed to introduce different probability and statistical uses to develop firm knowledge and

understanding in the following areas:
l The nature of data and variation, uses and abuses of statistics, design of experiments.
l Describing and comparing data, types of data, summarizing data with frequency tables, pictures of data, measures of

central tendency, measures of variation, measures of shape, graphs and exploratory data analysis.
l Probability, rules for computing probabilities, probabilities through simulations.
l Probability distributions, random variables, expectation mean and variance, Bernoulli and binomial experiments,

multi-nomial experiments, Poisson distribution.
l The normal distribution, nonstandard normal distribution, finding probabilities, finding critical values.
l Relations between distributions, central limit theorem, law of large numbers, normal distribution as an approximation

to the binomial distribution, Poisson approximation to binomial distribution, normal approximation to Poisson.
l Point and interval estimates, method of moments and maximum likelihood estimation, estimating a population mean

for large and small samples, student's T distribution, estimating a population proportion and variance, confidence

l Fundamentals of hypothesis testing, testing a claim about a mean for large and small samples, testing a claim about a

proportion, testing a claim about a standard deviation or reference.

  1. Teaching methodology:
    Formal lectures and tutorials based on solving statistical problems together as a group.

  2. Total available time 6 ECTS credits x 30 hours/credit = 180 hours

  3. Time allocation

    Forms of instructional activities Other activity forms

  4. Grading 14.1


    Grades :

  5. Conditions for signature and formal exam

  6. Language of instruction

  7. Method of quality control/assessment

  8. References

    Mandatory references


1. J. Schiller and R. Srinivasan

Additional references


1. M. DeGroot and M. Schervish



Formal lectures: 15 weeks x 3 hours/week

45 hours

30 hours 105 hours

70 points + 25 points + 5 points (below)
70 points

25 points

5 points 5 (five) 6 (six) 7 (seven) 8 (eight) 9 (nine) 10 (ten)


Tutorials and laboratory work: 15 weeks x 2 hours/week


Self-study, Projects, Homework assignments

Final examination

Mid-term examination

Homework, tutorial assignments and tutorial tests


below 50 points

between 50 and 60 points

between 60 and 70points

between 70 and 80 points

between 80 to 90 points

above 90 points

Students must obtain a final course above 25 points.* English
Follow UIST Quality Assurance Procedures



Probality and Statistics


Y ear

Sept 2008

Y ear




Probability and Statistics

Pearson Education

(*) Minimum score allowed for continuation of a pre-requisite course and also to qualify to rewrite the exam.