COURSE SPECIFICATION FORM,
approved by the Academic Council 17.06.2015 (#39)
SECTION A:DEFINITIVE
Items in this section may be reviewed and developed within Schools as part of the Annual Program
Monitoring Process and in line with the Guidelines to Modifications to Programs and Courses.
1.
1.1
1.2
1.3
1.4
1.5
2.
General course information
School: School of Engineering and Digital
Credits (ECTS): 6
1.6
Sciences
Course Title: Applied Probability and Statistics
1.7 Course Code: ENG 201
Pre-requisites: Calculus II
Effective from: 2018
1.8 (year)
Co-requisites: none
Programs:
__Bachelor of Engineering_____________________
(in which the course
is
Core
Elective
Course description (max.150 words)
This course provides an introduction to basic probability theory and statistics. Topics include sample
spaces, events, classical and axiomatic definition of probability, conditional probability,
independence, expectation and conditional expectation, variance, distributions of discrete and
continuous random variables, joint distributions, central limit theorem, descriptive statistics,
confidence interval estimation, and hypothesis testing.
3.
3.1
3.2
3.3
3.4
4.
Summative assessment methods(tick if applicable):
Examination
3.5 Presentation
Term paper
3.6 Peer-assessment
Project
3.7 Essay
Laboratory Practicum
3.8 Other (specify)
Course aims
_Homework____
The course aims to equip students with:
1) basic probability theory and techniques of descriptive statistics
2) important continuous and discrete random variables
3) basics of inferential statistics
1
COURSE SPECIFICATION FORM,
approved by the Academic Council 17.06.2015 (#39)
5.
Course learning outcomes (CLOs)
By the end of the course the student will be expected to be able to
3
PLO 7
2
PLO 6
3
PLO 5
2
PLO 4
PLO 3
CLO 1: Describe various interpretations of probability and
the difference between discrete and continuous random
3
variables
CLO 2: List important continuous and discrete distributions. 2
CLO 3: Calculate descriptive statistics and summarize a
dataset
CLO 4: Calculate confidence intervals and conduct
hypothesis tests
PLO 2
PLO 1
Course Learning Outcome (CLO)
(1=Objective addressed, 2=moderately, 3=substantially)
The description of various PLOs is as given below:
ABET Program learning Outcomes (PLOs):
PLO 1. An ability to identify, formulate, and solve complex engineering problems by
applying principles of engineering, science, and mathematics
PLO 2. An ability to apply the engineering design process to produce solutions that meet
specified needs with consideration for public health and safety, and global, cultural,
social, environmental, economic, and other factors as appropriate to the discipline
PLO 3. An ability to develop and conduct appropriate experimentation, analyze and interpret
data, and use engineering judgment to draw conclusions
PLO 4. An ability to communicate effectively with a range of audiences
PLO 5. An ability to recognize ethical and professional responsibilities in engineering
situations and make informed judgments, which must consider the impact of
engineering solutions in global, economic, environmental, and societal contexts
PLO 6. An ability to recognize the ongoing need to acquire new knowledge, to choose
appropriate learning strategies, and to apply this knowledge
PLO 7. An ability to function effectively as a member or leader of a team that establishes
goals, plans tasks, meets deadlines, and creates a collaborative and inclusive
environment
2
COURSE SPECIFICATION FORM,
approved by the Academic Council 17.06.2015 (#39)
Mapping of the eight NU graduate attributes to the new program learning outcomes (this table is
fixed so no need to change):
2. Be intellectually agile, curious, creative and open-minded
3. Be thoughtful decision makers who know how to involve
others
4. Be entrepreneurial. Self-propelling and able to create new
opportunities.
5. Be fluent and nuanced communicator across languages
and cultures
X
X
X
X
6. Be cultured and tolerant citizen of the world
7. Demonstrate personal integrity
8. Be prepared to take a leading role in the development of
their country
PLO 7
X
PLO 6
X
PLO 5
PLO 3
X
PLO 4
PLO 2
1. Possess an in-depth and sophisticated understanding of
their domain of study.
PLO 1
NU Graduate Attributes
X
X
X
X
X
X
X
X
3
COURSE SPECIFICATION FORM,
approved by the Academic Council 17.06.2015 (#39)
SECTION B: NON-DEFINITIVE
Course Syllabus Template
Details of teaching, learning and assessment
Items in this Section should be considered annually (or each time a course is delivered) and amended as
appropriate, in conjunction with the Annual Program Monitoring Process. The template can be adapted
by Schools to meet the necessary accreditation requirements.
6. Detailed course information
6.1 Academic Year: 2022-2023
6.3
6.2 Semester: Spring
7. Course leader and teaching staff
Position
Name
6.4
Schedule(class days, time): Tue, Thu, 13:30 pm 14:45 pm
Location (building, room): 3E.224
Office
#
Contact information
Course Leader
Amin Zollanvari &
Behrouz Maham
3e542
3419
amin.zollanvari@nu.edu.kz
behrouz.maham@nu.edu.kz
Course Instructor(s)
Amin Zollanvari &
Behrouz Maham &
Yerkin Abdildin &
Yerbol Sarbassov
3e542;
3419;
3329;
3508
amin.zollanvari@nu.edu.kz
behrouz.maham@nu.edu.kzy
yerkin.abdildin@nu.edu.kz
Aigerim Baimyrza
Yerbolat Kalpakov
3229
3203
aigerim.baimyrza@nu.edu.kz
Teaching Assistant(s)
Graduate Teaching
Assistant
8. Course Outline
Session
Date
(tentative)
1.15 hrs Week 1
×2
1.15 hrs Week 2
×2
1.15 hrs Week 3
×2
1.15 hrs
×2
1.15 hrs
×4
Week 4
1.15 hrs
×2
Week 7
Week 5-6
Office hours/or
by
appointment
By appointment
By appointment
ysarbassov@nu.edu.kz
yerbolat.kalpakov@nu.edu.k
z
By appointment
By appointment
Topics and Assignments
Course Aims
CLOs
Sample spaces, events, classical and axiomatic
definition
Conditional probability: Bayes’ rule and the law
of total probability, independence
Random variables, Probability distribution
function, Probability mass function, Probability
density function, expectation, variance
Bernoulli, Binomial, Poisson Distributions and
Applications
Uniform, Exponential, Normal, Gamma
Distributions, Relation among exponential,
Poisson and gamma distributions
and Applications
Joint distributions: discrete and continuous
Marginal and conditional distributions,
independence
1
1
1
1
1
1,2
1,2
2
1, 2
2
1,2
1,2
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COURSE SPECIFICATION FORM,
approved by the Academic Council 17.06.2015 (#39)
1.15 hrs
×1
1.15 hrs
×2
Week 8
1.15 hrs
×2
Week 10
1.15 hrs
×4
Week 9
Week 11
Week 12
1.15 hrs
×2
Week 13
1.15 hrs
×2
Week 14
1.15 hrs
×2
Week 15
2 hrs
Week 16
Midterm
Covariance, correlation of random variables;
expectation of functions of random
variables, Linear combinations of random
variables, Central limit theorem
Summaries of Data, Histograms and Box Plots,
Scatter Diagrams
Spring break
Construction and interpretation of confidence
intervals, Confidence intervals for proportion,
mean, and variance, Sample Size Determination,
Applications
Hypothesis testing, Tests of Statistical
Hypothesis, one-sided and two-sided
Hypotheses, P-values in Hypothesis Tests
Connection between Confidence intervals and
hypothesis tests, Tests on the Mean of a Normal
Distribution
Review
1, 2
1,2
1,2
1,2
1
3
3
4
3
4
3
4
1-3
1-4
1-3
1-4
Final Exam
9.
1
Learning and Teaching Methods (briefly describe the approaches to teaching and learning to be
employed in the course)
Lectures and independent study
10. Summative Assessments
#
Activity
Assignments/quizzes
Midterm(s)
Final
11. Grading
Letter Grade
A
A-
Percent
range
95-100
90-94.9
Date
(tentative)
Before Spring break
During the final
period
Weighting (%)
20%
30%
50%
CLOs
1,2,3,4
1,2
1,2,3,4
Grade description (where applicable)
Excellent, exceeds the highest standards in the assignment of course
Excellent, meets the highest standards for the assignment or course
5
COURSE SPECIFICATION FORM,
approved by the Academic Council 17.06.2015 (#39)
B+
B
BC+
C
85-89.9
80-84.9
75-79.9
70-74.9
65-69.9
C-
60-64.9
D+
D
F
55-59.9
50-54.9
0-49.9
Very good, meets the high standards for the assignment or course
Good, meets most of the standards for the assignment or course
More than adequate; shows some reasonable command of the material
Acceptable; meets basic standards for the assignment or course
Acceptable; meets some of the basic standards for the assignment or
course
Acceptable; while falling short of meeting basic standards in several
areas
Minimally acceptable; falling short of meeting many basic standards
Minimally acceptable; lowest passing grade
Failing; very poor performance
12. Learning resources (use a full citation and where the texts/materials can be accessed)
n/a
E-resources, including,
but not limited to:
databases, animations,
simulations, professional
blogs, websites, other ereference materials (e.g.
video, audio, digests)
n/a
E-textbooks
n/a
Laboratory physical
resources
Special software programs n/a
n/a
Journals (inc. e-journals)
[1]. “Applied Statistics and Probability for Engineers”, 6th edition, Call
Text books
number: QA276.12 .M645 2014
[2]. Jay L. Devore, Probability and Statistics for Engineering and the
Sciences, 8th Edition, 2012.
[3]. J. Devroye, N. Farnum, J. Doi, Applied Statistics for Engineers and
Scientists, 3rd Edition, 2014.
[4]. Sheldon Ross, Introduction to Probability and Statistics for
Engineers and Scientists, Academic Press, 5th Edition, 2014.
13. Course expectations
List the expectations of students for the course regarding the course attendance, class participation,
group work, late/missed submission of assignments.
Attendance will be taken randomly throughout the term.
Late submissions of assignments may not be accepted.
There will be no ‘curving’ of grades.
Final exam is cumulative (i.e. may include material covered from the first day of classes).
According to the University policy, a student, who is late for 30 or more minutes, will not be
allowed to take the exam without permission of School Administration.
6
COURSE SPECIFICATION FORM,
approved by the Academic Council 17.06.2015 (#39)
Midterms and Final Exam are closed books and closed notes, though you are permitted to bring
one hand-written A4 sheet of notes, double sided, so formulas and expressions need not be
memorized. Calculators without communication capabilities will be allowed.
14. Academic Integrity Statement
Provide a statement requiring the students taking this course to abide by the University policies on
academic integrity.
You may refer to the Student Code of Conduct and Disciplinary Procedures (approved by the AC on
05.02.2014), specifically, paragraphs 13-16 (plagiarism and cheating).
Cheating is strictly prohibited.
15. E-Learning
If the content of the course and instruction will be delivered (or partially delivered) via digital and
online media, consult with the Head of Instructional Technology to complete this section and/or
provide a separate document complementary to this Template.
16. Approval and review
Date of Approval:
Minutes #:
Committee:
Date(s) of Approved Change:
Minutes #:
Committee:
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