AZERBAIJAN STATE OIL AND INDUSTRY UNIVERSITY ... - mba…mba.edu.az/mba/uploads/phd/all_syllabi_for_management.pdf · Kinds of frameworks or tools that could help us to capture great

AZERBAIJAN STATE OIL AND INDUSTRY UNIVERSITY

BA PROGRAMS/ PhD

SYLLABUS

Course unit title TEACHİNG METHODS

Course unit code

Type of course unit Compulsory

Level of course unit Third cycle PhD program

Year of study 1st year Fall 2018

Semester when the course unit is delivered

1st Semester

Number of ECTS credits

allocated

2

Name of lecturers Coordinator: PhD Gulnara Ahmadova

Class information

Location: Room 3

Time: Thursday 19.40- 21.00

Office hours: at any time according to students’ appointment Contact: [email protected]

Learning outcomes of the

course unit

Course Description

This course was developed from an “Active and Collaborative Learning” perspective. The active learning approach is based on collaborative, inquiry-

based, student–centered approach to teaching, in which students are actively

involved in their own knowledge acquisition. We are experiencing a paradigm shift in teaching and learning. Strategies for

effective learning are complex and bring into play many factors from the age

of the learner, prior experiences, learning styles, the medium of instruction, cognitive development, and cultural influences.

Many factors drive curriculum and delivery designs. In order to be an

effective educator, one must be able to link the theories behind the strategies

using evidence-based practice in order to maximize their effectiveness.

Learning outcomes of the course:

Instructional methods will include such collaborative educational models as

small and large group teaching, team-based, interactive and experiential case-

based learning. Techniques will include the use of simulations as well as teaching at the bedside with a focus on educator behaviors that stimulate

achievement of learners. With an appreciation of the diversity of the student

body, participants will effectively integrate and apply technology into instruction to develop and deliver health professions curricula including web-

based teaching environments, content management systems, collaborative

project development, and interactive media with an emphasis on instructional

design advancements which affect the learning environment. Evidence of participants’ knowledge and application of course topics will be captured in a

professional portfolio.

Mode of delivery (face-to-face, distance learning)

Face-to-face

Prerequisites and co-

requisites

None

mailto:[email protected]

Recommended optional

programme components

NA

Recommended or required

reading

Required Text: What makes great teaching? Review of the underpinning

research Robert Coe, Cesare Aloisi, Steve Higgins and Lee Elliot Major , 2014

Methods for Teaching Promoting Student Learning

David A. Jacobsen, Paul Eggen University of North Florida Donald Kauchak University of Utah USA,2009.

Additional materials for class discussions and lectures related to the theme

will be distributed in class

Planned learning activities

and teaching methods

The main objective of this course is to introduce the basic concepts,

theoretical perspectives, and practices by interactive lecturing, case study

discussions, presentation sessions, which are useful for understanding and improving performance

Language of instruction English

Work placement(s) NA

Course contents:

1 WHAT MAKES GREAT TEACHING

The six components of great teaching

Kinds of frameworks or tools that could help us to capture great teaching

Assessing teacher quality through multiple measures Six approaches to teacher assessment

3

2 GOOD PEDAGOGY AND ELEMENTS OF TEACHING EFFECTIVENESS .

Developing indicators of good pedagogy that can be used reliably. Types of

evidence relevant to ‘effectiveness’ . Examples of effective practices

3

3

FRAMEWORKS FOR CAPTURING TEACHING QUALITY..

Classroom observation approaches

3

4 VALUE-ADDED MEASURES Student ratings Teacher self-reports

3

5 HOW COULD THIS PROMOTE BETTER LEARNING?

Validity Issues Approaches to providing feedback Enhancing teachers’ professional learning

3

6 WAYS OF TAKING THIS FORWARD

Overview of the evidence A general framework for teaching quality

3

7

BRAINSTORMING 3

8 TECHNIQUES FOR EFFECTIVE BRAINSTORMING

3

9 PROFESSIONAL DEVELOPMENT. THE TEACHER’S ROLE Motivating

Students Learning Environments Influence Learning

3

10

DIVERSITY IN THE CLASSROOM

Accommodating Through Standards 21

3

THE THREE-PHASE APPROACH TO INSTRUCTION

11 THE TEACHER AS DECISION MAKER

Factors Influencing Decision Making

3

12 CLASSROOM MANAGEMENT: AN OVERVIEW

Planning for Effective Management

3

13 TECHNOLOGY IN THE CLASSROOM

Implementation and utilization Facilitating Communications

3

14 KEY TEACHING METHODS IN MASTERS EDUCATION

2

15 PROJECT PRESENTATION 1

FINAL EXAM

Student workload

Activities

Number

Duration (hour)

Total Workload

(hour)

Course duration in class (including Exam weeks)

14 3 45

Labs and Tutorials

Assignment

Project/Presentation/Report 1 1 1

E-learning activities

Quizzes

Midterm Examination 1 3 3

Final Examination 1 3 3

Self Study 8 2 16

Total Workload 68

Total Workload/30(h) 2.26

ECTS Credit of the Course 2


BA PROGRAMS/ PhD

SYLLABUS

Course unit title Academic Writing

Course unit code



Year of study 1st year

Semester when the course

unit is delivered

1 Semester

Number of ECTS credits allocated

6

Name of lecturers Coordinator: PhD Ahmadova G.B.

Class information Location: Room:

Time:

Contact: [email protected]


course unit

Course Description

Academic Writing The course combines а process

approach to writing (where students work оn invention,

peer response, editing, and writing multiple drafts) with а pragmatic approach to teaching the basics of writing (with

direct instruction оn such elements as topic sentences,

thesis statements, and outlines). Most of the students still have

gaps in their knowledge, gaps that become increasingly apparent as

they put language in writing form. This course will help the

students correct their problems.

Learning Outcomes of the Course: After completing Academic Writing students should be able to:

to study and discuss examples of English academic

writing,

to discuss their own academic writing and the writing of

their classmates,

learn how important the reader is to the writer,

know how to express clearly and directly what they mean

to write,

know important new words and phrases,

develop the their writing skills to enable them to respond to input

applying information to a special task, to elicit, to select ,to

summarize information in a range of writing activities, such as essay, articles, reports, summary, e-mail,

develop their ability to apply knowledge of the language system and

practice their writing skills in realistic situations.

Mode of delivery (face-to-face, distance learning)

Face-to-face


requisites

None




NA


reading

Academic Writing from paragraph to essay by Dorothy E Zemach Lisa

A. Rumisek, Oxford 2011



Classroom and case study discussions and brainstorming, feedback and

presentation sessions, discussion sessions


Work placement(s) ASOIU

Course contents:

1

Introduction: Process Writing

Understanding process writing, the writing method used in most

English-speaking university classes

1 Pre-Writing: Getting Ready to Write

Choosing and narrowing а topic

Gathering ideas

Editing ideas

Unit 1

2 2 The Structure of а Paragraph

The definition of а paragraph

The parts of а paragraph

Identifying and writing topic sentences

Unit 2

3 3 The Development of а Paragraph

Paragraph support and development

Writing concluding sentences

Peer editing

Unit 3

4 4 Descriptive and Process Paragraphs

Descriptive paragraphs and reasons for writing them

Organising and writing descriptive paragraphs using adjectives and

prepositions

Process paragraphs and reasons for writing them

Using transition words to write а process paragraph

Unit 4

5 5 Opinion Paragraphs

Distinguishing between fact and opinion

Organising and writing paragraphs expressing opinions and

arguments

Unit 5

http://www.goodreads.com/author/show/795912.Dorothy_Zemach

http://www.goodreads.com/author/show/8579648.Lisa_A_Rumisek

http://www.goodreads.com/author/show/8579648.Lisa_A_Rumisek

Using transition words to express cause and effect

Using modal expressions to make recommendations

6 6 Comparison / Contrast Paragraphs

Comparison / contrast paragraphs and reasons for writing them

Organising comparison / contrast paragraphs

Connecting words used for comparing and contrasting topics

Writing about the advantages and disadvantages of а topic

MIDTERM EXAM

Unit 6

7 7 Рrоblет / Solution Paragraphs

Writing about problems and solutions

Using first conditionals

Writing а two-paragraph text with linking phrases

Unit 7

8

8 The Structure of aп Essay

The definition of an essay

Formatting an essay

Writing а thesis statement

Unit 8

9 9 Outlining ап Essay

• The purpose of ап outline

• Writing an outline

Unit 9

10 1О lntroductions and Conclusions

• The purpose of an introduction

• Types of information in introductions

• The purpose of а conclusion

• Writing conclusions

Unit 10

11 11 Unity and Coherence

• The importance of unity in essay writing

• Editing an essay for unity

• The importance of coherence in essay writing

• Creating coherence

Unit 11

Student workload

Activities Number Duration

(hour)

Total Workload

(hour)

Course duration in class 15 3 42

Preparation for Midterm Exam 1 20 20

Individual or Group Work 14 5 60

Midterm Exam 1 3 3

Paper/Project (including preparation

and presentation) 1 10 10

Homework 5 3 15

Preparation for the Final Exam 1 30 30

Final Exam 1 3 3

Total Workload 183



12 12 Essays for Examinations

• Common instructions for essay tests

• Writing timed essays and managing time

Unit 12

FINAL EXAM


BA PROGRAMS/ PhD

SYLLABUS

Course unit title Organizational Behavior



Year of study Fall 2017

Semester when the course unit

is delivered

No of ECTS credits allocated 6

Name of lecturer PhD Gulnara Ahmadova

Class information

Location: Room 5

Office hours: at any time in accordance with appointment


Learning outcomes of the course unit

Course Description Leadership and Organizational behavior is a field of study that investigates

the impact of effective management of an organization and a clear

understanding of human behavior and social processes. As this course introduces psychological and behavioral principles, it

focuses on the understanding and managing people in organizational

process and at the same time it provides an opportunity for leaders to

change and improve the existing system and improve the performance of the organization.

Therefore, managers need to have a good understanding of behaviors due

to individual differences, group diversity, culture influences, organization structure, and organization values in relation to their job.

After learning of this course the students will be able to introduce the basic

concepts , theoretical perspectives, and practices for understanding of

actions and behaviors and improve performances and organization’s productivity in the organizations.

Learning outcomes of the course:

Students who successfully complete this course will be able to:

1. Identify leadeship behaviors and determine when and where they are

most appropriate.

2. Understand the role of personality in shaping attitudes and behavior

coordinate team decision making and problem solving

3. Bargain collaboratively with individuals and design motivational

programs for the1across groups 4. Asses, compare, and contrast organizational cultures

analyze organizational problems and opportunities, apply relevant

theory to the situation, and propose appropriate interventions 5. Define and explain horizontal and vertical relations in organizational

settings

6. Define and explain the implications of organizational culture and HR practices on individual behavior.

Mode of delivery Face-to-face


Prerequisites and co-requisites None



NA

Required reading

Required Text: :Organizational Behavior, Stephen P. Robbins, Timothy A.Judge.

Additional materials for class discussions and lectures related to the theme

will be distributed in class.

Planned learning activities and teaching methods

The main objective of this course is to introduce the basic concepts, theoretical perspectives, and practices by interactive lecturing, case study

discussions, presentation sessions, which are useful for understanding and

improving performance.



1.

2

3

4

5

6

7

8

9

10

11

12

13

14

Organizational behavior

Three goals of OB. Total quality management.

What is Organizational Behavior?

Foundations of Individual Behavior Values.Types of Values.

Attitudes.Job satisfaction.

Perception.Attribution theory.

Personality and Emotions

The Myers-Briggs Type Indicator.Personality.

Emotions. Motivation Concepts Motivation: From Concepts to Applications

Maslow’s hierarchy of needs theory. Theory X and Theory Y. Contrast reinforcement and

goal-setting theories.

Management by objective

Identify the four ingredients common to MBO programs

Outline the five step problem solving model in OB Mod

Define Quality Circles Describe the link between skill based pay plans and motivation.

Quiz

Individual decision making Six-step rational decision-making model.

Identify decision-making styles

Foundation of group behavior

Formal and informal groups. The importance of the Hawthorne and Asch studies. The benefits and disadvantages of cohesive groups. Contrast groupthink and groupshift.

MIDTERM EXAM

Contrast teams with groups. Demonstrate the linkage between group concepts and high performing teams. Four types of teams.

Communication

The communication process. Contrast the three common types of small-group networks.

Leadership theories. Traits, Styles and Behaviors Fidler’s contingency model. Path-goal theory.

Differentiate transformational from transactional leadership. Resolving conflicts

Define conflict

Functional and dysfunctional conflict. Quiz

Power and Negotiation Define power and political behavior.

Foundations of Organization Structure Work specialization, Departmentalization, Chain of Command Span of Control

Presentation Revision

FINAL EXAM

OB ch 1

OB ch 2 Leadership, ch2

OB ch 3 OB ch 4, ch5

OB ch 6

OB ch 7

OB ch 8

Leadership, ch8

OB ch9

OB ch 10

OB ch 12

OB ch11

Student workload


(hour)

Total Workload

(hour)




Midterm Exam 1 3 3


and presentation) 1 10 10

Homework 5 3 15


Final Exam 1 3 3

Total Workload 183




BA PROGRAMS/ PhD

SYLLABUS Course unit title Decision Analysis

Course unit code



Year of study


unit is delivered


allocated

6

Name of lecturer Oleg Huseynov

Class information

Location: Room

Time: Wednesday, Friday

Contact:

Office hours: upon appointment


course unit

Course Description

This course focuses on the application of decision theory to the

quantitative analysis of strategic decision problems. Strategic decision

problems, in either the individual or firm-specific context, generally

involve large amounts of resources that must be committed to alternatives

in competitive, risky and uncertain environments. Examples would

include corporate acquisition decisions, major capital investment

decisions, new product decisions, and choices among alternate

technologies. Many of these problems can be conceptualized and

structured using the methodologies associated with decision analysis. It involves a wide range of quantitative and graphical methods for

identifying, representing, and assessing alternatives in order to determine

the best course of action. DA is regularly employed by many leading

companies in the pharmaceutical, oil and gas, utilities, automotive, and

financial services sectors. In this module, you learn about the basic

concepts of DA and how to apply it in a variety of practical business

planning situations.

Learning Outcomes of the Course

After completing this course, students should be able to:

• recognise the inherent difficulties involved in making decisions

characterised by complexity and uncertainty

• identify alternatives together with their associated uncertainties and

payoffs.

• systematically structure, analyse and solve realistic problems using

decision analysis methods

• incorporate a decision maker's risk attitude into the selection of a

preferred alternative.

• demonstrate techniques for assessing the value of information.

The intended generic learning outcomes.

On successfully completing the module students will be able to:

- deconstruct complex problems

- apply analytical and numerical skills to identify appropriate solutions

- present their findings in a clear and structured manner

- plan work and study independently using relevant resources



requisites



NA

Recommended or required reading

1. Robert T. Clemen and Terence Reilly, Making Hard Decisions. Third Edition, South-Western, Cengage Learning, 2014

2. Clemen, R.T., Making Hard Decisions: An Introduction to Decision Analysis (2nd Ed.), Belmont: Duxbury Press 1996

3. Goodwin, P. and Wright, G. Decision Analysis for Management Judgment (4th Ed.), Chichester: Wiley 2009

Additional information will be distributed either electronically or delivered in

printed forms.



Classroom lecturing, assignment, discussion sessions, presentation.


Work placement NA

Course contents:

1 Introduction to decision analysis

Multi-Criteria Decision Making and Decision making under risk and uncertainty

Multi-Criteria Decision Making. The Structure of a Decision Problem. Alternatives. Criteria and Subcriteria. Pareto optimality

Chapter

2

Multi-Criteria Decision Making

Analytic Hierarchy Process (AHP) approach

TOPSIS approach

Shot overview of existing methods

Chapter

3 Multi-Criteria Decision Making

Single objective and multiobjective optimization. Pareto optimal front

Linear programming.

Chapter

4

Multi-Criteria Decision Making

Sensitivity analysis

Goal Programming

Chapter

5 Decision Making under Uncertainty

Statement of problem: Alternatives, states of nature, outcomes

Traditional classification of decision relevant information. Utility function concept

Chapter

Chapter

Criteria for decision making under uncertainty.

6 Decision Making under Risk

Criteria for decision making under risk. Risk Attitudes.

Value of additional information. Bayes theorem. Decision trees.

Chapter

7 Decision Making under Risk

Behavioral decision making. Gain and loss attitudes. Prospect theory

Chapter

8 Midterm Exam

9 Decision Making under Risk and advanced utility models

Imprecise probabilities. Multiple priors. Maximin expected utility.

Chapter

10 Decision Making under Risk and advanced utility models

Choquet expected utility

Cumulative Prospect theory

Chapter

11 Decision making under imperfect information

Four levels of decision relevant information: precise, interval, fuzzy or probabilistic, Z-

information

Computation with interval information.

Chapter


Computation with fuzzy information.

Computation with probabilistic information.

Chapter


Computation with Z-information.

Chapter


Fuzzy Expected Utility

Multiattribute fuzzy decision making

Chapter

15 Revision Chapter

FINAL EXAM

Student workload

Number

Duration

(hour)

Total Workload

(hour)




Midterm Exam 1 3 3

Paper/Project (including preparation and presentation)

1 15 15

Homework 5 2 10


Final Exam 1 3 3

Total Workload 183




BA PROGRAMS/ PhD

SYLLABUS

Instructor: ass.professor Farida Huseynova Office: ASOA,4th Floor, Tel. no: 4934538

Email: [email protected]

Class Time: 16:30AM -18:30 AM Location:Room: 434

Office Hours: by appointment

Textsbooks and Materials (Required) Leadership. (Robert N.Lussier), 2000

Additional readings will be distributed in class.

----------------------------------------------------------------------------------

Course Overview

Leadership as a field of study involving the key elements of leadership, leader profile, effective ways and

development of leadership skills, classifying the traits of leaders and styles of leaders and analyze

leadership behaviors and the factors influencing them. This course provides an introduction to the

fundamentals of individuals as leaders, team leadership, and organizational leadership.

COURSE OBJECTIVES The boundaries of leadership skills are expanding, and we will identify ways to improve them in

organization’s productivity. From the perspective of other organization members, we will discuss how to

lead effectively with individuals and groups. Students who successfully complete this course will be able to:

1. solve leadership dilemma and design different programs for themselves and coworkers.

2. identify leadership activities and determine when they are most appropriate. 3. understand the role of personality in shaping attitudes

4. coordinate team decision making and problem solving

5. bargain collaboratively with individuals and across groups

6. asses, compare, and contrast interpersonal leader’s communication problems.

II. Upon completion of the course, each student will be able to:

1. Analyze and apply leadership skills.

2. Propose and defend effective solutions to be a good leader.

3. To apply theoretical knowledges into a life.

P r o j e c t P r e s e n t a t i o n

There will be one project based on the one of the themes made in Power Points. Through this

assignment, you will design and develop a presentation and learn how to use it.

Assignments: All assignments are due in class on the date indicated. Assignments may be turned in before the due date.

Assignment must be hard copy. E-mail assignments will not be accepted. Late assignments will not be accepted.

Attendance: Attendance is at the discretion of the student. However, students who attend regularly and participate in class

generally do better in the course. In papers (or magazine) will be marked as absence.

Class Policies: No Visitors without prior approval please turn cell phones to SILENT FOR CLASS and OFF FOR ALL


Weeks Topic Assignments hours hours Reading Independent

work

1 Who is a leader? 2 1 Chapter 1. 3

2 Leadership managerial roles 2 1 Chapter 2 3

3 Leadership traits

2 1 Chapter 2

3

4 Ethics, values, & attitudes 2 1 Chapter 3 3

5 Leadership behavior and motivation 2 1 Chapter 3

3

6 Major motivation theories 2 1 Chapter 4

3

7 Power and influence 2 1 Chapter 4

3

8 Networking 2 1 Chapter 5 3

9 Contingency theories of effective

leadership

2 1 Chapter 5 3

10 Leadership Continium Theory 2 1 Chapter 6 3

11 Communicacation,coaching and

cobflict skills

2 1 3

12 Dyadic Relationship. Building a trust

2 1 Chapter 7

3

13 Groups, teams, and participative

leadership Teambuilding

2 1 Chapter 9

Chapter 10

3

15 Charisma and transformational

leadership

Strategic leadership

2 1 Chapter 12

3

16 F I N A L 3

Independent work-52 hours. The students weekly meetings with the tutor during the first 4 weeks. In the

foreground organizational questions stand to study orders, exam orders, training periods, time management,

etc.

From the 5th week the weekly professional seminars can be visited by the students according to demand.

The tutor(instructor) prepares the students for tests and the upcoming exam.

Evaluation – Assessments & Applications:

Throughout the course you will need to complete a series of assessments and application exercises. They

will help you prepare for class discussions and hone your managerial skill set.

SCHEDULE of ASSINMENTS

Assessments & Applications:

Setting the Stage

Readings

Quinn: Ch 1

Stein: “When You Fly 1st Class, It’s Easy to Forget the Dots” (Ulearn)

Post-class

Complete “Competing Values Self-Assessment” & ask 3 to 5 others to complete ”Competing Values

Leadership Assessment by Others” (Ulearn)

Getting the Best from Individuals: Mentor Role – Part 1

Readings

Quinn: Ch. 2

Roberts et al.: “How to Play to Your Strengths” (Study.Net)

Colvin: “What It Takes to Be Great” (Study.Net)

Brousseau et al. “The Seasoned Executive’s Decision-Making Style” (Study.Net)

Getting the Best from Individuals: Mentor Role – Part 2

Do “Using the Johari Window to Analyze Behavior” – p. 43 in Quinn

Do “Developing Your Reflective Listening Skills” – p. 53 in Quinn

Case 1 Exam: Hogan and Bradley (Ulearn)

Team Power Exercise

Getting the Best from Teams: Facilitator Role

Readings

Quinn: Ch. 3

Structure as an Enabler: Monitor & Coordinator Roles

Readings

Quinn: Ch. 4 & 5

Complete “Linking Critical Outcomes & Core Processes” – p. 123 in Quinn

Do “Developing Performance Metrics for Your Job” – p. 140 in Quinn

Case 2 Exam: Feed R&D or Farm It Out (Study.Net)

Understanding Leadership: Director & Producer Roles - Part 1

Readings

Quinn: Ch. 6 & 7

Kramer: “The Great Intimidators” (Study.Net)

Sprier et al.: “Leadership Run Amok” (Study.Net)

Goffee & Jones: “Why Should Anyone Be Led by You?” (Study.Net)

Goleman: “Leadership That Gets Results” (Study.Net)

Clawson: “Levels of Leadership” (Study.Net)

Complete “Origins of Personal Vision” assessment - p. 192 in Quinn

Complete “When Are You Most Productive & Motivated” assessment - p. 200 in Quinn

Understanding Leadership: Director & Producer Roles - Part 2

Do “Crafting Your Personal Vision Statement” - p. 200 in QuinnDo “Creating Your Own Strategy for

Increasing Personal Productivity & Motivation” - p. 236 in Quinn

Organizational Context & Political Realities: Broker Role

Readings

Quinn: Ch. 9

Student workload

Number

Duration

(hour)

Total Workload

(hour)




Midterm Exam 1 3 3

Paper/Project (including preparation and presentation)

1 20 20

Homework 5 2 10


Final Exam 1 3 3

Total Workload 183




BA PROGRAMS/ PhD

SYLLABUS

Course unit title STATISTICAL THEORY

Course unit code



Year of study


unit is delivered


allocated

6

Name of lecturers Rena Zulfugarova

Class information

Time:



course unit

Course Description

The course provides theoretical justification of and extensions to the statistical

inference theory from the master courses. In particular general decision theory is

discussed and applied to estimation and hypothesis testing. Furthermore

optimality of estimators, hypothesis testing and interval estimation, sufficient

statistics, equivariant (invariant) and Bayes and minimax estimators are treated.

In addition an introduction to asymptotic theory is given in particular

convergence in probability and convergence in distribution as well as results like

the law of large numbers and the central limit theorem. The inference theory is

exemplified on exponential families of distributions.

Learning Outcomes of the Course:

Obtain a deeper understanding and a considerable extension to the statistical

inference theory in the master courses. The course is helpful when developing

new statistical methodology.

Mode of delivery (face-to-

face, distance learning)

Face-to-face


requisites



PHStat Program, EXCEL

Recommended or required 1. Mathematical Statistics with Applications 7ed Dennis D. Wackerly William

mailto:zulfugarova.rena@

reading

Mendenhall III Richard L. Scheaffer ISBN-13: 978-0-495-11081-1 ISBN-10:

0-495-11081-7

2. David M. Levine, David F., Stephan Timothy, C. Krehbiel, Mark L. Berenson, STATISTICS FOR MANAGERS USING Microsoft Excel

Custom Edition for UMASS-Amherst Professor Robert Nakosteen

Taken from: Statistics for Managers: Using Microsoft Excel, Fifth Edition

by David M. Levine, David F. Stephan, Timothy C. Krehbiel, and Mark L.Berenson . by David M. Levine, David F. Stephan, Timothy C. Krehbiel,

and Mark L. Berenson.Copyright 2008, 2005, 2002, 1999, 1997 by Pearson

Education, Inc.Published by Prentice Hall Upper Saddle River, New Jersey 07458, ISBN 0-536-04080 X

3. Selected chapter on Business Analysis, Second Edition taken from Decision

modeling with Microsoft Excel, Sixth edition by Jefferey H. Moor and Larry R. Weatherford, Operations Management, Fourth Edition by Roberta

Russell and Bernard Taylor, ISBN 0-536-83481-4

Additional information will be distributed either electronically or delivered in

printed forms.



Classroom lecturing, assignment, discussion sessions, presentation.



Course contents:

1. Introduction to statistics [2] Chapter 1,2

2. Non-parametric descriptive statistics [2] Chapter 1,2

3. Introduction to statistical decision theory [2] Chapter 9

4. Decision making under ignorance: Maximax, Maximin

[2]Chapter 10,11

[3] Chapter 8

5. Decision making under risk; Expected value; Expected value of perfect

information; Creating payoff matrices

[2]Chapter 10,11

[3] Chapter 8

6. Probability Sampling, Bayes inference [1] Chapters 1- 6

7. Sampling Distributions, Estimation: methods, theory and properties [1] Chapters 7-10

8. Midterm

9. Hypothesis testing [1] Chapters 7-10

10. Confidence sets [1] Chapters 7-10

11. Inferences from small Samples. Tests of two populations. Comparing Two

related samples.

[2]Chapter 9

12. Chi-Square Goodness-of-Fit Tests [2]Chapter 9

13. Least squares estimators – method and properties

Interpreting simple regression models

[1] Chapters 11,16

[2] Chapters 10

14. Inferences for coefficients, conditional mean

Prediction Intervals

[1] Chapters 11,16

https://en.wikipedia.org/wiki/Descriptive_statistics

15. Forecasting models. Using of statistical packages

[2] Chapter 11 [3] Chapter 13

FINAL EXAM

Student workload

Number

Duration

(hour)

Total Workload

(hour)




Midterm Exam 1 3 3


and presentation) 1 20

20

Homework 5 2 10


Final Exam 1 3 3

Total Workload 183




BA PROGRAMS/ PhD

SYLLABUS Course unit title DATA ANALYSIS

Course unit code



Year of study 2nd year

Semester/trimester when

the course unit is delivered

4th Semester


allocated

6

Name of lecturers Coordinator: Prof. Dr. Rafik Aliev

Class information Location: Rooms: 1

Time: Office hours: by appointment

Contact: [email protected],


course unit

Course description:

“Data Science”. Data Science (DS) is a new,

exponentially-growing field, which consists of a set of tools and techniques

used to extract useful information from data. Data Science is an

interdisciplinary, problem-solving oriented subject that learns to apply

scientific techniques to practical problems. The course orients on practical

classes and self-study during preparation of datasets and programming of data

analysis tasks.

Course Objective:

1. To develop practical data analysis skills, which can be applied to

practical problems 2. To develop fundamental knowledge of concepts underlying data

science projects.

3. To develop practical skills needed in modern analytics. 4. To explain how math and information sciences can contribute to

building better algorithms and software.

5. To give a hands-on experience with real-world data analysis.

6. To develop applied experience with data science software, programming, applications and processes.

This course is aimed at providing our students with a solid DS training, which

could boost their careers in one of TOP10 mostly required professions in the


world. The course is based the most recent DS tools and developments,

brought to the students from the author working experience as a director of DS

research department in several IT companies. While the choice of DS, its

problems and projects already defines the novelty of this class, we are trying to

do our best to provide our students with the most up-to-date learning

experience: - The lectures are taught online – convenient to attend and follow.

Using the most current teaching software packages, the students can fully

interact with the instructor and classmates, share desktops, share applications,

record class videos, take online tests and quizzes. - The students work with

real-world data. Unlike more conservative science classes, we prepare our

students to solve real-world problems by working on these problems in the

class. - Independent work is appreciated. The class includes several mini-

projects, which each student has to design and implement on its own. -

Analytical skills should evolve during classes. Students will work with noisy

data, imperfect practices, human errors, diverse equipment. We teach our

students to take data as it is, and to make most efficient use of what’s

available.

Learning Objectives:

1. Data mining

2. Statistics

3. Machine learning 4. Information visualization

5. Network analysis

6. Natural language processing 7. Algorithms

8. Software engineering

9. Databases

10. Distributed systems 11. Big data

Teaching Outcomes:

The main outcome of this class is to train a student to do practical DS work.

Career-wise, we expect our students to be able to develop into skilled DS

researchers or software developers. After completing the study of the

discipline IDS the student should:

• Know basic notions and definitions in data analysis, machine learning.

• Know standard methods of data analysis and information retrieval

• Be able to formulate the problem of knowledge extraction as combinations

of data filtration, analysis and exploration methods.

• Be able to translate a real-world problem into mathematical terms.

• Possess main definitions of subject field.

• Possess main software and development tools of data scientist.

• Learn to develop complex analytical reasoning.

Recommendations to the students

This class is meant to be interesting, and it’s meant to help you unveil a

completely new area of human knowledge, supporting the basic course on

Data Analysis and Data Mining. It gives the opportunity to learn analytical

skills and tools instead of only leveling coding skills. To anyone thinking

about taking this class I would suggest the following: - Take it only if you

are interested in learning something new - Be prepared to work - Be

independent, and look for new, unusual solutions. - Do not miss/skip classes

and homework. First, homework grades will be responsible for the

bulk of your class grade. Second, each class is dedicated to a different area,

and you do not want to miss any of them.



requisites



-


reading

Required:

1) James, G., Witten, D., Hastie, T., Tibshirani, R. An introduction to statistical learning with

2) applications in R. Springer, 2013. 2. Han, J., Kamber, M., Pei, J. Data

mining concepts and techniques. Morgan Kaufmann, 2011. 3) Aliyev R.A. Fundamentals of the Fuzzy Logic-Based Generalized

Theory of Decisions

4) Aliyev R.A.- Uncertain computation-based decision theory 2018

Supplementary :

1) “Practical Data Science with R”. Nina Zumel, John Mount. Manning,

2014

2) “Data Science for business”, F. Provost, T Fawcett, 2013 Course reading is mainly composed of book chapters and articles. Additional

information will be distributed either electronically or delivered in printed

forms.



Lectures, class discussions (case study discussions and brainstorming), reading

material from textbook, course papers, exams.

Language of instruction

English

Work placement(s) -

Course contents

Topics for research work and class projects

- Constructing neural network for deep learning

- Statistical data analysis - Implementation of decision tree model

- Probabilistic clusteringk

- Fuzzy clustering

Detailed contents

Week 1 Introduction to data science

Week 2 Types of data information

Week 3 Introduction to machine learning

Week 4 Regression analysis

Week 5 Model selection and evaluation

Week 6 Classification , decision trees

Week 7 Probability theory

Week 8 MIDTERM EXAM

Week 9 Fuzzy and crisp data

Week 10 Clustering: c-means, ANFIS

Week 11 Text mining and informational retreival

Week 12 Relational databases

Week 13 Big data storage and retrieval

Week 14 Generalizing lecture

Week 15 Presentations of final project

FINAL EXAM

Student workload


(hour)

Total Workload

(hour)




Midterm Exam 1 3 3


and presentation) 1 13

13

Homework 3 4 12


Final Exam 1 3 3

Total Workload 184



Documents

AZERBAIJAN STATE OIL AND INDUSTRY UNIVERSITY ... - mba…mba.edu.az/mba/uploads/phd/all_syllabi_for_management.pdf · Kinds of frameworks or tools that could help us to capture great