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 Concentrations

ersion 4 (July 2020)

  

Table of Contents

Concentration Title: Data Science and Analytics

Concentration Details

Eligibility

Concentration Description

Concentration Objectives

Concentration Students’ Learning Outcomes

Relationship of the objectives of the course and University mission and the Saudi Vision 2030

Summary of Developed Courses

Mapping between concentration courses and Students’ Learning Outcomes

Developed Courses

COURSE MATH 405: COURSE SPECIFICATIONS (DAD Template)

COURSE SPECIFICATIONS CHECKLIST

A. COURSE IDENTIFICATION AND GENERAL INFORMATION

B. COURSE DESCRIPTION, OBJECTIVES & LEARNING OUTCOMES

C. COURSE CONTENTS

D. TEACHING AND ASSESSMENT

E. OFFICE HOURS

F. LEARNING RESOURCES

G. Course Evaluation

H. NCAAA Program Accreditation Requirements (OPTIONAL)

COURSE STAT 413: COURSE SPECIFICATIONS (DAD Template)

COURSE SPECIFICATIONS CHECKLIST

A. COURSE IDENTIFICATION AND GENERAL INFORMATION

B. COURSE DESCRIPTION, OBJECTIVES & LEARNING OUTCOMES

C. COURSE CONTENTS

D. TEACHING AND ASSESSMENT

E. OFFICE HOURS

F. LEARNING RESOURCES

G. Course Evaluation

H. NCAAA Program Accreditation Requirements (OPTIONAL)

COURSE ICS 474: COURSE SPECIFICATIONS (DAD Template)

COURSE SPECIFICATIONS CHECKLIST

A. COURSE IDENTIFICATION AND GENERAL INFORMATION

B. COURSE DESCRIPTION, OBJECTIVES & LEARNING OUTCOMES

C. COURSE CONTENTS

D. TEACHING AND ASSESSMENT

E. OFFICE HOURS

F. LEARNING RESOURCES

G. Course Evaluation

H. NCAAA Program Accreditation Requirements (OPTIONAL)

COURSE ISE 487: COURSE SPECIFICATIONS (DAD Template)

COURSE SPECIFICATIONS CHECKLIST

A. COURSE IDENTIFICATION AND GENERAL INFORMATION

B. COURSE DESCRIPTION, OBJECTIVES & LEARNING OUTCOMES

C. COURSE CONTENTS

D. TEACHING AND ASSESSMENT

E. OFFICE HOURS

F. LEARNING RESOURCES

G. Course Evaluation

H. NCAAA Program Accreditation Requirements (OPTIONAL)

Preliminary Proposals

 

Concentration Title: Data Science and Analytics

Concentration Details

Host Dept: MATH

Participating Depts: ICS, ISOM

Program Coordinator: Dr. Ahmet Tatar

Dept: MATH

Program Team:

Dept:

Dr. Irfan Ahmad

ICS

Dr. Mousa AlBashrawi

IS&OM

Eligibility

Students who finished all junior level courses of the following majors are eligible to enroll in this concentration:

· MATH

· ICS

· ISE

A student of other majors can enroll in this concentration if he is able to fulfil the prerequisite requirements of all concentration courses.

For the concentration to be registered in the students’ records, the student should finish all the concentration courses successfully.

Concentration Description

This interdisciplinary program focuses on the analysis and handling of data from multiple sources and for various applications in order to draw inferences from it, combining topics from mathematics, statistics, and computer science. These topics include probability theory, inference, least-square estimation, maximum likelihood estimation, finding local and global optimal solutions (gradient descent, genetic algorithms, etc.), and generalized additive models. It also covers machine learning topics such as classification, conditional probability estimation, clustering, and dimensionality reduction (e.g. discriminant factor and principal component analyses), and decision support systems. The program also covers big data analysis, including big data collection, preparation, preprocessing, warehousing, interactive visualization, analysis, scrubbing, mining, management, modeling, and tools such as Hadoop, Map-Reduce, Apache Spark, etc.

Concentration Objectives

  1. Expose students to the mathematical and statistical knowledge necessary for a data analytics/scientist profession.
  2. Give the students the opportunity to experience most recent hands-on tools used in Data Science.
  3. Engage the students in real-world projects.
  4. Encourage students to collaborate with their peers.

Concentration Students’ Learning Outcomes

By the end of this concentration, the students will be able to:

  • SLO1: Reproduce the basic steps of the data analysis process.
  • SLO2: Describe the mathematical and statistical concepts underneath the data science problems.
  • SLO3: Use the fundamental computational tools in data science.
  • SLO4: Apply mathematical and statistical tools to develop models to solve data science problems.
  • SLO5: Communicate technical results in Data Science.
  • SLO6: Demonstrate professional and ethical responsibilities of data practitioners.

     

Relationship of the objectives of the course and University mission and the Saudi Vision 2030

The Data Science and Analytics Concentration will

  1. Help grow and diversify the economy (Objective 3). Our graduates either by joining companies who are already a driving force in the field of data science or by creating their own start-ups will strengthen Kingdom’s economy.
  2. Increase Employment (Objective 4) by supplying skilled data analysts and data scientists for sectors whose numbers are growing by the day where data science and analysis play an important role.
  3. Enhance Government Effectiveness (Objective 5). Our graduates can develop efficient automated decision support systems.

Summary of Developed Courses

Course

Dept(s)

Dept.

Course Status

(New/Revised/Existing)

Is Faculty Available to Teach this course (Y/N) 

MATH 405

Learning from Data

MATH&STAT

New

Y

STAT 413

Statistical Modeling

MATH&STAT

New

Y

ICS 474

Applied Big Data

ICS

New

Y

ISE 487

Predictive Analytics Techniques

ISE

New

Y

  • Course specification of all courses should be attached

 

In order to achieve a BS degree in Mathematics or Statistics with concentration of Data Science students have to take the following four courses students must have to choose their senior projects as Data Science capstone project.

Mapping between concentration courses and Students’ Learning Outcomes

Course Code

SLO 1

SLO 2

SLO3

SLO 4

SLO 5

      SLO 6

MATH 405

X

X

 

 

STAT 413

X

X

X

 

ICS 474

X

X

X

X

ISE 487

X

X

X

 

Developed Courses

COURSE MATH 405: COURSE SPECIFICATIONS (DAD Template)

Course Title:  Learning from Data

Course Code: MATH 405


Department: Mathematics and Statistics

College:  Science                                                                                             Date: 21-Jul-20

 

 

COURSE SPECIFICATIONS CHECKLIST

#

Item Name

Completed?

Yes ü / No

Reasons if not Completed

A

Course Identification and General Information

ü

 

B

Course Description, Objectives& Learning Outcomes

ü

 

C

Course Contents

ü

 

D

Teaching and Assessment

ü

 

E

Office Hours

ü

 

F

Learning Resources

ü

 

G

Course Evaluation

ü

 

H

Other NCAAA Program Accreditation Requirements (Optional)

ü

 

 

 

Prepared by

Course Instructor/Coordinator:     Dr. Ahmet E. TATAR

Signature: AET   Date: July 2020

 

Approval Data

Name

 

Council / Committee

 

Reference No.

 

Date

 


 

  1. Course title:  Learning from Data                                    Course Type: Elective

           Course code: MATH 405                                                     Course Credit Hours: 3-0-3

2.  Program(s) in which it is offered as core course: None

3.  Level at which this course is taught: UG - Fourth Year           

4.  Pre-requisites for this course (if any):

MATH 102 or MATH 106 and STAT 201 or 212, or 319 or ISE 205, and ICS 102 or ICS 103 or ICS 104

5.  Co-requisites for this course (if any):

None

6.  Location: Main Campus

7.  Mode of Instruction

 

Mode of instruction

Percentage (%)

in class (face to face)

100

Other   (Specify:                                                   )

 

Comments:

 

Part of the KFUPM multidisciplinary concentration “Data Science and Analytics” Program.

8.Course Components (total contact hours and credits):

 

Lecture

Tutorial

Laboratory

or Studio

Practical

Others

Total

Contact Hours (per semester)

45

0

0

0

0

45

Credit Hours

3

0

0

0

0

3

 

 

Study

Assignments

Library

Project/ Research Essay/These

Others

Total

Self-Learning Hours

(Estimation per semester)

25

10

 

10

 

45

 

Total learning hours =Total contact Hours + Total self-learning Hours = 90

 

 

B. COURSE DESCRIPTION, OBJECTIVES & LEARNING OUTCOMES

1. Catalog Course Description (General description in the form used in Bulletin):

Basic vector and matrix operations, Factorizations, Basic Probability Theory, Inference, Least-Square Estimation, Maximum Likelihood Estimation, and Gradient Descent.

 

2.  List the main objectives of this course

  • Introduce topics from linear algebra, statistics, and optimization related to data science
  • Discuss selected applications in Regression and Neural Networks using numerical software, toolboxes, and libraries 

 

3.  Map Course-level Student Learning Outcomes with the Program-level Student Learning Outcomes (PLOs).

Code#

CLOs

Aligned PLOs

PLO’s code

1

Knowledge

1.1

Describe linear algebra and statistics fundamental to many machine learning algorithms.

2

2

Skills

2.1

Apply linear algebra concepts to probability and statistics.

2,3,4

2.2

Apply linear algebra to optimization problems.

2,3,4

2.3

Use linear algebra and statistics in selected machine learning algorithms.

1,2,3,4

3

Competence

3.1

 

 

 

 

 

1. Subject Area Credit Hours

(Indicate the number of credit hours against the classification below)

Engineering/Computer Science

Mathematics/ Science

Humanities

Social Sciences/Business

General Education

Other

0

3

0

0

0

0

2. Topics to be Covered

List of Topics

Contact hours

Review of basic vector and matrix operations

6

Orthogonolity, Projections, Eigenvalues, and Eigenvectors

3

SVD and Factorizations

6

Basic Probability Theory

3

Descriptive Statistics

2

Frequentist and Bayesian Inference

4

Hypothesis Testing

3

Minimum Problems: Convexity and Newton’s Method

3

Linear Programming and Lagrange Multipliers

2

(Weighted) Least-Square Estimation and Maximum Likelihood Estimation

2

Gradient Descent

2

Matrix Completion

2

Linear Regression

1

Perceptron, Neural Networks, and the Chain Rule

6

 

D. TEACHING AND ASSESSMENT

1. Course-level student Learning Outcomes (CLO) and Alignment with Teaching Strategies and Assessment Methods

Code#

CLO

Course Teaching

Strategies

Course Assessment

Methods

1

Knowledge

1.1

Describe linear algebra and statistics fundamental to many machine learning algorithms.

Lecture

Quizzes; Homework assignment; Major Exams, Final Exam

 

2

Skills

2.1

Apply linear algebra concepts to probability and statistics.

Lecture

Quizzes; Homework assignment; Major Exams, Final Exam

 

2.2

Apply linear algebra to optimization problems.

Lecture

Quizzes; Homework assignment; Major Exams, Final Exam

 

2.3

Use linear algebra and statistics in selected machine learning algorithms.

 

Lecture

 

Quizzes; Homework assignment; Major Exams, Final Exam

3

Competence

3.1

 

 

 

 


2. Schedule of Assessment Tasks for Students During the Semester

 

Assessment task (e.g. essay, test, group project, examination, speech, oral presentation, etc.)

Week Due

Proportion of Total Assessment Score

1

Assignments (5 assignments)

Bi-weekly

5%

2

Quizzes

3 times

15%

3

Major Exam 1

Week 5

20%

4

Major Exam 2

Week 10

20%

5

Project

Week 15

10%

6

Final Exam

Final Exam Week

30%

 

 

1. The amount of time teaching staff are expected to be available each week


3 hours

 

 

1. List Required Textbooks


Gilbert Strang, Linear Algebra and Learning from Data

2. List Essential References Materials (Journals, Reports, etc.)


Carlos Fernandez-Granda, Probability and Statistics for Data Science

3. List Recommended Textbooks and Reference Material (Journals, Reports, etc.)

-

4. List Electronic Materials, Web Sites, Facebook, Twitter, etc.


KFUPM Blackboard

5. Other learning material such as computer-based programs/CD, professional standards or regulations and software.

  • R
  • Python

 

 

1. Strategies for Obtaining Student Feedback on Effectiveness of Teaching (e.g. face to face meetings, student in class evaluation, student survey, focus groups, etc.)

 

KFUPM’s end of semester online evaluation for instructor, textbook, and the course will be used.

 

 

1.      Arrangements for availability of faculty and teaching staff for individual student consultations and academic advice, in addition to the instructor office hours.

 

None

2.      Facilities Required (Accommodation, e.g. classrooms and laboratories; Technology Resources, e.g. data show and smart board; other resources, e.g. laboratory equipment, etc.)

 

  • Classrooms
  • Computer Labs for projects and homework

 

COURSE STAT 413: COURSE SPECIFICATIONS (DAD Template)

Course Title:  Statistical Modeling

Course Code: STAT 413


Department: Mathematics and Statistics

College:  Science                                                                                             Date: 21-Jul-20

 

 

COURSE SPECIFICATIONS CHECKLIST

#

Item Name

Completed?

Yes ü / No

Reasons if not Completed

A

Course Identification and General Information

ü

 

B

Course Description, Objectives& Learning Outcomes

ü

 

C

Course Contents

ü

 

D

Teaching and Assessment

ü

 

E

Office Hours

ü

 

F

Learning Resources

ü

 

G

Course Evaluation

ü

 

H

Other NCAAA Program Accreditation Requirements (Optional)

ü

 

 

 

Course Instructor/Coordinator:     Dr. Ahmet E. TATAR

Signature: AET   Date: July 2020

 

Approval Data

Name

 

Council / Committee

 

Reference No.

 

Date

 


 

  1. Course title: Statistical Modeling                                                   Course Type: Elective

           Course code: STAT 413                                                                        Course Credit Hours: 3-0-3

2.  Program(s) in which it is offered as core course: None

3.  Level at which this course is taught: UG - Fourth Year           

4.  Pre-requisites for this course (if any):

MATH 405

5.  Co-requisites for this course (if any):

None

6.  Location: Main Campus

7.  Mode of Instruction

 

Mode of instruction

Percentage (%)

in class (face to face)

100

Other   (Specify:                                                   )

 

Comments:

  • Part of the KFUPM multidisciplinary concentration “Data Science and Analytics” Program.
  • Cross-listed with STAT 513

8.Course Components (total contact hours and credits):

 

Lecture

Tutorial

Laboratory

or Studio

Practical

Others

Total

Contact Hours (per semester)

45

0

0

0

0

45

Credit Hours

3

0

0

0

0

3

 

 

Study

Assignments

Library

Project/ Research Essay/These

Others

Total

Self-Learning Hours

(Estimation per semester)

25

10

 

10

 

45

 

Total learning hours =Total contact Hours + Total self-learning Hours = 90

 

 

B. COURSE DESCRIPTION, OBJECTIVES & LEARNING OUTCOMES

1. Catalog Course Description (General description in the form used in Bulletin):

Simple and Multiple Linear Regression, Polynomial Regression, Splines; Generalized Additive Models; Hierarchical and Mixed Effects Models; Bayesian Modeling; Logistic Regression, Generalized Linear Models, Discriminant Analysis; Model Selection.

 

2.  List the main objectives of this course

  • Introduce statistical tools for modeling.
  • Develop models that learn from the observed data.
  • Implement statistical models based on the statistical analysis.

 

3.  Map Course-level Student Learning Outcomes with the Program-level Student Learning Outcomes (PLOs).

Code#

CLOs

Aligned PLOs

PLO’s code

1

Knowledge

1.1

Describe different statistical tools to analyze data.

2

2

Skills

2.1

Develop statistical models to describe the observed data using computational tools.

1,2,3,4

2.2

Interpret the statistical models.

1,4

2.3

Measure the effectiveness of models.

1,3,4

3

Competence

3.1

Present effectively through oral presentation and written reports outcome of the models.

1,5

 

 

 

1. Subject Area Credit Hours

(Indicate the number of credit hours against the classification below)

Engineering/Computer Science

Mathematics/ Science

Humanities

Social Sciences/Business

General Education

Other

0

3

0

0

0

0

2. Topics to be Covered

List of Topics

Contact hours

Statistical Learning

3

Simple and Multiple Linear Regression

6

Linear Model Selection and Regularization

6

Non-Linear Regression

6

Classification

6

Generalized Linear Models

6

Generalized Linear Mixed Models

6

Bayesian Generalized Linear Models

6

 

D. TEACHING AND ASSESSMENT

1. Course-level student Learning Outcomes (CLO) and Alignment with Teaching Strategies and Assessment Methods

Code#

CLO

Course Teaching

Strategies

Course Assessment

Methods

1

Knowledge

1.1

Describe different statistical tools to analyze data.

Lecture

Quizzes; Homework assignment; Major Exams, Final Exam

2

Skills

2.1

Develop statistical models to describe the observed data using computational tools.

Lecture

Discussion of real-life problems and products.

Quizzes; Homework assignment; Major Exams, Final Exam

2.2

Interpret the statistical models.

Lecture

Discussion of real-life problems and products.

Quizzes; Homework assignment; Major Exams, Final Exam

2.3

Measure the effectiveness of models.

Lecture

Discussion of real-life problems and products.

Quizzes; Homework assignment; Major Exams, Final Exam

3

Competence

3.1

Present effectively through oral presentation and written reports outcome of the models.

Presentation to the class

Group discussion

Project

 


2. Schedule of Assessment Tasks for Students During the Semester

 

Assessment task (e.g. essay, test, group project, examination, speech, oral presentation, etc.)

Week Due

Proportion of Total Assessment Score

1

Assignments (5 assignments)

Bi-weekly

5%

2

Quizzes

3 times

15%

3

Major Exams

Week 7

20%

4

Term Project

Week 15

20%

5

Final Exam

Final Exam Week

40%

 

 

1. The amount of time teaching staff are expected to be available each week


3 hours

 

 

1. List Required Textbooks


A. Agresti. Foundations of Linear and Generalized Linear Models, Wiley

2. List Essential References Materials (Journals, Reports, etc.)

 

3. List Recommended Textbooks and Reference Material (Journals, Reports, etc.)


G. James et al. An Introduction to Statistical Learning, Springer

4. List Electronic Materials, Web Sites, Facebook, Twitter, etc.

 

KFUPM Blackboard

5. Other learning material such as computer-based programs/CD, professional standards or regulations and software.

  • R
  • Python

 

 

1. Strategies for Obtaining Student Feedback on Effectiveness of Teaching (e.g. face to face meetings, student in class evaluation, student survey, focus groups, etc.)

 

KFUPM’s end of semester online evaluation for instructor, textbook, and the course will be used.

 

 

1.      Arrangements for availability of faculty and teaching staff for individual student consultations and academic advice, in addition to the instructor office hours.

 

 None

2.      Facilities Required (Accommodation, e.g. classrooms and laboratories; Technology Resources, e.g. data show and smart board; other resources, e.g. laboratory equipment, etc.)

 

  • Classrooms
  • Computer Labs for projects and homework

 


 

COURSE ICS 474: COURSE SPECIFICATIONS (DAD Template)

Course Title: Big Data Analytics

Course Code: ICS 474


Department: Information and Computer Science

College:  Computer Science and Engineering                                                                                   Date: 21-Jul-20

 

 

COURSE SPECIFICATIONS CHECKLIST

#

Item Name

Completed?

Yes ü / No

Reasons if not Completed

A

Course Identification and General Information

ü

 

B

Course Description, Objectives& Learning Outcomes

ü

 

C

Course Contents

ü

 

D

Teaching and Assessment

ü

 

E

Office Hours

ü

 

F

Learning Resources

ü

 

G

Course Evaluation

ü

 

H

Other NCAAA Program Accreditation Requirements (Optional)

ü

 

 

 

Prepared by

Course Instructor/Coordinator: Dr. Irfan Ahmed     Signature: ___________   Date: July 2020

 

Approval Data

Name

 

Council / Committee

 

Reference No.

 

Date

 


 

  • Course title: Big Data                             Analytics            Course Type: Elective

           Course code:  ICS 474                                                          Course Credit Hours: 3-0-3

2.  Program(s) in which it is offered as core course:

3.  Level at which this course is taught: UG - Fourth Year           

4.  Pre-requisites for this course (if any):

MATH 101 or MATH 106, and SE 205 or STAT 201 or STAT 211 or STAT 212 or STAT 319

5.  Co-requisites for this course (if any):

None

6.  Location: Main Campus

7.  Mode of Instruction

 

Mode of instruction

Percentage (%)

in class (face to face)

100

Other   (Specify:                                                   )

 

Comments:

 

8.Course Components (total contact hours and credits):

 

Lecture

Tutorial

Laboratory

or Studio

Practical

Others

Total

Contact Hours (per semester)

45

0

0

0

0

45

Credit Hours

3

0

0

0

0

3

 

 

Study

Assignments

Library

Project/ Research Essay/These

Others

Total

Self-Learning Hours

(Estimation per semester)

90

20

0

20

 

130

 

Total learning hours =Total contact Hours + Total self-learning Hours = 175

 

 

B. COURSE DESCRIPTION, OBJECTIVES & LEARNING OUTCOMES

1. Catalog Course Description (General description in the form used in Bulletin):

Introduction and foundation of big data and big-data analytics. Sources of big data. Smart clouds. Hadoop file system and Apache Spark. Storage management for big data. Machine learning and visualization with big data. Applications of big data. Big data security, privacy, and its societal impacts.

 

2.  List the main objectives of this course

  • Expose students to the basic principles of big data and data analytics.
  • Introduce students to the techniques of collection, storage, processing, and security of big data.
  • Perform big data analysis using statistics and machine learning.

 

3.  Map Course-level Student Learning Outcomes with the Program-level Student Learning Outcomes (PLOs).

Code#

CLOs

Aligned PLOs

PLO’s code

1

Knowledge

1.1

Describe big data and characteristics of big data

2

2

Skills

2.1

Perform big data modelling, analysis, and management.

1,3,4

2.2

Analyse and apply machine learning techniques to big data analysis using tools like Apache Hadoop, Apache Spark, etc.

1,3,4

2.3

Design, analyse and present experiments on big data.

1,3,4,5

3

Competence

3.1

 

 

 

 

 

1. Subject Area Credit Hours

(Indicate the number of credit hours against the classification below)

Engineering/Computer Science

Mathematics/ Science

Humanities

Social Sciences/Business

General Education

Other

2

1

 

 

 

 

2. Topics to be Covered

List of Topics

Contact hours

Introduction and Foundation of Big Data and Analytics

6

Data sources: IoT Sensing, Mobile and Cognitive Systems

3

Foundation of Smart Clouds: Azur, Google cloud AWS

3

Hadoop and Spark & their relevant echo systems

9

Big data base management systems

4

Machine learning for Big data analysis

6

Big Data visualization

2

Big Data in contemporary Applications

6

Big Data Security

3

Big Data and Society (ethical, legal, and privacy aspects)

3

 

D. TEACHING AND ASSESSMENT

1. Course-level student Learning Outcomes (CLO) and Alignment with Teaching Strategies and Assessment Methods

Code#

CLO

Course Teaching

Strategies

Course Assessment

Methods

1

Knowledge

1.1

Explain different neural network architectures and their usage in current standard intelligent software applications.

Lectures and group discussion

Assignments, quizzes, and exams

2

Skills

2.1

Perform big data modelling, analysis, and management.

Lectures and group discussion

Assignments, quizzes, project, and exams

2.2

Analyse and apply machine learning techniques to big data analysis using tools like Apache Hadoop, Apache Spark, etc.

Lectures and group discussion

Assignments, quizzes, project, and exams

2.3

Design, analyse and present experiments on big data.

Lectures and group discussion

Assignments, quizzes, project, and exams

3

Competence

3.1

 

 

 

 


 

2. Schedule of Assessment Tasks for Students During the Semester

 

Assessment task (e.g. essay, test, group project, examination, speech, oral presentation, etc.)

Week Due

Proportion of Total Assessment Score

1

Assignments

2nd, 4th, 6th, 10th, 12th

20%

2

Quizzes

3rd, 5th, 7th, 11th, 13th

10%

3

Major Exams

9th

20%

4

Term Project

15th

20%

5

Final Exam

TBA

30%

 

 

1. The amount of time teaching staff are expected to be available each week

 

6 hours

 

 

1. List Required Textbooks

 

Big Data Analytics: A Hands-On Approach, by Arshdeep Bahga & Vijay Madisetti, 2019

2. List Essential References Materials (Journals, Reports, etc.)

  • ACM Transactions on Knowledge Discovery in Data (TKDD).
  • SIGKDD Explorations
  • Data Mining and Knowledge Discovery journal
  • Analytics magazine I
  • Big Data

3. List Recommended Textbooks and Reference Material (Journals, Reports, etc.)

  • Big Data Fundamentals Concepts, Drivers & Techniques, by Thomas Erl, Wajid Khattak, and Paul Buhler, 2015.
  • Hwang Kai, "Cloud Computing for Machine Learning and Cognitive Applications", MIT Press (Edition 2017).
  • Aven, Jeffrey. Data analytics with Spark using Python. Addison-Wesley Professional, 2018.
  • Guller, Mohammed. Big data analytics with Spark: A practitioner's guide to using Spark for large scale data analysis. Apress, 2015.

4. List Electronic Materials, Web Sites, Facebook, Twitter, etc.

5. Other learning material such as computer-based programs/CD, professional standards or regulations and software.

 

 

 

 

1. Strategies for Obtaining Student Feedback on Effectiveness of Teaching (e.g. face to face meetings, student in class evaluation, student survey, focus groups, etc.)

 

Student in class evaluation and student survey

 

 

1.      Arrangements for availability of faculty and teaching staff for individual student consultations and academic advice, in addition to the instructor office hours.

 

 In addition to the instructor office hours, student consultations and academic advice can be done by appointment

2.      Facilities Required (Accommodation, e.g. classrooms and laboratories; Technology Resources, e.g. data show and smart board; other resources, e.g. laboratory equipment, etc.)

 

For group discussion, smart classrooms are recommended. Access to cloud computer is necessary, Students need to install Python.

 

 


 

COURSE ISE 487: COURSE SPECIFICATIONS (DAD Template)

Course Title:  Predictive Analytics Techniques

Course Code: ISE 487


Department: System Engineering

College: Computer Science and Engineering                                                   Date: 21-Jul-20

 

 

COURSE SPECIFICATIONS CHECKLIST

#

Item Name

Completed?

Yes ü / No

Reasons if not Completed

A

Course Identification and General Information

ü

 

B

Course Description, Objectives& Learning Outcomes

ü

 

C

Course Contents

ü

 

D

Teaching and Assessment

ü

 

E

Office Hours

ü

 

F

Learning Resources

ü

 

G

Course Evaluation

ü

 

H

Other NCAAA Program Accreditation Requirements (Optional)

ü

 

 

 

Prepared by

Course Instructor/Coordinator: Dr. Syed N. Mujahid            Signature: ___________   Date:  July 2020

 

Approval Data

Name

 

Council / Committee

 

Reference No.

 

Date

 


 

  1. Course title:   Predictive Analytics Techniques                          Course Type: Elective

           Course code: ISE 487                                                                            Course Credit Hours: 3-0-3

2.  Program(s) in which it is offered as core course: None

3.  Level at which this course is taught: UG - Fourth Year           

4.  Pre-requisites for this course (if any):

      1. MATH 405 or ISE 315

      2. ICS 102 or ICS 103 or ICS 104

5.  Co-requisites for this course (if any):

None

6.  Location: Main Campus

7.  Mode of Instruction:

 

Mode of instruction

Percentage (%)

in class (face to face)

100

Other   (Specify:                                                  )

 

Comments:

Can be taught as an online course.

8.Course Components (total contact hours and credits):

 

Lecture

Tutorial

Laboratory

or Studio

Practical

Others

Total

Contact Hours (per semester)

45

0

0

0

0

45

Credit Hours

3

0

0

0

0

3

 

 

Study

Assignments

Library

Project/ Research Essay/These

Others

Total

Self-Learning Hours

(Estimation per semester)

15

15

0

15

0

45

 

Total learning hours =Total contact Hours + Total self-learning Hours = 90

 

B. COURSE DESCRIPTION, OBJECTIVES & LEARNING OUTCOMES

1. Catalog Course Description (General description in the form used in Bulletin):

Characteristics of time series, trends, seasonality, noise, stationarity; Statistical background and model evaluation methods; Time series regression, variable selection and general linear regression; Exponential Smoothing and seasonal data; ARIMA based models including MA, AR, ARMA, ARIMA and SARIMA, Model validation and parameter estimation; Advance predictive analytics: Multivariate prediction, state space models, neural networks, spectral analysis and Bayesian methods.

 

2.  List the main objectives of this course

The aim of this course is to provide the following knowledge to students.

  • To learn data science methods for the analysis of data that have been observed over time.
  • To apply computational tools in predicting estimates.
  • Implement the predictive analytics knowledge on time series data.

 

3.  Map Course-level Student Learning Outcomes with the Program-level Student Learning Outcomes (PLOs).

Code#

CLOs

Aligned PLOs

PLO’s code

1

Knowledge

1.1

Characterize time series data

1, 2

2

Skills

2.1

Conduct parametric and non-parametric analysis of time series

1,2,3

2.2

Analyze stationary and non-stationary time series data.

1,2,3,4

3

Competence

3.1

Propose and evaluate apt methodology for a given data set.

4,5,6

 

 

 

1. Subject Area Credit Hours

(Indicate the number of credit hours against the classification below)

Engineering/Computer Science

Mathematics/ Science

Humanities

Social Sciences/Business

General Education

Other

1.5

1.5

 

 

 

 

2. Topics to be Covered

List of Topics

Contact hours

Introduction to Forecasting

2

Statistical Background

6

Regression Analysis

8

Exponential Smoothing

8

ARIMA Models

12

Advanced Methods

9

 

D. TEACHING AND ASSESSMENT

1. Course-level student Learning Outcomes (CLO) and Alignment with Teaching Strategies and Assessment Methods

Code#

CLO

Course Teaching

Strategies

Course Assessment

Methods

1

Knowledge

1.1

Characterize time series data

Lecture

Paper based exams

2

Skills

2.1

Conduct parametric and non-parametric analysis of time-series.

Lecture

Paper based exams

 

Analyze stationary and non-stationary time series data.

Lecture

Paper based exams

3

Competence

3.1

Propose and evaluate apt methodology for a given data set.

Lecture

Project

 


2. Schedule of Assessment Tasks for Students During the Semester

 

Assessment task (e.g. essay, test, group project, examination, speech, oral presentation, etc.)

Week Due

Proportion of Total Assessment Score

1

Assignments / Homework

Every two weeks (skipping the exam weeks)

8 – 12 %

2

Quizzes

After every major topic

10 – 15 %

3

Midterm Exams

Between weeks 6 to 8 per common exam schedule

25 %

4

Term Project

Week 13 or 14

20 – 25 %

5

Final Exam

As per registrar schedule

30 %

 

 

1. The amount of time teaching staff are expected to be available each week


Each faculty must have 3 hours per week as office hours to help students.

 

 

1. List Required Textbooks


Montgomery D. C., Jennings C. L., and Kulahci M., “Introduction to Time Series Analysis and Forecasting”, Wiley, 2015.

2. List Essential References Materials (Journals, Reports, etc.)

3. List Recommended Textbooks and Reference Material (Journals, Reports, etc.)

 

4. List Electronic Materials, Web Sites, Facebook, Twitter, etc.

 

5. Other learning material such as computer-based programs/CD, professional standards or regulations and software.

 

 

1. Strategies for Obtaining Student Feedback on Effectiveness of Teaching (e.g. face to face meetings, student in class evaluation, student survey, focus groups, etc.)

The normal end‐of‐semester University online evaluation of the instructor, course, and textbook by students.

 

 

1.      Arrangements for availability of faculty and teaching staff for individual student consultations and academic advice, in addition to the instructor office hours.

 

 None

2.      Facilities Required (Accommodation, e.g. classrooms and laboratories; Technology Resources, e.g. data show and smart board; other resources, e.g. laboratory equipment, etc.)

  • Classroom
  • Access to computer labs for working on project and homework

Preliminary Proposals

Course Title: 

MATH-405: Learning from Data  

New Course 

Course Level: 

400-Level  

Credits: 

L = 3, LAB = 0, CR = 3 

Course Description: 

 

Basic vector and matrix operations, Factorizations, Basic Probability Theory, Inference, Least-Square Estimation, Maximum Likelihood Estimation, and Gradient Descent.

Prerequisites by Topics

Basic Mathematics, Basic linear algebra, probability and statistics, and programing knowledge  

Prerequisites by Courses

MATH 102 or MATH 106 and STAT 201 or 212, or 319 or ISE 205, and ICS 102 or ICS 103 or ICS 104

Proposed Hosting Depart(s):  

Mathematics and Statistics

Mode of Delivery: 

Lectures and Projects

Course Objectives 

  • Introduce topics from linear algebra, statistics, and optimization related to data science
  • Discuss selected applications in Regression and Neural Networks using numerical software, toolboxes, and libraries 

List of Topics

 

Linear Algebra: [5 weeks]

•    Review of basic vector and matrix operations

•    Gauss Elimination

•    Orthogonality and Projections

•    Eigenvalues and Eigenvectors

•    SVD and Factorizations

Probability and Statistics: [4 weeks]

•    Basic Probability Theory

•    Central Limit Theorem

•    Descriptive Statistics

•    Frequentist and Bayesian Inference

•    Hypothesis Testing

Optimization: [3 weeks]

•    Minimum problems: Convexity and Newton’s method

•    Linear programing

•    Lagrange multipliers

•  (Weighted) Least-square estimation

•    Maximum Likelihood Estimation

•    Gradient Descent

Learning from Data: [3 weeks]

•    Matrix Completion

•    Linear regression

•    Perceptron

•    Neural Networks

•    Backpropagation and the Chain rule

•    The World of Machine Learning

Course Learning Outcomes

 The students will: 

CLO-1 

Describe linear algebra and statistics fundamental to many machine learning algorithms.

CLO-2 

Apply linear algebra concepts to probability and statistics.

CLO-3 

Apply linear algebra to optimization problems.

CLO-4 

Use linear algebra and statistics in selected machine learning algorithms.

Potential Textbook/ References 

  1. Gilbert Strang, Linear Algebra and Learning from Data
  2. Carlos Fernandez-Granda, Probability and Statistics for Data Science

Benchmarking

MIT, NYU

Teaching 

Faculty: 

 

 

Data Science and Analytics Team, Last updated: July 21, 2020 

 


 

Course Title: 

STAT-413: Statistical Modeling

New Course 

Course Level: 

400-Level  

Credits: 

L = 3, LAB = 0, CR = 3 

Course Description: 

 

Simple and Multiple Linear Regression, Polynomial Regression, Splines; Generalized Additive Models; Hierarchical and Mixed Effects Models; Bayesian Modeling; Logistic Regression, Generalized Linear Models, Discriminant Analysis; Model Selection.

Prerequisites by Topics 

Basic Mathematics and Statistics 

Prerequisites by Course 

MATH 405

Proposed Hosting Depart(s):  

Mathematics and Statistics

Mode of Delivery: 

Lectures and Projects

Course Objectives 

  • Introduce statistical tools for modeling.
  • Develop models that learn from the observed data.
  • Implement statistical models based on the statistical analysis.

List of Topics

 

  • Statistical Learning [1 week]
  • Linear Regression [2 weeks]
    • Simple Linear Regression
    • Multiple Linear Regression
  • Linear Model Selection and Regularization [2 weeks]
    • Subset Selection
    • Ridge – Lasso – Elastic Net
    • Dimension Reduction – PCA
  • Non – Linear Regression [2 weeks]
    • Polynomial Regression
    • Regression Splines
    • Generalized Additive Models
  • Classification [2 weeks]
    • Logistic Regression
    • Linear Discriminant Analysis
  • Generalized Linear Models [2 weeks]
  • Generalized Linear Mixed Models [2 weeks]
  • Bayesian Generalized Linear Models [2 weeks]

Course Learning Outcomes

 The students will: 

CLO-1 

Describe different statistical tools to analyze data.

CLO-2 

Develop statistical models to describe the observed data using computational tools.

CLO-3 

Interpret the statistical models.

CLO-4 

Measure the effectiveness of models.

CLO-5

Present effectively through oral presentation and written reports outcome of the models.

Potential Textbook / References 

  • A. Agresti. Foundations of Linear and Generalized Linear Models, Wiley
  • G. James et al. An Introduction to Statistical Learning, Springer

Benchmarking 

Colombia, Harvard

Teaching 

Faculty: 

 

 

Data Science and Analytics Team, Last updated: July 21, 2020 

 


 

Course Title: 

ICS-474 Big Data Analytics

New Course 

Course Level: 

400-Level  

Credits: 

L = 3, LAB = 0, CR = 3 

Course Description

 

Introduction and foundation of big data and big-data analytics. Sources of big data. Smart clouds. Hadoop file system and Apache Spark. Storage management for big data. Machine learning and visualization with big data. Applications of big data. Big data security, privacy, and its societal impacts

Prerequisites by Topics

Mathematics and Statistics

Prerequisites by Course

MATH 101 or MATH 106, and SE 205 or STAT 201 or STAT 211 or STAT 212 or STAT 319

Proposed Hosting Depart(s):  

Information and Computer Science

Mode of Delivery: 

 Face to face

Course Objectives 

  • Expose students to the basic principles of big data and data analytics.
  • Introduce students to the techniques of collection, storage, processing, and security of big data.
  • Perform big data analysis using statistics and machine learning.

List of Topics 

 

Introduction and Foundation of Big Data and Analytics     2 weeks

Data sources: IoT Sensing, Mobile and Cognitive Systems     1 week

Foundation of Smart Clouds: Azur, Google cloud AWS     1 week

Hadoop and Spark & their relevant echo systems     3 weeks

Big data base management systems     1.5 weeks

Machine learning for Big data analysis     2 weeks

Big Data visualization     ½ weeks

Big Data in contemporary Applications     2 weeks

Big Data Security     1 week

Big Data and Society (ethical, legal, and privacy aspects)    1 week

Course Learning Outcomes 

 The students will: 

CLO-1 

Describe big data and characteristics of big data.

CLO-2 

Perform big data modelling, analysis, and management.

CLO-3 

Analyse and apply machine learning techniques to big data analysis using tools like Apache Hadoop, Apache Spark, etc.

 CLO-4 

Design, analyse and present experiments on big data.

Potential Textbook/ References 

  • Big Data Analytics: A Hands-On Approach, by Arshdeep Bahga & Vijay Madisetti, 2019
  • Big Data Fundamentals Concepts, Drivers & Techniques, by Thomas Erl, Wajid Khattak, and Paul Buhler, 2015.
  • Hwang Kai, "Cloud Computing for Machine Learning and Cognitive Applications", MIT Press (Edition 2017).
  • Aven, Jeffrey. Data analytics with Spark using Python. Addison-Wesley Professional, 2018.
  • Guller, Mohammed. Big data analytics with Spark: A practitioner's guide to using Spark for large scale data analysis. Apress, 2015.

Benchmarking: 

 CMU (Heinz College),

Harvard University,

Rutgers University,

MIT

Teaching 

Faculty: 

 

 

Data Science and Analytics Team, Last updated: July 21, 2020 

 


 

 

Course Title: 

ISE-487: Predictive Analytics Techniques

New Course 

Course Level: 

400-Level  

Credits: 

L = 3, LAB = 0, CR = 3 

Course Description

 

Characteristics of time series, trends, seasonality, noise, stationarity; Statistical background and model evaluation methods; Time series regression, variable selection and general linear regression; Exponential Smoothing and seasonal data; ARIMA based models including MA, AR, ARMA, ARIMA and SARIMA, Model validation and parameter estimation; Advance predictive analytics: Multivariate prediction, state space models, neural networks, spectral analysis and Bayesian methods.

Prerequisites by Topics

Basic Mathematics and Statistics

Prerequisites by Course

Math 405, or ISE 315 and ICS 102 or ICS 103 or ICS 104

Proposed Hosting Depart(s):  

 ISE (SE)

Mode of Delivery: 

 Online, In classroom

Course Objectives 

The aim of this course is to provide the following knowledge to students.

  • To learn data science methods for the analysis of data that have been observed over time.
  • To apply computational tools in predicting estimates.
  • Implement the predictive analytics knowledge on time series data

List of Topics

 

  • Introduction to Forecasting (1 Week)
  • Statistical Background (2 Weeks):
    • Graphical and Numerical Representation
    • Characteristics and Transformations
    • Evaluating Forecasting Model
  • Regression Analysis (2.5 Weeks):
    • Least Square Estimation
    • Model Adequacy Checking
    • Variable Selection Methods
    • Generalized and Weighted Least Squares
    • Models for General Time Series Data
  • Exponential Smoothing Methods (2.5 Weeks):
    • First Order Exponential Smoothing
    • Modeling Time Series Data
    • Second Order Exponential Smoothing
    • Forecasting Non-Seasonal Data
    • Forecasting Seasonal Data
  • ARIMA Models (4 Weeks):
    • Models for Stationary Time Series
    • Moving Average Process
    • Autoregressive Process
    • ARMA Process
    • ARIMA Process
    • Model Identification and Parameter Estimation
    • SARIMA Process
  • Advanced Methods (3 weeks):
    • Multivariate Models
    • State Space Models
    • Arch and Garch Models
    • Neural Networks
    • Spectral Analysis
    • Bayesian Methods

Course Learning Outcomes

 The students will learn: 

CLO-1 

Characterize time series data.

CLO-2 

Conduct parametric and non-parametric analysis of time series.

CLO-3 

Analyze stationary and non-stationary time series data.

CLO-4

Propose and evaluate apt methodology for a given data set.   

Potential Textbook/ References 

Montgomery D. C., Jennings C. L., and Kulahci M., “Introduction to Time Series Analysis and Forecasting”, Wiley, 2015.

Benchmarking: 

MIT, Penn State University, Ohio State University

Teaching 

Faculty: 

Dr Naqebuddin Mujahid (ISE)

Dr Irfan Ahmed (ICS)

Dr Luqman Hamzah (ICS)

 

Data Science and Analytics Team, Last updated: July 21, 2020

 

Concentration Title: Data Science and Analytics


Eligibility

Students who finished all junior level courses of the following majors are eligible to enroll in this concentration:

· MATH

· ICS

· ISE

A student of other majors can enroll in this concentration if he is able to fulfil the prerequisite requirements of all concentration courses.

For the concentration to be registered in the students' records, the student should finish all the concentration courses successfully.

Concentration Description

This interdisciplinary program focuses on the analysis and handling of data from multiple sources and for various applications in order to draw inferences from it, combining topics from mathematics, statistics, and computer science. These topics include probability theory, inference, least-square estimation, maximum likelihood estimation, finding local and global optimal solutions (gradient descent, genetic algorithms, etc.), and generalized additive models. It also covers machine learning topics such as classification, conditional probability estimation, clustering, and dimensionality reduction (e.g. discriminant factor and principal component analyses), and decision support systems. The program also covers big data analysis, including big data collection, preparation, preprocessing, warehousing, interactive visualization, analysis, scrubbing, mining, management, modeling, and tools such as Hadoop, Map-Reduce, Apache Spark, etc.

Concentration Objectives

  1. Expose students to the mathematical and statistical knowledge necessary for a data analytics/scientist profession.

  2. Give the students the opportunity to experience most recent hands-on tools used in Data Science.

  3. Engage the students in real-world projects.

  4. Encourage students to collaborate with their peers.

Concentration Students' Learning Outcomes

By the end of this concentration, the students will be able to:

  • SLO1: Reproduce the basic steps of the data analysis process.

  • SLO2: Describe the mathematical and statistical concepts underneath the data science problems.

  • SLO3: Use the fundamental computational tools in data science.

  • SLO4: Apply mathematical and statistical tools to develop models to solve data science problems.

  • SLO5: Communicate technical results in Data Science.

  • SLO6: Demonstrate professional and ethical responsibilities of data practitioners.
     

Relationship of the objectives of the course and University mission and the Saudi Vision 2030

The Data Science and Analytics Concentration will

  1. Help grow and diversify the economy (Objective 3). Our graduates either by joining companies who are already a driving force in the field of data science or by creating their own start-ups will strengthen Kingdom's economy.

  2. Increase Employment (Objective 4) by supplying skilled data analysts and data scientists for sectors whose numbers are growing by the day where data science and analysis play an important role.

  3. Enhance Government Effectiveness (Objective 5). Our graduates can develop efficient automated decision support systems.

Summary of Developed Courses

Course

Dept(s)

Dept.

Course Status

(New/Revised/Existing)

Is Faculty Available to Teach this course (Y/N) 

MATH 405

Learning from Data

MATH&STAT

New

Y

STAT 413

Statistical Modeling

MATH&STAT

New

Y

ICS 474

Applied Big Data

ICS

New

Y

ISE 487

Predictive Analytics Techniques

ISE

New

Y

  • Course specification of all courses should be attached

 

In order to achieve a BS degree in Mathematics or Statistics with concentration of Data Science students have to take the following four courses students must have to choose their senior projects as Data Science capstone project.

Mapping between concentration courses and Students' Learning Outcomes

Course Code

SLO 1

SLO 2

SLO3

SLO 4

SLO 5

      SLO 6

MATH 405

X

X

 

 

STAT 413

X

X

X

 

ICS 474

X

X

X

X

ISE 487

X

X

X

 


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