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
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 
  
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.
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, leastsquare 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, MapReduce, Apache
Spark, etc.
 Expose students to the mathematical and statistical knowledge
necessary for a data analytics/scientist profession.
 Give the students the opportunity to
experience most recent handson tools used in Data Science.
 Engage
the students in realworld projects.
 Encourage students to collaborate with their
peers.
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.
The Data
Science and Analytics Concentration will
 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 startups will strengthen
Kingdom’s economy.
 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.
 Enhance Government Effectiveness (Objective 5).
Our graduates can develop efficient automated decision support systems.
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.
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  
Course Title: Learning from Data 
Course Code: MATH 405 
Department: Mathematics and Statistics 
College: Science Date: 21Jul20 

#  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  