Computing for Data Science

Data Science is “The ability to take data — To be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — and that’s going to be a hugely important skill in the next decades.”

Overview

The Computer Science department at USAL offers a Bachelor of Science (B.S.) with a concentration in computing for Data Science. The program provides students with cross-disciplinary knowledge and skills in computer science, mathematics, statistics, machine learning, artificial intelligence, and information visualization, to pursue effective careers in computing and data science.
The main focus of this program is turning data into useful knowledge through artificial intelligence and machine learning. Graduates will gain the skills in collecting and analyzing data, solving problems using intelligent techniques, and effectively communicating the solutions. Such skills are required in almost all industries making skilled data scientists increasingly demanded in companies all over the world. This field opens the door to diverse professions such as data analyst, data consultant, machine learning scientist, and data architect.

Courses of the curricula

University Requirements & Electives (22 credits)

Code

Subject

Credits

GENR201

Religion and public life

1 credit

ENGL 201

English communication skills I 

3 credits

ENGL 202

English communication skills II 

3 credits

ARAB 201

مهارات التواصل في  اللغة العربية I Arabic communication skills I 

3 credits

 

General elective 1

3 credits

 

General elective 2

3 credits

 

Department elective 1

3 credits

 

Department elective 2

3 credits

Total

22

Core Courses (52 credits)
Code Subject Credits
MATH203 Linear Algebra 3 credits
MATH201 Calculus 3 credits
MATH210 Discrete Mathematics 3 credits
MATH204 Probability and Statistics I 3 credits
CSCI205 Computer Science Overview 3 credits
CSCI206 Introduction to Programming 4 credits (3;1)
CSCI207 Object Oriented Programming 4 credits (3;1)
CSCI210 Computer Architecture and Logic Design 3 credits
CSCI212 Computer Networks I 3 credits
CSCI311 Introduction to Database Systems 3 credits
CSCI315 Web development 4 credits (3;1)
CSCI316 Data Structure and Algorithms 3 credits
CSCI317 Software Engineering 3 credits
CSCI320 Operating Systems 4 credits (3;1)
CSCI410 Artificial intelligence and Machine learning 3 credits
CSCI420 Final Year Project 3 credits
Total 52
Major / Concentration courses (31 credits)

Code

Subject

Credits

DTSC301

Introduction to Data Science

3 credits

DTSC302

Data Visualization

1 credit

DTSC420

Natural Language Processing

3 credits

DTSC422

Time series and statistical forecasting

3 credits

DTSC424

Deep learning

3 credits

MATH304

Probability and Statistics II

3 credits

MATH307

Statistical Models

3 credits

MATH310

Numerical Computation and Analysis

3 credits

COMP305

Advanced Programming

3 credits

COMP411

Advanced Data Structure and Algorithms

3 credits

COMP412

Advanced Databases

3 credits

Total

31

Courses Distribution Plan

  • First Year - Fall
  • First Year - Spring
  • First Year - Summer
  • Second Year - Fall
  • Second Year - Spring
  • Second Year - Summer
  • Third Year - Fall
  • Third Year - Spring
First Year - Fall
CodeCourse Name# of weeks# of Lecture hours# of labs hoursCredits
GENR201Religion and Public Life1515-1
MATH203 Linear Algebra1530-3
ENG 201English Communication skills I1545-3
ARAB201Arabic Communication skills1545-3
CSCI206Introduction to programming1545304 (3; 1)
CSCI 205Computer Science Overview1515-3
Total17
First Year - Spring
Code Course Name #Nb of weeks #Nb of Lecture hours #Nb of labs hours credits
MATH210 Discrete Mathematics 15 45 - 3
ENG202 English Communication Skills II 15 45 - 3
CSCI207 Object Oriented Programming 15 45 30 4 (3; 1)
CSCI210 Computer Architecture and Organization 15 30 - 3
MATH 201 Calculus 15 30 - 3
Total 16
First Year - Summer
 
Code Course Name #Nb of weeks #Nb of Lecture hours #Nb of labs hours credits
CSCI212 Computer Networks – I : Networking fundamentals 8 45 - 3
GE/DE Elective I 8 45 - 3
Total 6
Second Year - Fall
Code Course Name #Nb of weeks #Nb of Lecture hours #Nb of labs hours credits
CSCI311 Introduction to Database 15 45 - 3
CSCI316 Introduction to Data Structure and Algorithms 15 45 - 3
Math204 Probability and Statistics I 15 45 - 3
MATH310 Numerical Computation and Analysis 15 45 - 3
GE/DE Elective II 15 45 - 3
Total 15
Second Year - Spring
Code Course Name #Nb of weeks #Nb of Lecture hours #Nb of labs hours credits
COMP305 Advanced Programming 15 45 - 3
CSCI315 Web Development 15 45 30 4(3; 1)
CSCI317 Software Engineering 15 45 - 3
CSCI320 Operating Systems 15 45 30 4(3;1)
MATH304 Probability and Statistics II 15 45 - 3
Total 17
Second Year - Summer
CodeCourse Name# of weeks# of Lecture hours# of labs hourscredits
CSCI410Artificial Intelligence and Machine Learning845-3
DTSC301Introduction to Data Science845-3
Total6
Third Year - Fall
Code Course Name # of weeks # of Lecture hours # of labs hours credits
DTSC424 Deep learning 15 45 - 3
GE / DE Elective III 15 45 - 3
DTSC420 Natural Language Processing 15 45 - 3
MATH307 Statistical Models 15 45 - 3
GE / DE Elective IV 15 45 - 3
Third Year - Spring
Code Course Name # of weeks # of Lecture hours # of labs hours credits
COMP411 Advanced Data Structure and Algorithms 15 45 - 3
DTSC422 Time series and statistical forecasting 15 30 - 3
CSCI420 Final Year Project - - - 3
COMP412 Advanced Databases 15 45 - 3
Total 12
1 One credit is equivalent to 15 lecture hours or 30 laboratory hours per semester Courses Descriptions