Data Science Master’s Program
July 29, 2020
1. Introduction
With the qualification of the Data Science Master’s Program at the Ala-too International University,
you can build your skills for the most promising careers in 2020
· Data Scientist
· Machine Learning Engineer
· Business Intelligence (BI) Developer
· Data Analyst
· Big Data Analyst
· Data Engineer
· Enterprise Data Architect or Modeler
· Data Steward and Data Curator
The Master of Computer Science degree is a Data Science master program offered by the Department of Computer Science at Ala-Too International University. We offer graduate students an opportunity to augment their knowledge and expertise by combining course work and through research in a wide range of areas, e.g., Artificial Intelligence, Machine Learning, Deep Learning, Intelligent Systems, Big Data, Exploratory Data Analysis and Natural Language Processing under the umbrella of Data Science field.
Typically, Students in the master program will get the courses that are offered by the Computer Science department in the first year. In the second year, students will engage in a one-year in-depth research, in which the student specializes in their area of interest.
The advances in computer technology have added new fuel to the development of almost all of the science and engineering applications. Because of its role in the improvement of civilization, this discipline became a separate profession. In today’s age of information, Data Science is one of the promising and latest trends for the applications of engineering and science that contribute through professional services towards a more prosperous and sustainable society.
Today a wide range of the government and private sectors involving education, healthcare, finance, retail, marketing, analytics, automotive, consulting, technology demand employees equipped with data science skills. Currently most of the data science related careers are shaping our future. Graduates can work at the positions of data scientist, data analyst, big data analyst, business analyst, data science consultant, data architect, data engineer, business intelligence (BI) developer, data mining specialist, machine-learning engineer and predictive modeller.
The mission of the Department of Computer Science is to educate the students to gain an understanding of the fundamentals of science and engineering so that they can develop solutions to Computer Science problems and enhance their skills on Data Science applications, communication and research skills. It is aimed to especially emphasize teamwork, independent and innovative thinking and leadership qualities.
We take a comprehensive approach by integrating math skills, programming skills and data domain. Curriculum covers the several mathematical skills in statistics, linear algebra, calculus, multivariable calculus, differential equations, programming skills using Python, R and MATLAB, and data driven skills using data collection, data extraction, data transformation, data loading, data wrangling, data exploration, data analysis, data visualization. It aims to develop and implement predictive and intelligent data science models to support decision making in order to translate raw data into actionable insights. In addition, the data science master’s will excel the graduates at advanced computational skills to meet emerging challenges in big data analytics.
2. CURRICULUM
Master's Degree Program in Data Science is a two-year program, consisting of a total 120 ECTS credits. Each student takes the compulsory courses and elective courses. Elective courses can be selected from the specialization courses and optional courses (maximum 15 ECTS). The specialization courses will be chosen based on the student’s major. The optional courses will be selected by considering the student’s previous studies and personal interest.
2.1 Structure of the Studies
2.1.1 Coursework (60 ECTS)
- Compulsory courses
1. Advanced Research Methodology for Data Science
- Specialization courses
The specialization courses will be chosen based on the student’s major.
2. Foundations of Data Science
3. Artificial Intelligence and Deep Learning
4. Machine Learning for Data Science
5. Big Data Analytics
6. Exploratory Data Analysis and Visualization
7. Python for Data Science
8. Software Design for Data Science
9. Natural Language Processing
10. Reinforcement Learning
11. Special Topics in Data Science
12. Introduction to Statistics & Probability
13. Linear Algebra for Data Science
14. Multivariable Calculus
15. Differential Equations
16. Optimization Methods
- Optional courses (max 15 ECTS)
17. Intelligent Systems and Robotics
18. Digital Image Processing
19. Fuzzy Logic and Neuro-fuzzy Systems
20. Data Warehousing and Data Mining
21. Cloud computing
In addition, students can include studies from other Faculties, Departments and Units (majors) to their optional studies based on his/her own interesting areas or study domain.
2.1.2 Advanced Studies (60 ECTS)
· Seminar work, 15 ECTS
· Master’s thesis, 45 ECTS
|
|
1. Semester |
||||
|
CODE |
COURSE NAME |
T |
P |
C |
ECTS |
|
CS 6XX |
Specialization course 1 |
3 |
0 |
3 |
6 |
|
CS 6XX |
Specialization course 2 |
3 |
0 |
3 |
6 |
|
CS 6XX |
Specialization course 3 |
3 |
0 |
3 |
6 |
|
CS 6XX |
Specialization course 4 |
3 |
0 |
3 |
6 |
|
CS 6XX |
Specialization course 5 / Optional Course 1 |
3 |
0 |
3 |
6 |
|
|
|
|
|
|
|
|
Total |
15 |
0 |
15 |
30 |
|
|
|
2. Semester |
|||||
|
CODE |
COURSE NAME |
T |
P |
C |
ECTS |
|
|
CS 610 |
Advanced Research Methodology for Data Science |
3 |
0 |
3 |
6 |
|
|
CS 6XX |
Specialization course 6 |
3 |
0 |
3 |
6 |
|
|
CS 6XX |
Specialization course 7 |
3 |
0 |
3 |
6 |
|
|
CS 6XX |
Specialization course 8 |
3 |
0 |
3 |
6 |
|
|
CS 6XX |
Specialization course 9 / Optional Course 2 |
3 |
0 |
3 |
6 |
|
|
|
|
|
|
|
|
|
|
Total |
15 |
0 |
15 |
30 |
||
|
|
3. Semester |
|||||
|
CODE |
COURSE NAME |
T |
P |
C |
ECTS |
|
|
CS 998 |
Master’s Thesis Seminar |
0 |
0 |
0 |
15 |
|
|
|
|
|
|
|
|
|
|
Total |
0 |
0 |
0 |
15 |
||
|
|
4. Semester |
|||||
|
CODE |
COURSE NAME |
T |
P |
C |
ECTS |
|
|
CS 999 |
Master's Thesis |
0 |
0 |
9 |
45 |
|
|
|
|
|
|
|
|
|
|
|
Total |
9 |
0 |
9 |
45 |
|
|
|
Grand Total |
39 |
0 |
39 |
120 |
|
|
Elective Courses |
|||||
|
CODE |
COURSE NAME |
T |
P |
C |
ECTS |
|
|
CS 610 |
Advanced Research Methodology for Data Science |
3 |
0 |
3 |
6 |
|
|
CS 611 |
Foundations of Data Science |
3 |
0 |
3 |
6 |
|
|
CS 612 |
Artificial Intelligence and Deep Learning |
3 |
0 |
3 |
6 |
|
|
CS 613 |
Machine Learning for Data Science |
3 |
0 |
3 |
6 |
|
|
CS 614 |
Big Data Analytics |
3 |
0 |
3 |
6 |
|
|
CS 615 |
Exploratory Data Analysis and Visualization |
3 |
0 |
3 |
6 |
|
|
CS 616 |
Python for Data Science |
3 |
0 |
3 |
6 |
|
|
CS 617 |
Introduction to Statistics & Probability |
3 |
0 |
3 |
6 |
|
|
CS 618 |
Software Design for Data Science |
3 |
0 |
3 |
6 |
|
|
CS 619 |
Linear Algebra for Data Science |
3 |
0 |
3 |
6 |
|
|
CS 620 |
Natural Language Processing |
3 |
0 |
3 |
6 |
|
|
CS 621 |
Special Topics in Data Science |
3 |
0 |
3 |
6 |
|
|
CS 622 |
Reinforcement Learning |
3 |
0 |
3 |
6 |
|
|
CS 623 |
Applied Programming and Computer Science |
3 |
0 |
3 |
6 |
|
|
CS 624 |
Intelligent Systems and Robotics |
3 |
0 |
3 |
6 |
|
|
CS 625 |
Digital Image Processing |
3 |
0 |
3 |
6 |
|
|
CS 626 |
Fuzzy Logic and Neuro-fuzzy Systems |
3 |
0 |
3 |
6 |
|
|
CS 627 |
Data Warehousing and Data Mining |
3 |
0 |
3 |
6 |
|
|
CS 628 |
Multivariable Calculus |
3 |
0 |
3 |
6 |
|
|
CS 629 |
Differential Equations |
3 |
0 |
3 |
6 |
|
|
CS 630 |
Optimization Methods |
3 |
0 |
3 |
6 |
|
|
CS 998 |
Master’s Thesis Seminar |
0 |
0 |
0 |
15 |
|
|
CS 999 |
Master's Thesis |
9 |
0 |
9 |
45 |
|