Statistics and Data Science
Degree
Bachelor of Natural Sciences
Program length
4 years
Treshold score for state scholarship
110
Threshold score for paid department
80
UNT subjects
Mathematics
Physics
Level of English
B1 (Intermediate)
The program aims to train highly qualified specialists in statistics and data science who possess innovative problem-solving skills in statistics and data analysis. Students will gain the ability to apply their knowledge to analyze various problems in predictive analytics, marketing analytics, economics, biology, and other fields.
The educational process includes research work and the application of data analysis to real-life problems, which helps develop important academic skills such as working with databases, data analysis, programming, experimental design, academic writing, and understanding academic integrity and plagiarism.
Moreover, students will have the opportunity to participate in scientific projects that prepare them for successful admission to local and international Master’s or PhD programs in mathematical computer modeling, mathematics, and statistics.
Upon graduation, students will be equipped to pursue careers in educational and scientific institutions, IT companies, banks and insurance companies, and other organizations that utilize applied statistics, mathematics, and computer technology in their operations.
- The program enables students to apply their knowledge in probability theory, mathematical statistics, random processes, data analysis, calculus, linear algebra, differential equations, numerical analysis, optimization methods, discrete mathematics, and mathematical logic to solve practical problems encountered in written exams.
- Students will develop logical skills in programming using Python, allowing them to construct computer programs proficiently.
- They will acquire the ability to manipulate data sets, work with databases, and perform statistical analysis on collected data by constructing computer programs.
- Students will be able to utilize statistical methods, professional software, computer graphics, visualization techniques, and other relevant tools to address scientific and applied problems effectively.
- Through Modern Regression Analysis 1-2 courses, students will learn to design and test multiple linear regression models and develop strategies for constructing statistical models.
- The program encourages students to develop an understanding of government operations, market dynamics, institutional frameworks, societal relations, major ethical theories, and problems. Moreover, they will have the opportunity to demonstrate fluency in multiple languages through the study of non-area subjects such as economics, sociology, philosophy, Russian/Kazakh language, Turkish language, and more.
1 semestr | code | title | th | pr | cr | ects |
---|---|---|---|---|---|
MAT 125 | Discrete mathematics | 2 | 2+0 | 4 | 5 |
MDE 160 | Community engagement and value based Society 1 | 0 | 1+0 | 1 | 0 |
MDE 171 | History of Kazakhstan | 2 | 1+0 | 3 | 5 |
MDE 291 | Physical Education 1 | 0 | 1+0 | 1 | 2 |
SDS 101 | Calculus 1 | 2 | 2+0 | 4 | 5 |
SDS 103 | Linear algebra | 2 | 2+0 | 4 | 5 |
XXX XXX | [ NAE ] Foreign Language 1 (MDE 190, MDE 192) | 0 | 3+0 | 3 | 5 |
XXX XXX | [ NAE ] Turkish Language 1 (MDE 283, MDE 285) | 0 | 3+0 | 3 | 4 |
Local Credit | ECTS |
---|---|
Theoretical course: 18 | Theoretical course: 24 |
Non Theoretical course: 5 | Non Theoretical course: 7 |
Sum: 23 | Sum: 31 |
2 semestr | code | title | th | pr | cr | ects |
---|---|---|---|---|---|
MAT 103 | Analytic Geometry | 1 | 1+0 | 2 | 3 |
MAT 106 | Information and Communication Technologies (in English) | 1 | 2+0 | 3 | 5 |
MDE 170 | Community engagement and value based Society 2 | 0 | 1+0 | 1 | 0 |
MDE 292 | Physical Education 2 | 0 | 1+0 | 1 | 2 |
SDS 107 | Introduction to probability theory | 2 | 2+0 | 4 | 5 |
SDS 110 | Calculus 2 | 2 | 2+0 | 4 | 5 |
XXX XXX | [ NAE ] Foreign Language 2 ( MDE 191, MDE 194) | 0 | 3+0 | 3 | 5 |
XXX XXX | [ NAE ] Turkish Language 2 (MDE 284, MDE 286) | 0 | 3+0 | 3 | 4 |
Local Credit | ECTS |
---|---|
Theoretical course: 19 | Theoretical course: 27 |
Non Theoretical course: 2 | Non Theoretical course: 2 |
Sum: 21 | Sum: 29 |
3 semestr | code | title | th | pr | cr | ects |
---|---|---|---|---|---|
MAT 205 | Ordinary differential equations | 2 | 2+0 | 4 | 5 |
MAT 172 | Philosophy | 2 | 1+0 | 3 | 5 |
MAT 293 | Physical Education 3 | 0 | 1+0 | 1 | 2 |
SDS 105 | Algorithms and programming 1 | 2 | 2+0 | 4 | 5 |
SDS 204 | Statistics and its Applications 1 | 2 | 2+0 | 4 | 5 |
SDS 210 | Calculus 3 | 2 | 2+0 | 4 | 6 |
XXX XXX | [ NAE ] Kazakh/Russian language1 (MDE 111/MDE113/MDE115/MDE117/MDE121/MDE123/MDE 125/MDE127) | 0 | 3+0 | 3 | 5 |
Local Credit | ECTS |
---|---|
Theoretical course: 22 | Theoretical course: 31 |
Non Theoretical course: 1 | Non Theoretical course: 2 |
Sum: 23 | Sum: 33 |
4 semestr | code | title | th | pr | cr | ects |
---|---|---|---|---|---|
MAT 331 | Optimization methods | 2 | 1+0 | 3 | 5 |
MAT 294 | Physical Education 4 | 0 | 1+0 | 1 | 2 |
SDS 106 | Algorithms and programming 2 and Educational Practice | 2 | 2+0 | 4 | 5 |
SDS 205 | Statistics and its Applications 2 | 2 | 2+0 | 4 | 5 |
SDS 311 | Data Wrangling, Analysis and Visualisation | 2 | 2+0 | 4 | 5 |
XXX XXX | [ NAE ] Kazakh/Russian language2(MDE 112/MDE114/MDE116/MDE118/MDE122/MDE124/MDE 126/MDE128) | 0 | 3+0 | 3 | 5 |
Local Credit | ECTS |
---|---|
Theoretical course: 18 | Theoretical course: 25 |
Non Theoretical course: 1 | Non Theoretical course: 2 |
Sum: 19 | Sum: 27 |
5 semestr | code | title | th | pr | cr | ects |
---|---|---|---|---|---|
MAT 152 | Real and Functional analysis | 2 | 2+0 | 4 | 5 |
MDE 151 | Module of Social and Political Knowledge (Political Science) | 1 | 0+0 | 1 | 2 |
MDE 152 | Module of Social and Political Knowledge (Sociology) | 1 | 0+0 | 1 | 2 |
SDS 307 | Modern Regression Analysis | 2 | 2+0 | 4 | 6 |
SDS 310 | Database Management Systems | 2 | 2+0 | 4 | 5 |
XXX XXX | [ NAE ] Elective 1 (MDE 161, MDE 162, MDE 163, MDE 164, MDE 165, MDE 166, MDE 289, MAT 144) | 2 | 1+0 | 3 | 5 |
XXX XXX | [ AE ] Area Elective Course (CSS 633, MAT 211, MAT 147) | 2 | 1+0 | 3 | 5 |
Local Credit | ECTS |
---|---|
Theoretical course: 20 | Theoretical course: 30 |
Non Theoretical course: 0 | Non Theoretical course: 0 |
Sum: 20 | Sum: 30 |
6 semestr | code | title | th | pr | cr | ects |
---|---|---|---|---|---|
CSS 634 | Deep Learning | 2 | 1+0 | 3 | 5 |
MAT 148 | Measure theory | 2 | 2+0 | 4 | 6 |
MDE 153 | Module of Social and Political Knowledge (Cultural Studies) | 1 | 0+0 | 1 | 2 |
MDE 154 | Module of Social and Political Knowledge (Psychology) | 1 | 0+0 | 1 | 2 |
XXX XXX | [ AE ] Area Elective Course 2 (SDS 119, MAT 426, MAT 445) | 2 | 1+0 | 3 | 5 |
XXX XXX | [ AE ] Area Elective 3 (INF 376, MAT 521, MAT 438) | 2 | 1+0 | 3 | 5 |
XXX XXX | [ AE ] Elective 4 (CSS 421, CSS 423, SDS 306) | 2 | 1+0 | 3 | 5 |
Local Credit | ECTS |
---|---|
Theoretical course: 18 | Theoretical course: 30 |
Non Theoretical course: 0 | Non Theoretical course: 0 |
Sum: 18 | Sum: 30 |
7 semestr | code | title | th | pr | cr | ects |
---|---|---|---|---|---|
SDS 370 | Methods of Scientific research | 2 | 2+0 | 4 | 5 |
SDS 406 | Industrial internship 1 | 0 | 3+0 | 3 | 4 |
XXX XXX | [ AE ] Area Elective Course (MAT 524, MAT 364, MAT 310) | 2 | 1+0 | 3 | 5 |
XXX XXX | [ AE ] Area Elective 7 (SDS 408, MCM 309, SDS 303) | 2 | 1+0 | 3 | 5 |
XXX XXX | [ AE ] Area Elective 8 (INF 342 / CSS 465 / CSS 323) | 2 | 1+0 | 3 | 5 |
XXX XXX | [ AE ] Area Elective 6 (SDS 120, MAT 515, SDS 121) | 2 | 1+0 | 3 | 5 |
XXX XXX | [ AE ] Area Elective 5 (SDS 111 / MAT 315 / MAT 150) | 2 | 1+0 | 3 | 5 |
Local Credit | ECTS |
---|---|
Theoretical course: 22 | Theoretical course: 34 |
Non Theoretical course: 0 | Non Theoretical course: 0 |
Sum: 22 | Sum: 34 |
8 semestr | code | title | th | pr | cr | ects |
---|---|---|---|---|---|
SDS 412 | Academic writing | 2 | 2+0 | 4 | 6 |
XXX XXX | [ NTE ] Final attestation (SDS 410 / SDS 411) | 0 | 6+0 | 6 | 8 |
XXX XXX | [ AE ] Elective 7(SDS 402/MAT 409) | 0 | 6+0 | 6 | 12 |
Local Credit | ECTS |
---|---|
Theoretical course: 10 | Theoretical course: 18 |
Non Theoretical course: 6 | Non Theoretical course: 8 |
Sum: 16 | Sum: 26 |
Total Local Credit | Total ECTS |
---|---|
Theoretical course: 147 | Theoretical course: 219 |
Non Theoretical course: 15 | Non Theoretical course: 21 |
Sum: 162 | Sum: 240 |
Graduates with a degree in Statistics and Data Science have a wide range of career opportunities across various industries. Here are some potential career choices:
- Data Scientist: Analyzing complex data sets to extract insights and solve business problems using statistical models and machine learning techniques.
- Data Analyst: Collecting, cleaning, and analyzing data to identify patterns and trends.
- Business Analyst: Applying statistical analysis and data modeling to help organizations make informed business decisions.
- Data Engineer: Building and maintaining data infrastructure, databases, and pipelines to ensure efficient data storage and processing.
- Machine Learning Engineer: Developing and implementing machine learning algorithms and models for predictive analysis and automation.
- Statistician: Conducting statistical research, designing experiments, and analyzing data to provide insights and support decision-making.
- Risk Analyst: Assessing and managing risks using statistical models and quantitative analysis in industries such as finance and insurance.
- Research Scientist: Conducting research and experiments, analyzing data, and publishing findings in academic or industrial settings.
- Consultant: Providing data-driven insights and recommendations to clients in various industries.
- Data Manager: Overseeing data collection, storage, and governance to ensure data quality and integrity.
Moreover, graduates can continue their education in MSc and PhD programs.