Program Description
Data Science encompasses the generation of insights and value from raw data and is the
core of digital businesses across all sectors. It’s a field that requires a diverse mix of
capabilities and skills—and never gets boring. Data informs key decisions, leads to
optimisation of existing processes, and is the enabler of entirely new business models via
data insights and automation. When you take a bachelor’s degree in data science you join
the data revolution that is leading major changes in businesses, economies, and societies
today.
Our Bachelor of Science in Data Science aims to provide a programme of study
that combines data science, machine learning, statistics and mathematics. The programme
uses a rigorous approach, has a mathematical focus and involves applying data science to
the social sciences.
Program Objectives
The Bachelor of Science in Data Science Engineering will prepare you for further study, or
for professional and managerial careers, particularly in areas requiring the application of
quantitative skills. The programme also allows you to choose to study a specialist area
according to your developing interests and career plans
Program Highlights
Degree Awarded
_________________________
Bachelor of Science in Data Science
Commencement of Session
_________________________
January 2023 (then 4 times a year; Oct, Jan, Apr or Jul)
Study Model
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Online Studies / E-learning
Approval
_________________________
To be approved by the Ministry of Education and Research, Estonia.
Duration
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36 Months
Credits
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180 ECTS/EAP ( European Credit Transfer and Accumulation System)
Program Learning Outcomes
Gain extensive first-hand experience of carrying out typical workflows of data
analytics.
Learn about acquiring, querying and understanding the basic properties of data,
analysis, how to extract insights from data and how to report the results.
Be able to use and understand classical and modern data-analytics techniques,
statistical machine learning and artificial intelligence techniques.
Be competent in computer programming in data-analytic contexts.
Have a broad range of knowledge useful in data-analytic contexts, including topics at
an intermediate or advanced level in economics and finance. Depending on your
course choices you could also acquire knowledge of advanced topics in mathematics
and statistics.
Be able to think in a critical manner.
Program Curriculum
Provides basic knowledge and skills required for understanding and managing IT and/or Data Science specific subjects.
Contains courses focused on IT essentials and important components of IT – networks, programming, Python Language, databases, web applications, etc.
Contains courses focused on Data Science, IT systems
administration and development, Data Quality and Data Wrangling, Model Engineering, Time Series Analysis, Explorative Data Analysis and Visualization, Time Series Analysis, Neural Nets and Deep Learning and Big Data Technologies and also a corresponding internship.
administration and development, Data Quality and Data Wrangling, Model Engineering, Time Series Analysis, Explorative Data Analysis and Visualization, Time Series Analysis, Neural Nets and Deep Learning and Big Data Technologies and also a corresponding internship.
Student can freely choose any courses offered by the institute without any restrictions.
Classical thesis (formulation of a practical problem with
corresponding analysis and solution) related to Data Science or to an area, where Data Science plays an important role.
corresponding analysis and solution) related to Data Science or to an area, where Data Science plays an important role.
Mandatory Core Courses
PBAM0705 - Macroeconomics.
PBAM0710 - Business Administration.
PBAM0715 - Microeconomics.
PBAM0725 - Organisation Management.
PBAM0750 - Corporate Finance.
PBAM0810 - Marketing.
PBAM0820 - Human Resource Management.
PBAM0830 - Innovation & Management.
PBAM0840 - Management Techniques.
PBAM0850 - Corporate Governance and International Business.
Thesis project
PBAM1080 - Business Research Methods.
PBAM1090 - Thesis Presentation & Defence.
Elective Courses (Any 3)
AI Specialist
Data Analyst
Data Engineer
Applied Sales
International Marketing and Branding
Smart Factory
Supply Chain Management
Program Structure
| Semester 1 | ||||
|---|---|---|---|---|
| S. N. | Course | Course Name | EAP | Hours/Week |
| 1 | UCSC0101 | Introduction to Data Science | 5 | 4 |
| 2 | UCSC0102 | Professional Science Essentials | 5 | 4 |
| 3 | UCSC0103 | Agile Project Management | 5 | 4 |
| 4 | UCSC0104 | Introduction to Programming with Python | 5 | 4 |
| 5 | UCSC0105 | Mathematics: Analysis | 5 | 4 |
| 6 | UCSC0106 | Statistics Probability and Descriptive Statistics | 5 | 4 |
| Total For The Semester | 30 | 24 | ||
| Semester 2 | ||||
|---|---|---|---|---|
| S. N. | Course | Course Name | EAP | Hours/Week |
| 1 | UCSC0201 | Object Oriented and Functional Programming with Python | 5 | 4 |
| 2 | UCSC0202 | Mathematics: Linear Algebra | 5 | 4 |
| 3 | UCSC0203 | Statistics – Inferential Statistics | 5 | 4 |
| 4 | UCSC0204 | Intercultural and Ethical Decision-Making | 5 | 4 |
| 5 | UCSC0205 | Collaborative Work | 5 | 4 |
| 6 | UCSC0206 | Introduction to Data Protection and Cyber Security | 5 | 4 |
| Total For The Semester | 30 | 24 | ||
| Semester 3 | ||||
|---|---|---|---|---|
| S. N. | Course | Course Name | EAP | Hours/Week |
| 1 | UCSC0301 | Database Modeling and Database Systems | 5 | 4 |
| 2 | UCSC0302 | Project: Build a Data Mart in SQL | 5 | 4 |
| 3 | UCSC0303 | Cloud Computing | 5 | 4 |
| 4 | UCSC0304 | Machine Learning – Supervised Learning | 5 | 4 |
| 5 | UCSC0305 | Machine Learning – Unsupervised Learning and Feature Engineering | 5 | 4 |
| 6 | UCSC0306 | DS Software Engineering | 5 | 4 |
| Total For The Semester | 30 | 24 | ||
| Semester 4 | ||||
|---|---|---|---|---|
| S. N. | Course | Course Name | EAP | Hours/Week |
| 1 | UCSC0401 | Business Intelligence | 5 | 4 |
| 2 | UCSC0402 | Project: Business Intelligence | 5 | 4 |
| 3 | UCSC0403 | Data Quality and Data Wrangling | 5 | 4 |
| 4 | UCSC0404 | Explorative Data Analysis and Visualization | 5 | 4 |
| 5 | UCSC0405 | Time Series Analysis | 5 | 4 |
| 6 | UCSC0406 | Model Engineering | 5 | 4 |
| Total For The Semester | 30 | 24 | ||
| Semester 5 | ||||
|---|---|---|---|---|
| S. N. | Course | Course Name | EAP | Hours/Week |
| 1 | UCSC0501 | Big Data Technologies | 5 | 4 |
| 2 | UCSC0502 | Neural Nets and Deep Learning | 5 | 4 |
| 3 | UCSC0503 | Elective 1 | 5 | 4 |
| 4 | UCSC0504 | Seminar | 5 | 4 |
| 5 | UCSC0505 | Elective 2 | 10 | 8 |
| Total For The Semester | 30 | 24 | ||
| Semester 6 | ||||
|---|---|---|---|---|
| S. N. | Course | Course Name | EAP | Hours/Week |
| 1 | UCSC0601 | Project: From Model to Production | 10 | 4 |
| 2 | UCSC0602 | Elective 3 | 10 | 4 |
| 3 | UCSC0603 | Bachelor Thesis | 10 | 16 |
| Total For The Semester | 30 | 24 | ||