91滴滴

Big Data Analytics

Academic master鈥檚 programme 90 ECTS (if previously obtained a professional bachelor’s degree) or 120 ECTS (if previously obtained a academic bachelor’s degree)

This is the only master’s programme in Latvia that prepares data (including Big Data) professionals to work in companies and organisations.

Accredited: until 5 August 2027

General information

  • Beginning:
    From September 1
  • Duration:
    1.5 years (if completed 4-year bachelor鈥檚 studies)
    2 years (if completed 3-year bachelor鈥檚 studies)
  • Type:
    Full-time
  • Language:
    English (full-time evening studies), Latvian (full-time evening studies)
  • Credit points:
    90 ECTS (if previously obtained a professional bachelor's degree) 120 ECTS (if previously obtained an academic bachelor's degree)
  • Programme degree:
    Master鈥檚 degree of social science in economics
  • Tuition fee per year (for the 2026/2027 academic year):
    Citizens and permanent residents of Latvia, as well as citizens of other EU, EEA, and EU candidate countries:
    3600 EUR (Full-time evening, Latvian)
    4700 EUR (Full-time evening, English)

    Other countries:
    6200 EUR (Full-time evening, English)

This is the only master’s programme in Latvia that prepares data (including Big Data) professionals to work in companies and organisations. It provides students with in-depth skills regarding data acquisition, storage, collection, visualisation and analysis. This programme is in line with the world’s leading data professional education programmes implemented at Harvard University, Stanford University, IBM Big Data University and other prestigious universities, and helps students develop skills such as business skills, analytical skills, computer skills, statistical and mathematical skills and machine learning skills.

Admission rules

Please read the admission rules published on our website under Study at 91滴滴 -> Admission rules

These are full-time studies that take place in the evenings in the main building of 91滴滴, located at 3 Me啪a Street. These are lectures with a wide range of educational content in the field of economics, related to business processes, the use of company and organisation data in information systems, forecasting modelling, risk management, statistical analysis and business data processing.
The purpose of the study programme is to train students to perform organisational data processing using SPSS and Power BI software packages, to perform data processing in relational databases using SQL and R language tools, to perform data processing in non-relational databases (NoSQL) using MapReduce approach and various tools of Hadoop eco-environment, as well as to apply machine learning methods in data processing using supervised and unsupervised machine learning algorithms, Python language, KNIME and other tools.

Module 1 鈥淏usiness Data Processing鈥� 11 CP (16.5 ECTS)

Business requirements analysis and specification of information systems (Mini MBA, Mg.bus.man., Mg.inf.sys. J. Paksis, 鈥淓mergn鈥�): 2 CP (Part A)
Multivariate data analysis (Dr.oec., prof. B. Sloka): 2 CP (Part A)
Forecasting methods (Dr.sc.comp.h.c., prof. P. Riv啪a): 2 CP (Part B)
Practical application of Power BI in business data processing (Dr.phys., prof. hon. I. Godmanis): 2 CP (Part A)
Business analytics in SPSS environment (Dr.admin.sc., prof. I. Ludviga): 2 CP (Part A)
Corporate social responsibility and environmental ecology (S. Blumberga): 1 CP (Part A)

Module 2 鈥淏ig Data Management鈥� 14 CP (20 ECTS)

Introduction to Big Data and machine learning (Dr.phys., Assoc. I. Godmanis): 3 CP (Part A)
Use of SQL language when working with relational databases (Mg.dat. E. Pl膩cis, 鈥淎ccenture鈥�: 2 CP (Part A)
R language (Mg.dat. A. Alksnis): 2 CP (Part A)
Data pre-processing and data management using the R language: (Dr.oec., asoc.prof. E. Br膿姆is): 2 CP (Part B)
Big Data management tools (Mg.eng.sc. A. Vesjolijs, 鈥淎ccenture鈥�): 3 CP (Part A)
Data visualisation methods (Dr.sc.ing. S. B膿rzi拧a): 2 CP (Part B)

Module 3 鈥淗arnessing Big Data in new technologies鈥�: 9 CP (13.5 ECTS)

Python language (Dr.dat., asoc.prof. U. Boj膩rs): 2 CP (Part B)
Practical machine learning using Python language (Dr.sc.comp. J. R膩ts): 2 CP (Part B)
Big data analytics in business 鈥� KNIME and other tools (MBA, Chicago uni., U. Spr奴d啪s): 3 CP (Part B)
Business platforms (Dr.phys, Assoc. I. Godmanis): 2 CP (Part C)

Internship

  • 6 CP (with 4-year bachelor’s studies),
  • 26 CP (with 3-year bachelor’s studies)

Master鈥檚 thesis (20 CP)

The demand for data professionals who can and do use (extract, collect, store, process, visualise and analyse) data (including Big Data) is growing rapidly worldwide, including in Latvia. The increasing development of digital business platforms, Internet of Things and artificial intelligence technologies and their application in companies and organisations is based on the use of a rapidly growing amount of diverse data (Big Data), which graduates of this study programme are familiar with.

After graduation, a student can continue education in the doctoral programme 鈥淏usiness Management鈥�.

Any student of the master’s programme who has completed at least one year of study can participate in the Erasmus+ exchange programme.
A student can go to study at one of 91滴滴’s partner universities. The duration of study mobility is 2鈥�12 months.
The student does not have to pay for the period spent in the study and internship exchange programme abroad; the tuition fee is covered by the partner university, but the student continues to pay the 91滴滴 tuition fee. For the period spent abroad during mobility, the student is awarded an Erasmus+ scholarship to cover the costs of transport and accommodation.
* Erasmus+ internship mobility provides that a student can undergo an internship in one of the companies of interest in one of the Erasmus+ programme countries. For this internship period, the student receives an Erasmus+ scholarship. The duration of internship mobility is 2鈥�12 months.
Find out more about Erasmus+ and how to apply here!

Since the 2023/2024 academic year, information on the self-assessment of the study programme has been included in the study field progress report, which is available here.

2018./19
2019./20
2020./21
2021./22

2022./23

Elza Rozi艈a
Study programme administrator
Phone
show phone
E-mail
show e-mail
Address
108, Me啪a iela 3
Monta Sapata
Head of the Study Department
Phone
show phone
E-mail
show e-mail
Address
108, Me啪a iela 3
Back
Ivars Godmanis Dr.phys.
Director of the Master's Study programme "Big Data Analytics", Associate Professor, Researcher
Phone
show phone
E-mail
show e-mail
Address
404, Me啪a iela 3

    Contact us

    To top