MScSurvey Statistics and Data Analysis
| Study location | Hungary, Budapest |
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| Type | Master, full-time |
| Nominal duration | 4 semesters (2 years) (120 ECTS) |
| Study language | English |
| Awards | MSc (Survey Statistician and Data Analyst) |
| Course code | Social Science |
| Accreditation | OH-FHF/387-5/2008 |
| Tuition fee | €3,500 per semester For February intake – if there is one for the program – tuition fee will be 3000 EUR |
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| Application fee | €100 one-time The application fee is non-refundable |
| Entry qualification | Undergraduate / Bachelor diploma (or higher) Applicants must have a BA/BBA degree in any of the following fields: Social Studies, Sociology, Applied Economics, Economic and Financial Mathematical Analysis, Commerce and Marketing. If the BA degree does not meet all the necessary requirements, the applicant may be required to complete additional credits in the relevant fields during their first year of study to fulfill these requirements upon acceptance. Documents to submit with application
The entry qualification documents are accepted in English. If this is not the case, you will need official translations along with verified copies of the original. You must take the original entry qualification documents along with you when you finally go to the university. The entry qualification documents are accepted in the following languages: English. Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original. You must take the original entry qualification documents along with you when you finally go to the university. |
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| Language requirements | English Certification of English knowledge (both written and oral): B2
The applicant’s English knowledge will also be assessed during the admission interview. |
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| Other requirements | A motivation letter must be added to your application. Applicants must upload a motivation letter in English that introduces themselves and outlines their reasons for applying, including their motivations, interests, and goals related to the programme. The letter should be between 1,500 and 4,000 characters. The procedure of the entrance examination Applicants with a complete application package will be notified after the application deadline about the exact time and date of their online interviews. It is the applicant’s responsibility to have technically suitable conditions for the interview from their side. Applicants are expected to be prepared taking questions regarding the compulsory admission materials (see: Recommended Readings) from the side of admission committee composed of a professor, a lecturer and a student representative. A successful oral entrance exam is the perquisite of getting admitted. If the applicant fails the oral entrance exam, the application will be rejected. |
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Overview
The data revolution is creating a need for data analytics in a wide variety of fields. We have developed our programme in response to this need. Our goal is to train data analysts who can contribute to data-driven decision making in business/industry, public administration, or social research.
Our students will be able to analyze large databases, be familiar with statistical solutions for survey-based research, and conduct online research and web analytics in scientific, governmental, and business applications. They will have knowledge of network analysis, natural language processing, be familiar with the basics of machine learning, be prepared to implement analytic solutions in R and Python, and know the basics of data analysis infrastructure (SQL, Git, and other tools). We will also provide the mathematical foundation that is essential for a deep understanding of methods and for lifelong learning.
The programme also provides students with practical knowledge that introduces them to the entire process of data analytics (project management, data collection, data visualization, business communication). The programme is recommended for applicants seeking a complex programme that is flexible while providing a solid foundation in data analytics.
Specializations:
There are no specializations, but students can tailor their learning path to their own interests as the electives are organized into the following modules of two to three courses: Biomedical Research, Economic Research, Digital Data Analytics, Social Research, Business Research.
Strength of programme:
The programme has been running successfully in Hungarian for 30 years and is one of the most popular master’s programmes at ELTE. Its founder is Tamás Rudas, Professor at the Department of Statistics at the University of Washington, former Director General of the Center for Social Science Research of the Hungarian Academy of Sciences, Fellow of the European Academy of Sociology, former President of the European Association of Methodology.
The success of the programme is based on continuous improvement and adaptation to changing social contexts. The ideal data analyst has three main skills: statistical, programming, and business/social research skills. Accordingly, we provide strong mathematical/statistical/machine learning skills to extract new insights from the data. Programming skills are also provided for effective data management. Business/social research knowledge is provided to understand the context of the data.
In addition, we try to develop the creativity needed to ask good questions, select good data, and find good interpretations. Our strength is that, beyond the technical side of analytics, we also aim to provide the business context so that our students can independently translate research questions into analytical problems and then translate the results back to the client.
Leader of the programme:
Renáta NÉMETH, Professor
Entry requirements:
Applicants must have a BA/BBA degree in any of the following fields: Social Studies, Sociology, Applied Economics, Economic and Financial Mathematical Analysis, Commerce and Marketing.
In the case of other bachelor’s degrees: individual consideration.
If the BA degree does not meet all the necessary requirements, the applicant may be required to complete additional credits in the relevant fields during their first year of study to fulfill these requirements upon acceptance.
Documents to submit with application:
- Bachelor-level degree
- Transcript of records
- Motivation letter
- Copy of the main pages of the passport (needs to be valid)
- Proof of the application fee transfer
- Language certificate (if the applicant has one)
The procedure of the entrance examination:
Applicants with a full application package will be asked after the application deadline to participate in an oral entrance exam. The institutional admission scores are based on a total evaluation of the academic excellence (based on the submitted documents) and the results of the entrance exam. The entrance exam seeks to assess the general and professional knowledge and interest of the applicant.
Applicants are expected to be prepared taking questions regarding the compulsory admission materials (see: Recommended Readings) from the side of admission committee composed of a professor, a lecturer and a student representative.
A successful oral entrance exam is the perquisite of getting admitted. If the applicant fails the oral entrance exam, the application will be rejected.
Entrance exam:
Yes
Type of entrance exam:
Oral
Entrance exam location:
Online
Application procedure
The applications are examined by the Admission Board and applicants are notified of the outcome of the selection in the online application system.
Further details of the entrance exam:
During the exam, applicants will have a discussion on one of the topics listed below.
The topics are based on Freedman, D. – Pisani, R. – Purves, R.: Statistics, 4th edition. W. W. Norton & Company, 2007 (or later edition). After each topic the corresponding parts of the Freedman et al. book are listed.
1. Experiments and observational studies (Freedman Part I.)
2. Probability: independence, product rule, addition rule, conditional probability (Freedman Part IV.)
3. Statistical inference: point estimate, confidence interval, why inference is useful, what has an effect on the confidence interval (Freedman Part V. and VII.)
4. Null hypothesis significance testing: why is it useful, null- and alternative hypothesis, significance level, one- and two-sided tests (Freedman Part VIII.)
5. Z-test, t-test: when are these applied, what is the difference between one- and two-sample (independent samples) tests (Freedman Part VIII.)
6. Chi-square tests: when are these used, what are the assumptions (Freedman Part VIII.)
7. Correlation: what is it used for, what are the assumptions, how to use it (Freedman Part III.)
8. Regression: what is it used for, what are the assumptions, how to use it (Freedman Part III.)
9. Reliability and validity: what do these mean, what factors influence these (Freedman Part I. and VI.)
10. Sampling: simple random sampling, stratified sampling, two-stage sampling, sampling design and validity, why use two-stage sampling, what is representativity (Freedman Part VI.)
Contact person:
International Office, Eszter Borbála Bagi
E-mail: international@tatk.elte.hu
Programme structure
Career opportunities
The programme has the advantage of adapting to the complex needs of the labor market. Our graduates can design research based on either questionnaire, digital or administrative data sources. We want to provide our students with the opportunity to develop a learning path and career path that suits their own interests, whether they want to use applications of data analytics based on artificial intelligence or quantitative tools of market research.
Our programme includes a six-week internship, and we have decades of experience working with renowned business figures. This, combined with the knowledge gained in the programme, enables our graduates to choose from a wide range of job opportunities. They are typically employed in data-intensive sectors: public administration, info-communication, financial services, market and public opinion research, marketing, non-profit sector, healthcare and pharmaceuticals. At the same time, many of our graduates are working in research institutions or in higher education, as the programme also prepares them for doctoral studies, potentially in all the disciplines covered by the programme (applied mathematics, statistics, social sciences, computer science).
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