Datatieteen maisteriohjelma: ha­ku­lo­mak­keen ky­sy­myk­set

HUOM! Tämä sivu koskee sinua, jos olet joulukuussa 2016 tai tammikuussa 2017 hakenut seuraavaan hakukohteeseen: Da­ta­tie­teen mais­te­rioh­jel­ma, fi­lo­so­fian mais­te­ri (2 v)

Motivaatiokirje ja sen sisältämä opintosuunnitelma sekä analyysi aiemmista opinnoistasi tehdään vastaamalla hakulomakkeella annettuihin kysymyksiin, jotka on lueteltu alla.  Mietithän vastauksia huolellisesti etukäteen ennen hakulomakkeen täyttämistä, sillä vastauksia ei voi muuttaa lomakkeen lähettämisen jälkeen.  Vastausten merkkimäärä on rajoitettu.  Hakulomakkeen täyttämisaika on myös rajallinen.

Tämän maisteriohjelman opetuskieli on englanti. Tämän takia alla olevat kysymykset ovat vain englanniksi.  Voit kuitenkin halutessasi vastata kysymyksiin myös suomeksi tai ruotsiksi.   

Huomioithan, että Helsingin yliopisto saattaa tarkistaa motivaatiokirjeen ja muut vastaavat dokumentit plagiaatintunnistusjärjestelmällä. 

Motivation letter / analysis questions

  • Briefly describe and explain in English: Your motives to apply for this programme at the University of Helsinki. How your previous studies enable you to succeed in the studies in the chosen programme and how the programme complements and develops your previous studies. How studying in this programme relates to your future aspirations and academic objectives.  (2000 characters)
  • Please fill in information about your degree and its course content in the categories mentioned below. Please notice that you need not have studies in every category 1-9, but in particular categories 1-5 are emphasised in the selection criteria.
  • Category 1, mathematics for data science: Please list at most four most advanced courses in probability theory and linear algebra. Describe each course’s main content.  A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them. (600)
  • Category 2, statistics: Please list at most four most advanced or relevant courses. Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.  (600)
  • Category 3, programming skills and languages: List all programming languages and for each language name one or two courses that covered that particular language. Give also a short overview of course contents.  (600)
  • Category 4, data structures and algorithms: Please list at most two most advanced or relevant courses. Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.  (600)
  • Category 5, computer organization and architecture, operating systems, computer networks: Please list at most four most advanced or relevant courses.  Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.  (600)
  • Category 6, data bases, data management, software engineering: Please list at most four most advanced or relevant courses.  Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.  (600)
  • Category 7, other courses in mathematics: Please list at most four most advanced or relevant courses.  Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.  (600)
  • Category 8, other courses in computer science: Please list at most four most advanced or relevant courses.  Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.  (600)
  • Category 9, application areas: If you have significant studies in potential application areas of data science (e.g. life sciences, natural sciences, humanities, business) then list here the subjects you studied, the number of credits and a very brief description of the contents of your studies. (600)
  • Scientific writing: If your degree contains a thesis, explain briefly your thesis writing process and the required structure of the thesis. You should be able to explain the thesis process and requirements even, if you are writing your thesis during spring 2017. If your degree does not contain a thesis, explain the largest scientific writing task that is included in your degree.  (700)
  • Draw up a study plan. How will you complete your Master's studies in two years’ time? Please visit the web page of the programme and make a preliminary study plan for your master's studies here.  (2000)
  • Please fill in your GPA (Grade Point Average) percentage.  (Alternatively, you can fill your GPA value and the maximum.) If your official transcript or final degree certificate does not state GPA percentage, please calculate it as the weighted average of all grades in your official transcript. Do this by multiplying each grade’s point value by the number of credits. Sum all of these together and divide by the total number of credits. Please calculate the value as best as you can. This GPA will be verified by the university. An accurate self-reported GPA will help to expedite the processing of your application.  (150)
  • Give your own ECTS estimate of your degree size (numerical value, sum of the course sizes). You can find more information about ECTS from the document: http://ec.europa.eu/education/tools/ects_en.htm.  (150)
  • Give your own ECTS estimate of amount of mathematics amd statistics in your degree (numerical value, sum of the course sizes).  (150)
  • Give your own ECTS estimate of amount of CS studies in your degree (numerical value, sum of the computer science course sizes).  (150)