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)

Tämä sivu päi­vi­te­tään vii­meis­tään 30.11.2018.

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

  • a) What are your motives to apply for this Master's Programme at the University of Helsinki?  (1000 characters)
  • b) How do your previous studies enable you to succeed in the studies of this Master's Programme? (500)
  • c) How do the studies of this Master's Programme complement and broaden your previous studies? (500)
  • d) How studying in this Master's Programme relates to your career objectives and what you wish to achieve by completing your master's degree? (500)
  • e) Outline your academic interests in the chosen field, i.e., indicate what you see as being the topic of your Master's thesis and how it relates to this Master's Programme. (1000)
  • 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 at www.helsinki.fi/en/programmes/master/data-science and make a preliminary study plan for your master's studies here. (2000)

  • Please fill in your GPA (Grade Point Average), and the grading scale at your previous university. If your official transcript or final degree certificate does not state GPA, 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)

  • Fill here your GPA mapped to the University of Helsinki grading scale 0-5, where 0 is fail, 1 lowest accepted and 5 highest grade (you may use decimal values). (100)

  • Please state the total number of credits included in your degree. State also the number of credits required for this type of degree at your institution. If your institution does not use a credit system, please use the system of your institution (for example study hours). Please describe briefly the system used in your institution (for example 1 credit = 30 hours of study). (150)

  • Please state the amount of computer science and data science studies in your previous degree (a numerical value, a sum of the course credits/sizes). (150)

  • Please state the amount of mathematics and statistics studies in your previous degree (numerical value, sum of the course credits/sizes). (150)

  • Category 1, programming skills and languages: Please list all programming languages you have studied, and for each language name one or two courses that covered that particular language. 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, 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 3, statistics and probability theory: 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 4, mathematics for data science: Please list at most four most advanced courses in linear algebra, differential calculus and integral calculus. 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, other courses in computer science: Please list at most four most advanced or relevant courses, primarly on computer organization and architecture, operating systems, and databases. 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, other courses in statistics and 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 7, application areas: If you have significant studies in other subjects that are relevant for your data science studies, e.g., in areas where you would like to apply data science (e.g. life sciences, natural sciences, humanities, business), then please 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 2019. If your degree does not contain a thesis, explain the largest scientific writing task included in your degree. (700)