Study unit
3622353 Probabilistic inference for data science 2, 5 Cp  
Code 3622353  Validity 01.01.1950 -
Name Probabilistic inference for data science 2  Abbreviation DTN2 
Credits 5 Cp Date of expiry  
TypeIntermediate Studies Subject0390 Statistics 
ClassStudy Unit  Hours  
Study right   Grading0-5 
Recommended scheduling 
Organisation Computer Science (J,K) 

Learning outcomes 

The student knows basics of statistical estimation and inference in the analysis of independent and dependent data


The joint distribution of a random sample, statistic, sample mean, sample variance and sample covariance and their properties. The concept of estimator and its central properties (sufficient statistic, bias, variance, root mean square error and efficiency). Law of large numbers and the central limit theorem. Estimation methods: maximum likelihood, least squares and Bayesian estimation.

Modes of study 

Self-study based on web material (115 h), lecture (2 h), contact teaching 14 h, written examination 4 h.

Teaching methods 

Student prepares for the weekly tutorials by (1) reading given material and watching videos at moodle, (2) doing a related weekly exam and (3) starting the weekly exercises. In tutorials there is also possibility to make some questions on weekly topics. The student (4) returns the weekly exercise report to moodle by the deadline and (5) after the submission deadline, reads the correct answers and self-evaluated his/her own report.  Overall, the students need approximately 15 hours per week for the course, in addition to the 2 hour contact sessions.

Study materials 

Handout and other material available in Moodle.Morris H. DeGroot ja Mark J. Schervish 2012, Probability and statistics (4 painos), parts of chapters 5-10 or Casella& Berger 2001. Statistical Inference (2. painos), parts of chapters 5-10                     

Evaluation criteria 

Grade (0-5) is based either (1) on a weighted mean of weekly exercises, weekly exams and written exam or (2) on the written exam only, so that the grade is the better one out of these two.


Probabilistic inference for data science 1, Basics in Statistics is recommended


Spring semester

Offering data 

Course is open to anyone. No restrictions in the number of participants. Useful also for PhD students who need statistical methods in their research

Further information 

Teaching language: English


Letter (J, K) in front of the name of the course/exam indicates the campus on which teaching or exam takes place: J = Joensuu, K = Kuopio.

Present and future teaching
Functions Name Type Cp Teacher Timetable
Registration not started
(KUOPIO): Probabilistic inference for data science 2  Multi-modal teaching  Markku Kuismin  22.03.22 -21.06.22
Registration not started
(JOENSUU): Probabilistic inference for data science 2  Multi-modal teaching  Markku Kuismin  22.03.22 -21.06.22

Future exams
No exams