Study unit
3621680 Algorithmic Data Analysis, 5 Cp  
Code 3621680  Validity 01.01.1950 -
Name Algorithmic Data Analysis  Abbreviation ADA 
Credits 5 Cp Date of expiry  
TypeAdvanced Studies Subject0530 Computer Science 
ClassStudy Unit  Hours  
Study right   Grading0-5 
Recommended scheduling 
   
Organisation Computer Science (J,K) 

Description
Learning outcomes 

Student understands: the possibilities and limitations of algorithmic data analysis; the applications and prerequisites of different data analysis methods and algorithms; the effects of the different methods to the outcomes of the data analysis process; the connections of individual methods to larger families of methods.

Student knows the different applications of data analysis and can choose the correct algorithm for the problem. Student can preprocess the data for the algorithm. Student can interpret the results of the algorithms. Student understand new methods from the literature and their connections to existing methods.

Student can do independent data analysis using existing tools and implementations. Student can also create novel methods based on the existing ones and can understands and can implement methods from the literature.                          

  

 
Content 

Advanced classification methods. Mining data streams. Mining temporal and spatial data. Outliers detection. Social network analysis. Python is the programming language used in the course.              

 
Modes of study 

Lectures, theoretical and programming exercises, exam.

 
Teaching methods 

Lectures 30 h, exercises 16 h                              

 
Study materials 

Lecture material. Aggarwal: Data Mining - The Textbook. Leskovec, Rajaraman & Ullman: Mining of Massive Datasets                                                        

 
Evaluation criteria 

Grading: 0–5, based on exam and assignments

 
Prerequisites 

Introduction to Algorithmic Data Analysis (or equivalent knowledge). Probabilistic Inference for Data Science (or equivalent knowledge). Design and Analysis of Algorithms (or equivalent knowledge).                        

 


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 ended
(KUOPIO): Algorithmic Data Analysis  Lecture and exercise course  Esther Galbrun  28.10.19 -31.01.20
Registration ended
(JOENSUU): Algorithmic Data Analysis  Lecture and exercise course  Esther Galbrun  28.10.19 -31.01.20

Future exams
No exams