Juha Tuomi

Data Science Course

Time:    30.10. – 18.12.2019
Place:    Linnanmaa Campus
Extent:  5 cr

Lecturer of the course is Professor Ahmed Elragal, Information systems, Luleå University of Technology.

The Data Science course will cover a number of topics, including data to be mined and data mining strategies. The techniques will be studied in association with the algorithms needed to implementing them. The course will also rely on business cases. That is, each technique will be studied in association with a business scenario. This will enhance understanding of the techniques and equip the learner with the necessary knowledge and skills required to formulate and solve mining problems.

  • The course will run in mixed mode, i.e. lectures on-campus & online; while most lyonline.
  • The course will run based on BYOD - bring your own device. The needed software will be distributed to the regostered participants with the course welcome message.
  • The course is in English language.

Entry requirements
The course is targeted mainly to post-graduate students in technical faculties. Graduate students near graduation from suitable disciplines will also come to question.

In order to meet the general entry requirements for the data science course, you must have accomplished a minimum of 120 ECTS of university studies, out of which 60 ECTS in the areas of computer or system science. Added to that, you must have studied at least one Database course or have similar knowledge on the subject.

Course Aims
Data science is the discovery of patterns and hidden information in large datasets. This course aims at the understanding of the data science concepts and techniques. The course provides students with the detail about most aspects of data mining and knowledge discovery, focusing on techniques and algorithms in respect to how they are used to solve business problems.

After this course the student will be able to:

  1. Understand what is data science;
  2. Differentiate between knowledge discovery in database and data mining;
  3. Describe data mining as a process;
  4. Explain the CRISP-DM process;
  5. Describe the different applications where data mining is used;
  6. Understand the different data mining techniques and algorithms;
  7. Analyze the performance of data mining techniques and algorithms;
  8. Evaluate the mining outcomes;
  9. Understand how to formulate and solve business problems using data mining.

Realization
During the course, students will work on individual task and a group task. For group work, students will collaborate with each other using a variety of collaboration tools. Also, students will be provided access to Rapid Miner, once of the world’s leading mining tools in order to solve business problems and cases. Also, students will be working on R Programming languages to solve business problems.

Examination
Group tasks 2,5 credits
Written examination, 2.5 credits

Delivery Schedule
The course consists of 10 lectures, as the below table:

Tools
In the course we will be using RapidMiner Software and R Programming Language. Two of the most commonly used tools in Data Science.

Assignments
The course includes five assignments, as follows:

Registrations
Registration in the course is closed..

More information
For more information, please contact Post-doctoral researcher Jari Ruuska (firstname.lastname at oulu.fi)

Source of the image: http://sudeep.co/data-science/Understanding-the-Data-Science-Lifecycle/

 

 

Last updated: 30.10.2019