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CS38A0050 Big data in business and industry, 6 cr 
Code CS38A0050  Validity 01.08.2017 - 31.07.2019
Name Big data in business and industry  Abbreviation Big data in bus 
Credits6 cr   
TypeBasic studies  
  Grading scaleStudy modules 0-5,P/F 
  Eligibility for post-graduate studiesno
    Allowed to study several timesno
Organisation LUT School of Engineering Science 

Description by Study Guide
Year  M.Sc. (Tech.) 2 
Teaching Language  English 
Teacher(s) in Charge 

Jyrki Savolainen, D.Sc., Post-Doctoral Researcher

(Jozsef Mezei, D.Sc., Research Fellow)


The course discusses the most important new tools for understanding the potential impact of big data analytics on decision making and business performance. Through analyzing typical business decision problems from the perspective of data requirements, the course discusses the role of big data analytics in modern organizations. After the completion of the course, the students: know the most important technological requirements of performing big data analytics, understand the role of big data in transforming modern organizations through data driven decision making, understand the impact of data volume, variety, and velocity, understand how to create value with big data, become familiar with the techniques and tools for capturing, processing, and interpreting big data, know the most important methods to reduce big data sets by extracting the most important information, are familiar with several real-world scenarios of big data use from different business sectors, understand the role of big data in creating business value, know how to apply the discussed concepts and tools to business projects.


Core content: big data technology, data and dimension reduction, role of data driven decision making in modern organizations.
Additional content: machine learning methods for big data analytics, network analysis
Special content: text analytics

Teaching Methods 

Lectures 20 h, computer room tutorials 10 hours, course assignments involving big data analysis (using relevant software) 75 h. Written exam and preparation for the exam 55 h. Total workload for the student 160 h.

Examination in Examination schedule (Yes/No)  Yes 
Examination in Moodle (Yes/No)  No 
Examination in Exam (Yes/No)  No 
Assessment scale and assessment methods 

Course assignments (70% of the grade), written examination (30% of the grade), grading 0-5.

Course Materials 

The following two books cover several topics introduced in the course:
Thomas Davenport, 2015: Big Data at Work

The rest to be annouced later.

Additional material will be distributed in the course.


The course will rely on using software, relevant knowledge of the used software required; TBA (Matlab or R)
Basic knowledge in statistics.

Limitation for students? (Yes, number, priorities/Leave empty)  60 
Places for exchange-students? (Yes, number/No)  No 
Places for Open University Students?(Yes, number/No)  No 

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