Big Data Analytics, 6 credits (732A54)

Analys av Big data, 6 hp

Main field of study

Statistics

Level

Second cycle

Course type

Programme course

Examiner

Olaf Hartig

Course coordinator

Olaf Hartig

Director of studies or equivalent

Patrick Lambrix
Course offered for Semester Weeks Language Campus VOF
F7MSL Statistics and Machine Learning, Master´s Programme - First and main admission round 2 (Spring 2021) v202113-202122 English Linköping o
F7MSL Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students) 2 (Spring 2021) v202113-202122 English Linköping o

Main field of study

Statistics

Course level

Second cycle

Advancement level

A1N

Course offered for

  • Master's Programme in Statistics and Machine Learning

Entry requirements

A bachelor’s degree in one of the following subjects: statistics, mathematics, applied mathematics, computer science, engineering, or equivalent. Completed courses in calculus and linear algebra are required. Completed courses in statistics covering at least 6 ECTS credits and a course in programming covering at least 6 ECTS credits are also required.

Documented knowledge of English equivalent to Engelska B/Engelska 6. 

Intended learning outcomes

After completed the course, the student should on an advanced level be able to:
- collect and store Big Data in a distributed computer environment
- perform basic queries to a database operating on a distributed file system
- account for basic principles of parallel computations
- use MapReduce concept to parallelize common data processing algorithms
- account for how standard machine learning models should be modified in order to process Big Data
- use tools for machine learning for Big Data

 

Course content

The course introduces main concepts and tools for storing, processing and analyzing Big Data which are necessary for professional work and research in data analytics.

- Introduction to Big Data: concepts and tools
- Introduction to Python
- Basic principles of parallel computing
- Introduction to databases
- File systems and databases for Big Data 
- Querying for Big Data 
- Resource management in a cluster environment
- Parallelizing computations for Big Data 
- Basic Machine Learning algorithms
- Machine Learning for Big Data 

Teaching and working methods

The teaching comprises lectures and computer exercises. Lectures are devoted to presentations of theories, concepts and methods. Computer exercises provide practical experience of manipulation with Big Data. Homework and independent study are a necessary complement to the course. Language of instruction: English. 

Examination

Written reports on the computer assignments. Written examination. Detailed information about the examination can be found in the course’s study guide. 

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it. If the coordinator has instead recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.

Grades

ECTS, EC

Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.

Department

Institutionen för datavetenskap
There is no course literature available for this course.
LAB1 Laboratory work EC 3 credits
TENT Examination EC 3 credits

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