LOG904-153 Big Data (Autumn 2018)

ECTS Credits:
2.5
Responsible faculty:
Avdeling for logistikk
Campus:
Molde
Course Leader:
Berit Irene Helgheim
Teaching language:
English
Study level:
{code=HN, name=Second degree level}
Duration:
½ year
Schedule (TP) Reading list (Leganto)

Table of contents

Contents

Overview of Data Science, What is Big data, Data Analitics. Case studies and success history. Brief description of main algorithms. Introdution to R, use R for data exploration, data wangling, data analysis, data visualization, feature engineering and machine learning. Application to a local case study¿. The tpoics covered in this seminar are: Data Analitics, data manupation, outliers identification, main algorithms, data mining. Using R platform to help on this data manipulation process.

The course is connected to the following study programs

The student's learning outcomes after passing the exam

Give the student an overview of data science and new challengers on big data. First steps for non computer science student on data manipulation towards the knowledge extraction from raw data.

Forms of teaching and learning

60% pratical using R platform ¿ 40% theoretical

Exam

  • Form of assessment: Home assessment
  • Proportion: 50%
  • Duration: -
  • Grouping: Group
  • Grading scale: Letter (A - F)
  • Support material: All printed and written supporting material
  • Form of assessment: Written school assessment
  • Proportion: 50%
  • Duration: 3 Hours
  • Grouping: Individual
  • Grading scale: Letter (A - F)
  • Support material: Only general dictionary in mother tongue/Norwegian/English in paper version
Last updated from FS (Felles studentsystem) Oct. 14, 2026 4:31:27 AM