Course code BUS350

BUS350 Introduction to Data Analytics

There may be changes to the course due to to corona restrictions. See Canvas and StudentWeb for info.

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Showing course contents for the educational year 2021 - 2022 .

Course responsible: Erlend Dancke Sandorf, Dag Einar Sommervoll
ECTS credits: 5
Faculty: School of Economics and Business
Teaching language: EN
(NO=norsk, EN=Engelsk)
Teaching exam periods:
This course starts in the Autumn parallel. This course has teaching/evaluation in the Autumn parallel.
Course frequency: Annually. 
First time: 2020H
Course contents:

The course aim to fasilitate data driven decision making. Data based decision making requires knowledge about data gathering, structure, analysis and visualization. The course is divided into six parts.

  • Common data sources (relasjonsdatabaser,SQL)
  • Data analysis tools (Excel,Python, R)
  • Interfaces (Rstudio Rmarkdown, jupyter)
  • Data gathering, data preparation, data exploration
  • Aggreation og feature engineering
  • Visualization (ggplot, leaflet)
Learning outcome:

Knowledge:

  • Understand the properties of raw data structures and their implications for the use of analytics techniques,
  • Know common database structures and understand their implications for data management and data extraction,
  • Know important data aggregation and data transformation techniques,
  • Understand their effect on levels of measurement and their implications for the interpretation of estimation results and predictions.

Skills:

  • Have basic skills in R and Python and be able to use these in an apropriate interface (Rstudio, Rmarkdown, jupyter-notebook).
  • Be able to perform basic feature engineering tasks (variable selection,transformations, feature testing),

General competence:

  • Have an understanding of what compromises a complete analysis from data gathering to discussion of results.  The student should be aware of how choices made in all stages of the data analysis may bias the analysis.
Learning activities:
Lectures, flipped classroom activities, computer lab, independent work on exercises.
Teaching support:
Canvas, flipped-classroom activities.
Syllabus:
Detailed readings will be announced on the Canvas page of the course in the beginning of the semester.
Prerequisites:
MATH100 Introductory mathematics or ECN102 Introduction to mathematics for economists; STAT100 Statistics 
Recommended prerequisites:
Mandatory activity:
None
Assessment:
Folder evaluation. 3 assigments + 1 midterm exam. All elements are graded Passed/Not Passed.
Nominal workload:
125 hours. This is a work-intensive course.
Entrance requirements:
This course is open only for students in at the School of Economics and Business.
Type of course:
Two lecture hours per week (September to December). In addition, intensive work on exercises.
Note:
The course will be taught in English. Incoming students can contact student advisors at the School of Economics and Business (studieveileder-hh@nmbu.no) for admission to the course. 
Examiner:
External examiner will control the quality of syllabus, questions for the final examination, and principles for the assessment of the examination answers.
Examination details: Portfolio: Passed / Failed