Course code BUS350

BUS350 Introduction to Data Analytics

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Showing course contents for the educational year 2022 - 2023 .

Course responsible: Erlend Dancke Sandorf, Dag Einar Sommervoll
ECTS credits: 5
Faculty: School of Economics and Business
Teaching language: EN
(NO=norsk, EN=Engelsk)
Limits of class size:
120 students
Teaching exam periods:
This course starts in the Autumn parallel. This course has teaching/evaluation in the Autumn parallel.
Course frequency: Annually. 
First time: Study year 2020-2021
Preferential right:
Preference is given to students at the School of Economics and Business and masters students. The course is open to bachelor students if the pre-requisites are met.
Course contents:

The course aims to facilitate data driven decision making by giving students fundamental knowledge about data gathering, data structures, analysis, and visualization. The course will cover the following topics:

  • Common data structures (flat and relational databases including basic SQL)
  • Data analysis tools and interfaces (R, RStudio and RMarkdown)
  • Data gathering, preparation and exploration ("data wrangling")
  • Data aggregation and feature engineering
  • Data and model visualization (ggplot)
  • Data modeling
Learning outcome:

Knowledge:

  • Understand the properties of raw data structures and their implications for the use of data analysis techniques
  • Know common database structures and their implication for data management and data extraction
  • Know important techniques for data preparation, transformation, aggregation, and exploration ("data wrangling")
  • Understand how these pre-analysis choices affect analysis and interpretation of results

Skills:

  • Have basic skills in R and be able to work with these in an appropriate interface (e.g., RStudio or RMarkdown)
  • Be able to take "messy" raw data and prepare it for analysis
  • Be able to perform basic feature engineering tasks, e.g., variable selection, transformation, and aggregation
  • Be able to create informative data analysis visualizations
  • Create reproducible reports using, e.g., RMarkdown

General competence:

  • Effectively communicate results using text, tables, and visualizations
  • Understand what compromises may be necessary in the data analysis process from raw data to discussion of results and how these may affect, or bias, the analysis
Learning activities:
Flipped classroom with online videos and resources available on Canvas, computer labs and independent work with weekly problem sets that can be submitted for additional feedback.
Teaching support:
In-person computer labs with teaching assistants. Computer labs will be primarily on campus, but one will be held online.
Syllabus:
https://r4ds.had.co.nzR for Data Science by Hadley Wickham & Garrett Grolemund, which is available under a https://creativecommons.org/licenses/by-nc-nd/3.0/us/CC BY-NC-ND 3.0 License.Pre-recorded videos on CanvasAdditional links and reading materials will be provided on Canvas at the beginning of the semester
Prerequisites:
MATH100 Introductory Mathematics or ECN102 Introduction to Mathematics for Economists, or equivalent; and STAT100 Statistics or equivalent.
Recommended prerequisites:
None
Mandatory activity:
None
Assessment:
Portfolio assessment comprising 4 assignments. The course is graded pass/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 / Not Passed