Course code BUS350

BUS350 Introduction to Data Analytics

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Showing course contents for the educational year 2020 - 2021 .

Course responsible: Joachim Scholderer, Atle Guttorm Guttormsen
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. Cancelled in 2020.
First time: Study year 2020-2021
Preferential right:
-
Course contents:

The aim of this course is to prepare participants for the "analytics-heavy" specialisation courses offered in our master's programmes in business administration, data science and entrepreneurship & innovation. The course will consist of six parts:

  • Typical data structures in business analytics, logistics and finance (event logs, time series, RFM data, relational data),
  • Typical database structures in business analytics, logistics and finance (relational databases, data warehouses, data lakes),
  • Analytics platforms (Python, R, SAS),
  • Important data transformations (log, logit, probit),
  • Data aggregation and feature engineering (raw data aggregation with SQL, low-rank approximation with PCA, clustering),
  • Business intelligence and data visualisation platforms (Power BI, Tableau).
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:

  • Be able to use general-purpose analytics platforms such as Python, R and SAS,
  • Be able to use basic SQL queries for data extraction and aggregation,
  • Be able to perform basic feature engineering tasks (transformations, PCA and clustering) with time series data, aggregated event log data and panel data,
  • Be able to develop dashboards using business intelligence and visualisation platfroms such as Power BI and Tableau. 

General competence:

  • Understand the importance of analytics and information systems in modern, data-driven businesses,
  • Become able to independently develop further analytics skills by progressing from a broad basic foundation. 
Learning activities:
Lectures, flipped classroom activities, 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:
INF120 Programming and data processing
Mandatory activity:
Completion of four mandatory assignments and participation in two one-hour multiple choice tests.
Assessment:
Continuous exam, consisting of four mandatory assignments (weight: 15% each) and two one-hour multiple choice tests (weight: 20% each).
Nominal workload:
150 hours. This is a work-intensive course.
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: :