GRA 4142 Data Management and Python Programming

GRA 4142 Data Management and Python Programming

Course code: 
GRA 4142
Department: 
Economics
Credits: 
6
Course coordinator: 
Alfonso Irarrazabal
Course name in Norwegian: 
Data Management and Python Programming
Product category: 
Master
Portfolio: 
MSc in Business Analytics
Semester: 
2018 Autumn
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

In the new economy data will be an ever more important aspect of business. Firms are slowly reacting to more data available on consumer behavior and markets in general. Competence in programming skills is essential to extract information and knowledge from data.

Advances in our capability to generate and collect information are pushing us toward a business world centered around data. Databases are currently at the heart of modern organizations. This course covers the basic concepts of data management, database systems, and the use of databases in business applications.

The goals of this course are twofold:
i) to introduce learners to the basics aspects of Python programming with a special focus on data analysis
ii) to provide adequate technical detail related to capturing, cleaning, and accessing data, while emphasizing the organizational and implementation issues relevant in an organization environment.

 

Learning outcomes - Knowledge

First part

  • Understand basic concepts of Python programming.
  • To gain basic knowledge in data analysis.

Second part

  • Understand the principles of good database design;
  • Gain an understanding of relational database management systems;
  • Develop an understanding of Structured Query Language (SQL);
  • Comprehend how database systems are used for strategic and operational decision making

 

Learning outcomes - Skills

First part

  • Learn basic blocks of Python programming such as variables, datatypes, loops, conditionals, functions etc.
  • Learn basic skills for data analysis to handle, analyze and visualize data using the Pandas package.

Second part

  • Develop entity-relationship diagrams, relational schemas, and data dictionaries for a database depending on a set of business rules;
  • Write SQL statements for a variety of data definition and data manipulation scenarios;
  • Being able to design data architecture solutions for several application needs and evaluate existing commercial database management systems in terms of these needs.
  • Be able to interface Python programs with a database.
Learning Outcome - Reflection
  • To evaluate the veracity of several types of data and decide whether it is meaningful to the problem being analyzed.
  • Privacy and confidentiality concerns that may emerge from the use of sensitive data.
Course content

This courses introduces the students to Python programming. Students will use the Python syntax to work with, among others, different data types, loops, and functions.

A second component of this course is to learn the principles of working with relational databases. Students will learn the principles of good database design, as well as the practical aspects of retrieving data from such databases using SQL. Finally, they will know how to interface programs written in python with a database

Learning process and requirements to students

The course will be a combination of lectures and tutorials. 

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class that is not included on itslearning or text book.

All parts of the assessment must be passed in order to get a grade in the course.

Software tools
No specified computer-based tools are required.
Additional information

-

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41421
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
50No72 Hour(s)Group/Individual (1 - 3)
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41422
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
50No72 Hour(s)Group/Individual (1 - 3)
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:50
Invigilation:No
Grouping (size):Group/Individual (1-3)
Duration:72 Hour(s)
Comment:
Exam code: GRA 41421
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:50
Invigilation:No
Grouping (size):Group/Individual (1-3)
Duration:72 Hour(s)
Comment:
Exam code: GRA 41422
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam organisation: 
Ordinary examination
Total weight: 
100
Sum workload: 
0

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.