GRA 4142 Data Management and Python Programming-SUMMER COURSE

GRA 4142 Data Management and Python Programming-SUMMER COURSE

Course code: 
GRA 4142
Department: 
Data Science and Analytics
Credits: 
6
Course coordinator: 
Alfonso Irarrazabal
Jan Kudlicka
Course name in Norwegian: 
Data Management and Python Programming-SUMMER COURSE
Product category: 
Master
Portfolio: 
MSc Summer Courses
Semester: 
2021 Spring
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

SUMMER COURSE (This course description is for the summer course)

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

In general, students will develop analytical and digital skills associated to programming and data management. 

For the first part of the course, students will

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

 

  • Learn how to collect, manage and analyze data in with emphasis on applications relevant for international business.

For the second part, they will

  • 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.

 

General Competence
  • To evaluate the veracity of several types of data and decide whether it is meaningful to the problem being analyzed.
  • To demonstrate abilities of analytical and critical thinking.
  • Privacy and confidentiality concerns that may emerge from the use of sensitive data.
  • Explore the value of data in relation to corporate social responsibility and sustainability goals.

 

Course content

The first part of the course introduces students to Python programming. Students will use the Python syntax to work with, among others, different data types, loops, and functions.

The second part of this course will teach students the principles of working with relational databases. They 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.

Teaching and learning activities

Open-source software (will be specified during the first session).

The sessions will be organized as a mixture of lectures and working on in-class assignments.

In addition to the organized classes students are expected to prepare for the sessions by watching selected videos online and/or reading selected texts. Students are also expected to work on (ungraded) assignments after each session, either individually or in small groups (of 2-3 students). More information will be given during the first session.

Note that the plan is preliminary and subject to small changes.

 

Software tools
Software defined under the section "Teaching and learning activities".
Additional information

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.

The exam for this course has been changed since autumn semester 2020. The course now has one exam instead of two. It is not possible to retake one of the old exam versions. If you want to improve your grade in the course you will have to retake the new 100% exam. The code for the new exam is GRA 41423.  

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.

Covid-19

Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.k

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41423
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
100No24 Hour(s)Individual
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:100
Invigilation:No
Grouping (size):Individual
Duration:24 Hour(s)
Comment:
Exam code: GRA 41423
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
36 Hour(s)
Group work / Assignments
100 Hour(s)
Prepare for teaching
12 Hour(s)
Examination
12 Hour(s)
Expected time: 8-16 hours.
Sum workload: 
160

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.