GRA 4151 Python for Data Analysis - SUMMER COURSE

GRA 4151 Python for Data Analysis - SUMMER COURSE

Course code: 
GRA 4151
Department: 
Data Science and Analytics
Credits: 
6
Course coordinator: 
Jan Kudlicka
Course name in Norwegian: 
Python for Data Analysis - SUMMER COURSE
Product category: 
Master
Portfolio: 
MSc Summer Courses
Semester: 
2022 Spring
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

Python for Data Analysis is a master-level summer course in programming and data analysis using the Python programming language and Python libraries for data processing, analysis and visualization (NumPy, Pandas, Matplotlib and Seaborn).

All sessions combine classical lectures with discussing and solving problems related to data analysis.

The course is intended for students with at least some basic programming experience (in any language). Experience with Python is not required. 

GRA 4151 Python for Data Analysis is offered for MSc in Strategic Marketing Management, MSc in Leadership and Organisational Psychology, MSc in Business, and MSc in Applied Economics.

The course is not open to students of MSc in Business Analytics (the contents of this course is covered by GRA 4142 Data Mangement and Python Programming). Students in MSc in Business cannot do both GRA 4151 and GRA 4142 since the courses are equivalent.

Learning outcomes - Knowledge

Upon completion of the course the student shall be able to:

  • understand, explain and use fundamental programming concepts, including:
    • syntax and semantics,
    • variables,
    • types,
    • basic data structures,
    • expressions and statements,
    • control flow (conditionals and loops),
    • functions and libraries,
    • input/output operations,
    • exceptions

with focus on the Python programming language.

Learning outcomes - Skills

Upon completion of the course the student shall be able to:

  • use integrated development environments,
  • design, implement, run, test and debug programs in Python based on a given textual description of a problem,
  • process, analyze, summarize and visualize datasets using Python, NumPy, Matplotlib and Seaborn and other libraries,
  • read and understand Python source code implemented by others.
General Competence

Upon completion of the course the student shall have stronger competence in:

  • processing and analyzing data with help of computers,
  • using online resources as aids to solve problems,
  • reading and understanding technical documentation,
  • working in groups.
Course content
  • Introduction, installation of Python, Jupyter lab, IDEs.
  • Executing Python code.
  • Variables, basic types, user input and output.
  • Control flow (conditional execution, loops).
  • Organizing code (functions and libraries).
  • Data structures.
  • Strings, reading, writing and processing text files, regular expressions.
  • Vectors and matrices (NumPy).
  • Random numbers and the Monte Carlo method.
  • Processing and analyzing tabular data with Pandas (reading, cleaning, manipulating, grouping and aggregating data).
  • Plotting and visualization (Matplotlib, Seaborn).
  • Introduction to more advanced topics in data analysis and data science.
Teaching and learning activities
  • Sessions combining classical lecture with discussing and solving practical problems (40 hours).
  • Homework exercises.
Software tools
No specified computer-based tools are required.
Additional information

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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.

Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

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Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41511
Grading scale:
Pass/fail
Grading rules:
Two examiners
Resit:
Examination when next scheduled course
100No30 Hour(s)Individual
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:100
Invigilation:No
Grouping (size):Individual
Duration:30 Hour(s)
Comment:
Exam code:GRA 41511
Grading scale:Pass/fail
Resit:Examination when next scheduled course
Type of Assessment: 
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.