GRA 4151 Python for Data Analysis - SUMMER COURSE
GRA 4151 Python for Data Analysis - SUMMER COURSE
Python for Data Analysis is a master-level summer course in programming and data analysis using the Python programming language and its libraries for data processing, analysis and visualization (in particular, 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.
The course is offered to students of the following programmes:
- MSc in Business
- MSc in Leadership and Organisational Psychology
- MSc in Entrepreneurship and Innovation
- MSc in Digital Communication Management
Note that students are not allowed to take both this course and GRA 4142 Data Mangement and Python Programming due to a large overlap in the course content. This applies in particular to students of MSc in Business in which GRA 4142 is offered as an elective course. For the same reason this course is not open to students of MSc in Business Analytics (in which GRA 4142 is a mandatory course).
The course will be run physically on campus in Oslo, without any streaming/recording.
Note that this is a very challenging course and the students should be prepared to dedicate 8 hrs per day (including the lectures, self-studies and solving exercises).
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.
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.
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.
- 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.
- 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).
- Sessions combining classical lecture with discussing and solving practical problems (40 hours).
- Homework exercises.
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.
| Assessments |
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Exam category: Submission Form of assessment: Submission other than PDF Exam/hand-in semester: First Semester Weight: 100 Grouping: Individual Duration: 30 Hour(s) Exam code: GRA 41511 Grading scale: Pass/fail Resit: Examination when next scheduled course |
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.