Universitat Internacional de Catalunya
Computational Designing
Other languages of instruction: English, Spanish,
Teaching staff
Introduction
The ubiquitous digitalization of life has become a new reality for organizations, sectors and society in general. Whenever we think of new ways to create value in organizations and businesses, we invariably look in the direction of digital technologies for a helping hand. At the core of those digital technologies are software applications and the algorithms underlying those applications. In this course, participants will develop algorithmic thinking skills that will enable them to navigate complex problems. In particular, participants will build a core foundation in algorithmic thinking, the Python programming language, and how to use it for data analysis. This is a hands-on course where participants will actively program. The course concludes with a project that cohesively unites all the concepts covered. This course does not require any prior knowledge of programming.
Pre-course requirements
There are no prerequisites except basic knowledge of statistics
Objectives
Use algorithmic thinking to solve problems.
Programming Effectively in Python
Apply data analysis techniques with Python
Learning outcomes of the subject
At the end of the course students should be able to:
Use algorithmic thinking to solve problems.
Programming Effectively in Python
Apply data analysis techniques with Python
Syllabus
INTRODUCTION
1. Computational thinking
2. Work environment: Google Colab
FIRST STEPS IN THE PYTHON LANGUAGE
1. Types of data
2. Flow control
3. Data structures
MORE ADVANCED CONCEPTS
1. Functions
2. Libraries
ANALYSIS OF DATA
1. Data frames
2. Basic statistics
Teaching and learning activities
In person
This is a hands-on course that requires participants to be active in their own learning.
Participants must carry out autonomous work to prepare each session.
This preparatory work consists of reading the content of the next session and reviewing the previous session.
Details of the preparatory work can be found on the course website.
The session will build on and take advantage of the preparatory work of the participants.
The face-to-face sessions will be used to review the preparatory work, resolve doubts, carry out new exercises, individual questionnaires, team activities and challenges.
This course requires all participants to bring their own device (laptop or tablet) to the sessions to perform different activities.
Class attendance is mandatory.
In several sessions, participants will carry out team activities.
Individual tests and team activities, which will be graded, will be posted on the course website prior to the session.
Evaluation systems and criteria
In person
The grade will be calculated based on these concepts and weights:
1. 70% final exam
2. 20% deliver individual exercises
3. 10% Assistance
Bibliography and resources
- Downey, A. (2020) “Think Python: How to Think Like a Computer Scientist”, O’Reilly Media, Inc.
- The Python Language Reference, https://docs.python.org/3/reference/
- The Python Standard Library, https://docs.python.org/3/library/index.html