Universitat Internacional de Catalunya

Informatics

Informatics
3
12736
2
First semester
OB
TECHNOLOGY TRAINING
INFORMATICS AND GRAPHICAL DESIGN TECHNIQUES
Main language of instruction: English

Other languages of instruction: Catalan, Spanish

Teaching staff


Positive participation in class will be factored into the final grade.

Introduction

Digitalization has had a huge implication for research and development as well as management in all sectors, but the exact sciences have particularly been transformed.  Examples of the impact of digitalization can be seen in virtual experimentation and simulation, high throughput experimentation, and big data. To prepare students to take their first steps in this new research landscape, this course will introduce them to the data in general, big data, logical planning and manipulation of data and the foundation of programming. 

It is likely in the future that all professionals will require basic data literacy and logical thinking, as technologies become more central in daily activities. Such skills are especially relevant for bioengineers, whose profession involves a close relationship with data acquisition, analysis and presentation.

Pre-course requirements

None.

Objectives

  1. Basic data literacy: To acquire familiarity with terminologies and ideas relating to data and big data
  2. Logical thinking: To practice logical thinking and skills related to planning and presentation of structured ideas and processes 
  3. Python: to acquire basic syntax and programming skills and practice writing and debugging code 

Competencies

  • CB1 - Students must demonstrate that they have and understand knowledge in an area of study based on general secondary education. This knowledge should be of a level that, although based on advanced textbooks, also includes some of the cutting-edge elements from their field of study.
  • CB2 - Students must know how to apply their knowledge to their work or vocation in a professional way and have the competences that are demonstrated through the creation and defence of arguments and the resolution of problems within their field of study.
  • CB3 - Students must have the ability to bring together and interpret significant data (normally within their area of study) and to issue judgements that include a reflection on important issues that are social, scientific or ethical in nature.
  • CG10 - To know how to work in a multilingual and multidisciplinary environment.
  • CG4 - To resolve problems based on initiative, be good at decision-making, creativity, critical reasoning and communication, as well as the transmission of knowledge, skills and prowess in the field of Bioengineering
  • CT5 - To use information sources in a reliable manner. To manage the acquisition, structuring, analysis and visualisation of data and information in your specialist area and critically evaluate the results of this management.
  • CT6 - To detect gaps in your own knowledge and overcome this through critical reflection and choosing better actions to broaden your knowledge.
  • CT7 - To be fluent in a third language, usually English, with a suitable verbal and written level that is in line with graduate requirements.

Learning outcomes

After successfully completing the course, the student will be able to:

  1. Describe the characteristics, impact and technical challenges of big data and digitalization
  2. Understand data types in statistics and how they may be analyzed, interpreted and presented
  3. Understand the basics of programming and be able to plan a simple program logically 
  4. Be able to write a simple python program, including functions, conditional statements and loops. Understand python packages, data structures and debugging. Acquire the vocabulary related to these technical skills. 

Syllabus

1. Data and statistical variables

2. Big data 

3. Introduction to programming 

4. Python variables

5. Conditional execution

6. Functions

7. Iterations

8. Data structures

9. Using databases

Teaching and learning activities

In person



  1. Theoretical lectures 
  2. In-class practical sessions
  3. Personal assignments
  4. Optional exercises at home
  5. Discussions and informal quizzes

Evaluation systems and criteria

In person



First sitting: 

The final grade of the course is calculated according to the following model:

  1. Group and individual assignments, participation and effort: 10% 
  2. Final exam: 90% 

 

A minimum mark of 5 is required for passing the final exam. In total a minimum global mark of 5 is required to pass the course.

 

Second sitting

Students who have not passed the subject in the first sitting will have the opportunity to return to take a final exam. The midterm exam, deliveries and multiple choice exam marks will remain unchanged. The evaluation criteria will be the same as in the first sitting.

Therefore, an essential requirement to pass the subject is to obtain a grade greater than or equal to 5 in the final exam of the second sitting..

 

Important considerations:

  1. Plagiarism, copying or any other action that may be considered cheating will be zero in that evaluation section. Besides, plagiarism during exams will mean the immediate failing of the whole subject.
  2. In the second-sitting exams, the maximum grade students will be able to obtain is "Excellent" (grade with honors distinction will not be possible).
  3. Changes of the calendar, exam dates or the evaluation system will not be accepted.
  4. Exchange students (Erasmus and others) or repeaters will be subjected to the same conditions as the rest of the students.

Bibliography and resources

Main readings:

(1) Severance CR. Python for Everybody: Exploring Data Using Python 3. Creat Commons. 2016; 

Further readings:

1. Donoho D. 50 Years of Data Science. Journal of Computational and Graphical Statistics. 2017. 

2. Ristevski B, Chen M. Big Data Analytics in Medicine and Healthcare. J Integr Bioinform. 2018 May 10;15(3).

Evaluation period

E: exam date | R: revision date | 1: first session | 2: second session:
  • E1 13/01/2023 A04 14:00h