Content

Ders Adı (Course Name)

Computing in Molecular Biology and Genetics I

Course Adaptation (I don’t know what this means)

Var

Compulsory Attendance

Theory: 70, Practice: 80, Lab: 0 (I don’t know what this means)

Course Teacher(s)

Andres Aravena

Purpose

The students will learn how to handle experimental data and how to communicate it to scientists of other data-oriented disciplines.

Course Learning Outcomes

Students will learn how to produce publication quality reports with reproducible results. How to get raw data, extracting relevant information, filter it using several selection criteria. How to store and retrieve it in efficient and useful ways. How to transform it, organize it, categorize it, display, show and understand the results. Tools include Unix command line tools, SQL and the R statistical package. The student should be able to understand how computer networks work and what are their limitations.

Course Content (Short Description)

This course teaches the basic elements of data handling techniques in modern computing systems that allow the management and understanding of experimental results.

Teaching and Learning Methods

Exposition of theoretical aspects, followed by practical exercises by the students. Student work is encouraged.

Resources

Online resources about R. There are plenty of them.

Course Material (Auxiliary Equipment, modal etc.)

Slide Presentations, Data Show, Blackboard, computer network

Continuous Improvement in the Context of the courses (questionnaires, interviews, and so on.) Front Shown Measurement and Evaluation Tools and Objectives

Quiz, Project working and homework are used effectively to improve the course. At the end of Mid-term and Final Exams we will report which are the questions with the lower number of correct answers between the students with high attendance rate. According to this results lecture notes are updated and additional homework activities are recommended.