Content

1. Communication in Science.

A scientist generates products that need to be marketed conveniently. This part of the subject leads the student to realize that the development of skills to communicate scientific results in an effective manner is, at least, as
important as generating them. Being English the lingua franca among scientists, all activities will be conducted in this language. 

In essence, the course consists of:

Lecturing: Speaking to an audience about your research is a privilege and a great occasion to know and be known. However your product (your science) may not reach the customer (the audience). Beware of Power Point-induced sleep, make the simplest possible slides, use body language wisely, make eye contact with the audience, respect your time limits, and much more. 

Poster design: Effective poster design is much more than merely putting your figures together and fitting some text in between. Consider yourself in the middle of a 400-poster session, competing with every one to attract
the attention of that important scientist coming down the hall, with whom you want to talk. Just passing by, the big guy may ask you, "hum, what have you done here?" Unless you say something captivating within 15
seconds, his eyes might be already on the next poster.

Paper writing: What to publish, where, and how. We will emphasize abstract (summary) writing. Abstracts, unlike most beginners may think, is one of the trickiest parts of scientific writing. Most potential readers of your
paper will only devote a few seconds to read your abstract from scientific databases. If you don't catch their attention, you have failed. Within this paper writing part, we will get into the peer-review system.

Additionally, we can discuss some science ethics andthe science and art of fundraising.

2. Statistical Analysis of Experimental Data.

Introduction. Statistics is a central issue for experimentalists, both before and after the experiments are performed. In the former case because careful experimental design is needed if we want the experiment yields right answers to the questions we are asking for and in the latter case because data sets resulting from experiments need systematic and accurate analyses in order to produce unbiased and reproducible conclusions. Variability is inherently linked to biology and statistics is responsible for variability modeling, that is, for separating the diverse sources of error to identify trends, associations, correlations helpful for exploring
the intricate jungle of life sciences.

Objectives. The subject comprises a basic course on statistics. The fundamental objective is to qualify the students for accurate analysis and interpretation of experimental data.

Contents. 1. An introduction to the statistical package. 2. Working with data in a project. 3. Monovariate and bivariate descriptive statistics. 4. Random variables and probability distributions. 5. Statistical inference: Estimation and hypothesis testing. 6. Analysis of the differences between two groups or conditions: two independent samples and paired data. 7. Analysis of the differences between two or more groups: Analysis of variance (ANOVA). 8. Two-way ANOVA. 9. Linear regression.