Bio Training in Riga

This instructor-led, live training in Riga (online or onsite) is aimed at scientists and anyone interested to learn about the basics of Nanoscience and its applications. By the end of this training, participants will be able to:

• Understand the field of nanosciences and nanotechnologies.
• Differentiate those in the nano range from those in the micro range.
• Know the fundamental theory of electron and energy transfer.

This instructor-led, live training in Riga (online or onsite) is aimed at healthcare professionals and workers who wish to learn intermediate to advanced-level knowledge of the different genetic sequencing and testing operations. By the end of this training, participants will be able to:

• Learn and understand key concepts of genetics.
• Understand how genetic sequencing and testing operations work.

This is a practical workshop, which will introduce basics of bioinformatics. It is aimed at people with basic biological background and knowledge in molecular biology, who want to learn how to process and work with biological data using computational tools. It has been designed especially for:

• Researchers working in experimental laboratories on biological macromolecules proteins, RNA and DNA.
• Scientists whose main area of expertise is not biology, but they need to cope with biological data.
• Managers introducing new products (e.g. drugs), who want to broaden their knowledge about nowadays bioinformatics.
• Computer programmers who are going to write programs dealing with biological molecules.

The course is divided into two parts: theoretical and practical. The lecture is interwoven with simple practical exercises, quizzes and tests to make sure that attendees understand the concepts. During the training we will take you along a guided tour that covers all the basic aspects of biological data analysis. This workshop is an ideal opportunity to get familiar with bioinformatics and its many faces! At the end, you should be able to tackle any basic bioinformatics problem: you will know where to get the required data from, how to obtain relevant information and how basic software and algorithms work. Our aim is to facilitate your understanding of biological data. By the end of the course you will be able to work with biological data using bioinformatics tools.

Good Clinical Practice (GCP) is a set of guidelines for clinical research done on human subjects. GCP serves as the international standard for designing, conducting, monitoring, documenting, analyzing, and reporting clinical trials. In this instructor-led, live training, participants will learn the most important guidelines and fundamental principles applied in Good Clinical Practice (GCP) for clinical trials and researches involving human subjects. This course provides an overview of the different GCP areas, but provides slightly more focus on the ICH-centric GCP for Investigational Drugs and Biologics. By the end of this training, participants will be able to:

• Understand the fundamental principles of GCP for investigational drugs and biologics as based on the International Conference on Harmonization (ICH)
• Apply the guidelines stipulated by the GCP to their clinical researches and trials

Audience

• Clinical investigators
• Clinical research professionals
• Healthcare and medical professionals
• Clinical trials support staff
• Research and development staff

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This course will provide an introduction to microbial genomics. The content is intended to provide a basic hands-on training on data analysis, tools and resources for microbial bioinformatics and metagenomics. Who are expected to attend?
Microbiologist, faculty, post doctorates, graduate students, research staff, and industry researchers interested in microbial genomics. Format of the course
Topics will be delivered using a mixture of lectures (20%), practical sessions (60%) and open discussions (20%). 
Practical work during the course will use small example datasets, but it is possible to work on your personal data.
This course will be held in a computer lab, which has desktop computers running Microsoft Windows OS with high speed internet connection.  However, you can use your own laptop, only be sure you have Chrome or Firefox and have a recent version of Java installed. 

This is a practical course, which shows why programming is a powerful tool in the context of solving biological problems. During the course participants will be taught the Python programming language, a language widely considered both powerful as well as easy to use. This course might be considered as a demonstration how bioinformatics improves biologists lives. The course is designed and aimed for people without computer science background who want to learn to program. This course is suited for:

• Researchers dealing with biological data.
• Scientists who would like to learn how to automate everyday tasks and analyse data.
• Managers who want to learn how programming improves workflows and conducting projects.

By the end of the course, participants will be able to write short programs, which will allow them to manipulate, analyse and deal with biological data and present results in a graphical format.

This course aims to give researchers an understanding of the principles of statistical design and analysis and their relevance to research in a range of scientific disciplines. It covers some probability and statistical methods, mainly through examples. This training contains around 30% of lectures, 70% of guided quizzes and labs. In the case of closed course we can tailor the examples and materials to a specific branch (like psychology tests, public sector, biology, genetics, etc...) In the case of public courses, mixed examples are used. Though various software is used during this course (Microsoft Excel to SPSS, Statgraphics, etc...) its main focus is on understanding principles and processes guiding research, reasoning and conclusion. This course can be delivered as a blended course i.e. with homework and assignments.