Bio Training Courses
Biology, Biotechnology, Bio Technology
Subcategories
Bio Course Outlines
| Code | Name | Duration | Overview |
|---|---|---|---|
| sccomm | Scientific Communication | 14 hours | During this workshop participants will be familiarised with basics of scientific communication including ways of preparing presentations, short articles and posters promoting scientific results. The theoretical course is interwoven with simple practical exercises, quizzes and tests to make sure that participants understand the concepts. During the training you will be taught how to present your results in a comprehensible and interesting way. By the end of the course you will be able to deliver a memorable speech and create a presentation that will be remembered. The course has been designed for people who want to learn how to present their work in an interesting, clear, understandable and memorable way. It has been designed especially for: Researchers presenting their scientific results at conferences and workshops Scientists applying for grants Managers who want to introduce their ideas and plans in a scientific environment Lecturers who want to learn how to run their courses in a professional way General rules of scientific communication Rules for preparing high quality scientific presentations Rules for preparing understandable and effective scientific figures and posters |
| bioinfbas | Basics of Bioinformatics | 21 hours | 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. Introduction Sequence alignments BLAST and its Applications Translation tools Primer Designing Epitope Prediction Secondary structure prediction Phylogenetic analysis |
| mgmint | Introduction to Microbial Genomics & Metagenomics | 21 hours | 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. The course will highlight key web-based resources, approaches and methodologies for DNA sequence data analysis for microbial genomics. The major topics to be covered will include: 16S/18S metagenomics and species identification. Multi locus sequence typing. SNPs and plasmids profiling. SNPs based phylogeny. Prediction of antibiotics resistant mechanism. |
| geneticsint | Introduction to Genetics | 14 hours | This course has been created for people who want to make reasonable decisions related to genetics, e.g.: managers introducing new products using genetically modified plants researchers whose main area or expertise is not genetics but they need to feed the results of genetic research into their work computer programmers who are going to deal with data related to DNA forensic experts, judges, lawyers, who need to make decisions based on DNA profiling (aka DNA testing or DNA matching) It can be helpful for people who simply want to understand how genetics works. It is a more narrative course in terms of the story, starting from the very origin of life and explaining how our genetic code has evolved over time and how can we trace the ancestry of human evolution. Using the story of evolution, the trainer introduces you into scientific lingo and explains the workings of genes, DNA and RNA, heredity and natural selection. The lecture is intertwined with simple exercises, quizzes and tests to make sure you understood the concepts and facts. This course does not cover moral, political or religious point of view of genetics, but presents confirmed facts, not theories or conjectures. The Evolution History of Earth History of Life How can we be so sure about "LUCA", the last universal common ancestor. How to read molecular sequences Why evolution is an established scientific fact, not a theory The "Cauldron" and appearance of RNA How primitive forms of life exchange their genetic code What can we read from fossil vestiges Two groups of bacteria How organism became multicellular From bacteria to humans How an organism work Metabolism How energy is extracted and used Reproduction Development Mutations and evolution Is natural selection a theory or a fact? Brief history of homo sapiens How do we know that we come from Africa? Prehumans (2mln years ago) Human brain and neurons The price for big brain Modern humans Genes and race Modern evidence of evolution Human DNA Genome, genetics, DNA, RNA and chromosomes explained Tracing maternal ancestors, mitochondrial DNA (mtDNA Tests) Tracing male ancestors (Y Line Tests, Y chromosomal DNA test) How cloning works? DNA matching, how a genetic code of a single cell related to a person DNA The Gnome project What causes Cancer HIV and AIDS Genetics and the Industry How can we change the DNA to improve food production The history of penicillin, we do not have to do it at random Genetics and live stock, current state and the future Genetics and drug production |
| statsres | Statistics for Researchers | 35 hours | 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. Scientific Method, Probability & Statistics Very short history of statistics Why can be "confident" about the conclusions Probability and decision making Preparation for research (deciding "what" and "how") The big picture: research is a part of a process with inputs and outputs Gathering data Questioners and measurement What to measure Observational Studies Design of Experiments Analysis of Data and Graphical Methods Research Skills and Techniques Research Management Describing Bivariate Data Introduction to Bivariate Data Values of the Pearson Correlation Guessing Correlations Simulation Properties of Pearson's r Computing Pearson's r Restriction of Range Demo Variance Sum Law II Exercises Probability Introduction Basic Concepts Conditional Probability Demo Gamblers Fallacy Simulation Birthday Demonstration Binomial Distribution Binomial Demonstration Base Rates Bayes' Theorem Demonstration Monty Hall Problem Demonstration Exercises Normal Distributions Introduction History Areas of Normal Distributions Varieties of Normal Distribution Demo Standard Normal Normal Approximation to the Binomial Normal Approximation Demo Exercises Sampling Distributions Introduction Basic Demo Sample Size Demo Central Limit Theorem Demo Sampling Distribution of the Mean Sampling Distribution of Difference Between Means Sampling Distribution of Pearson's r Sampling Distribution of a Proportion Exercises Estimation Introduction Degrees of Freedom Characteristics of Estimators Bias and Variability Simulation Confidence Intervals Exercises Logic of Hypothesis Testing Introduction Significance Testing Type I and Type II Errors One- and Two-Tailed Tests Interpreting Significant Results Interpreting Non-Significant Results Steps in Hypothesis Testing Significance Testing and Confidence Intervals Misconceptions Exercises Testing Means Single Mean t Distribution Demo Difference between Two Means (Independent Groups) Robustness Simulation All Pairwise Comparisons Among Means Specific Comparisons Difference between Two Means (Correlated Pairs) Correlated t Simulation Specific Comparisons (Correlated Observations) Pairwise Comparisons (Correlated Observations) Exercises Power Introduction Example Calculations Factors Affecting Power Exercises Prediction Introduction to Simple Linear Regression Linear Fit Demo Partitioning Sums of Squares Standard Error of the Estimate Prediction Line Demo Inferential Statistics for b and r Exercises ANOVA Introduction ANOVA Designs One-Factor ANOVA (Between-Subjects) One-Way Demo Multi-Factor ANOVA (Between-Subjects) Unequal Sample Sizes Tests Supplementing ANOVA Within-Subjects ANOVA Power of Within-Subjects Designs Demo Exercises Chi Square Chi Square Distribution One-Way Tables Testing Distributions Demo Contingency Tables 2 x 2 Table Simulation Exercises Case Studies Analysis of selected case studies |
| drugdsg | Drug Design | 21 hours | This course has been created for people who want to understand the basics of structural bioinformatics including protein-ligand docking and virtual screening, i.e.: Researchers whose main area or expertise is not a structural bioinformatics, but they want to broaden their knowledge in this field Computer programmers who are going to deal with structural data (crystal structures, PDB format files, modeling pipelines) or with docking/virtual screening software Biology, biotechnology and bioinformatics students who want to expand their knowledge into structural bioinformatics and molecular interactions This course is more like a workshop. The trainer introduces theoretical bases as well as basic pipeline for structure modeling and molecular interactions. Biological structures Structure and function of DNA, RNA and proteins Information provided by 3D structures General methods of solving protein structures Structural databases and formats Protein Data Bank PubChem and ZINC libraries Structural file formats Protein-ligand docking Representation of macromolecules and ligands Search methods and flexibility of objects Scoring methods Useful data in docking DOCK6 approach of protein-ligand docking Virtual screening Examples |
| mdlbiostruct | Modeling of Biological Structures | 21 hours | This is a practical workshop, which will introduce basics of molecular modelling of proteins, RNA and DNA. It is well suited for people with biological background, with knowledge in molecular biology, who want to learn how to predict 3D structures of proteins, RNA and DNA molecules. It is especially suitable for: Researchers working in experimental laboratories on structure determination or analysis Scientists whose main area of expertise is not biology, but they need to cope with the results of structural biology research Managers introducing new products (e.g. drugs) who want to broaden their knowledge about novel techniques and possibilities in the field of drug design, structural biology and bioinformatics Computer programmers who are going to write programs dealing with biomolecules. The course is divided into theoretical and practical parts. The lecture is interwoven with simple practical exercises, quizzes and tests to make sure attendees understand the concepts. During the training you will be taught how to use software dedicated for building 3D models. Furthermore, we will show how to improve and assess models. During the course we will present various software and different approaches used in novel structure modelling techniques. Our aim is to facilitate your understanding of modelling principles, reasoning and structure-based conclusion making process. By the end of the course you will be able to prepare a 3D structure model of protein, RNA or DNA molecules of reasonable standard. Protein Modelling Two main approaches in 3D structure modelling de novo, homology Homology modelling basics background, techniques, software Choosing a template Alignment preparation Preparing a model Model assessment RNA Modelling Types of modelling de novo, homology Homology modelling basics background, techniques, software Choosing a template Alignment preparation Preparing a model Model assessment |
| progbio | Programming for Biologists | 28 hours | 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 programming. 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. Introduction to the Python programming language Why Python? Using Python to deal with biological data Working with the iPython shell Your first programme Writing Python scripts Importing modules Working with protein and RNA/DNA sequences Finding motives Transcription and translation in silico Handling sequence alignments Parsing data in different biological formats Parsing FASTA Data format conversions Running biological analyses BLAST Accessing biological web services Dealing with biological 3D structures using Python Python facilitates statistical analysis Visualizing data Creating bar and scatter plots Calculating an Area Under Curve (AUC) Working with .xls and .csv files Importing data from and exporting to MS Excel / OpenOffice Calc Writing .xls and .csv files Using Python to create an automated data processing pipeline |
Upcoming Courses
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| Statistics for Researchers - Dubai | Sun, 2017-12-03 09:30 | 6500USD / 10350USD |
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