Time Series Analysis Discrete Probalistic Models and their Applications

The joint development of areas such as discrete mathematics, probability, operational research and computer theory is continuous and relevant. The solution of theoretical and applied problems in these areas can be given in terms of a probabilistic modeling that arises from physics as well as the analysis of engineering systems or areas such as economics, biology, engineering, neuroscience. The tools used in such modeling include Markov chains, martingales, stochastic optimization, coupling and combinatorics. Introduce and apply probability topics such as Markov chains, coupling and Poisson approaches through concrete examples such as those from the stochastic models for information theory, engineering, combinatorics, biology, neuroscience and other areas of application of Probability and Stochastic Processes. Institute of Mathematics ans Statistics (IME) São Paulo main campus 1. Discrete Probability Models; 2. Markov chains; 3. Recurrence and ergodicity; 4. Coupling and limiting behavior; 5. Martingales; 6. Renewal processes; 7. Contemporary Topics: Variable Range, Poisson Approximations, Reliability Theory, and Queue Theory Fabio Prates Machado, Luiz Renato Goncalves Fontes, Miguel Natalio Abadi 50 MAE5703 8 The evaluation will consist of the score’s average of tests and lists of exercises. https://www.ime.usp.br/en

Introduction to Scientific Computing for Biological Data Analysis

Many fields of biological research have changed markedly over the past few years with the rise of high-throughput laboratory techniques such as microarrays, massive nucleic acid sequencing and proteomic technologies. These technical developments have brought forth not only a significant, and still ongoing, change in philosophical outlook, but have also transformed how work in certain fields is performed in the lab _ more specifically, the computer lab. Generation of huge data files that are only useful after extensive computational processing became a frequent task in many biological research groups. Proper training in basic computational concepts and tools that can greatly aid in such endeavors have thus become essential in order to extract all the information that many modern large-scale techniques of biological research can provide. This course’s goal is to provide intensive and advanced training in computer usage on the command-line interface (CLI) for large-scale data analysis. At the end of the course, students from biologically-oriented backgrounds should be able to use the CLI to view, edit, manipulate, and summarize large data files, successfully extracting biological information and insight from the high-throughput analyses that generated those files. Biomedical Sciences Institute (ICB) São Paulo main campus • Introduction to computers and the Unix family of operating systems. • Accessing the shell (Bash), locally or remotely, and Bash basics. • Getting help with man, info, apropos, and Internet search engines. • Moving around the directory tree; finding and executing programs; navigating/understanding the system (memory, disk space etc.). • System structure; file types; user and group permission model; Changing file access (owner, group, permissions). • Standard streams and redirection; piping. • Finding and manipulating files and directories (create, delete, move, copy, rename, append, concatenate etc.). • Describing and summarizing file content (wc, file); getting data into the system (wget, scp, ftp). • Creating, exploring, and sub-setting files. • Comparing, sorting, and editing files. • Compressing and decompressing data (tar, gz, zip etc.). • Basics of regular expressions. • Compiling third-party programs. • Automating the CLI with basic Bash scripting. Jo_o Marcelo Pereira Alves 10 ICB5765 4 The course is structured in short lectures intermingled with class activity sessions, in order to make the course as practice-oriented as possible. In order to better reflect everyday research practice, the most widespread file formats used in the field will also be introduced and used in as many practical examples as possible. The whole course, including exams, takes place in a computer lab. The use of the command-line environment of Unix-like operating systems (such as Mac OS X and Linux-based systems) will be intensively explored, in order to give students all the working knowledge necessary to run most bioinformatics tools and efficiently analyze their output. Avaliation Form: Final grade will be calculated as the weighted average of midterm exam (weight 2), final exam (weight 2), in-class quizzes (weight 1), and practical exercises (weight 3). A passing grade consists of 5.0 or higher final average and at least 75% attendance. Students with regular semester final grades between 3.0 and 4.9 and attendance above 75% can take a supplementary exam, in which case the second final grade will be the average of the final grade above and the supplementary exam. https://ww2.icb.usp.br/ing/

Experimental Statistics II and Mixed Models

The proposed program includes experimental designs used in the agricultural experimentation whose structure requires special attention due to the methodology applied for the analysis of the obtianed data. Aims to provide a solid methodological basis for the use of models in the analysis of continuous data and in research, involving concepts of matrix algebra and statistical inference, teaching Hasse diagrams, linear models for incomplete data using the theory of mixed models, including the estimation techniques, checking of model fitting, diagnostics, inference and confidence intervals. Luiz de Queiroz College of Agriculture (ESALQ) Piracicaba campus Hasse Diagrams. Unbalanced cross-classifications. Split-plot experiments. Split-Block experiments. Incomplete block designs. Lattice squares. Groups of experiments. Groups of experiments with common treatments (augmented block). Introduction to mixed models. Sonia Maria de Stefano Piedade, Clarice Garcia Borges Demetrio, Taciana Villela Savian 25 LCE5872 8 Evaluation tests and Seminars http://pt.esalq.usp.br/

Microbiome Data Analysis

A Microbiome is a complex microbial community, which can inhabit a wide variety of environments, from the human gut, to soil, to food products. The study of these communities is done through Next Generation Sequencing, and its associated metadata. There is a large demand to have people correctly trained that can perform this analysis. This class will supply this demand by introducing the students to current analytical tools used for microbiome data analysis, through theoretical and practical classes, which will enable the student to conduct a final data analysis project (primary data or database available data). To provide theory and practical background on conducting microbiome data analysis. School of Pharmaceutical Sciences (FCF) São Paulo main campus Fundamentals concepts in microbiome related experimental design, fundamentals of sequencing library preparation, introduction to data analysis using Qiime, Picrust, Permanova, R, A’nvio etc. Christian Hoffmann 20 FBA5906 6 The class will be thought in English and may have the participation of invited international data analysts, to be announced opportunely. Students will be required to hand in a final paper based on the analysis they conduct throughout the semester. http://www.fcf.usp.br/english.php

Statistical Mechanics

This course is essential for those who intend to work in many body systems, in order to describe observable macroscopic quantities from the microscopic description of the system. To introduce the basis of statistical mechanics, with a view to their application in different areas, such as magnetism, biology, nuclear physics, etc. Faculty of Philosophy, Sciences and Letters at Ribeirão Preto (FFCLRP) Ribeirão Preto campus 1. Review of ensemble theory (a) microcanonical ensemble (b) canonical ensemble (c) gran canonical ensemble and (d) pressure ensemble 2. Ideal Gas (a) classica gas: Maxwell-Boltzmann statistics (b) quantum gases: quantum statistics: Base-Einstein and Fermi-Dirac 3. Phase transitions and critical phenomena (a) simple fluids: van der Waals equation (b) simple ferromagnet: Curie-Weiss equation (c) Landau theory 4. Ising Model (a) exact solution in one dimension (b) mean-field approach 5. theory of scale and group renormalization (a) scale theory of thermodynamic potentials (b) scale of critical correlations (c) Kadanoff construction (d) Renormalization of the Ising model (e) general scheme of renormalization group Alexandre Souto Martinez 25 5915736 6 Arithmetical mean of two tests. https://www.google.com.br/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiBp_-p9NzYAhWHkZAKHY_oACkQFggnMAA&url=http%3A%2F%2Fwww.ffclrp.usp.br%2Fdown.php%3Fid%3D1430%26d&usg=AOvVaw3-C7BSHGAhorxoB-Rfx8dD