14717 items found.
In this lecture, we will introduce various aspects of algorithms that form the basis of computer science and data science. No programming will be done.
In this lecture, we will introduce various aspects of algorithms that form the basis of computer science and data science. No programming will be done.
In this lecture, we will introduce various aspects of algorithms that form the basis of computer science and data science. No programming will be done.
This class discusses how several phenomena could be formulated in mathematical modeling. Each lecture introduces one phenomenon and a mathematical model that describes the phenomenon. This series of lectures firstly addresses modeling with differential equations, and in the later part, mathematical analysis of perceptual phenomena in human psychology are also discussed.
This class discusses how several phenomena could be formulated in mathematical modeling. Each lecture introduces one phenomenon and a mathematical model that describes the phenomenon. This series of lectures firstly addresses modeling with differential equations, and in the later part, mathematical analysis of perceptual phenomena in human psychology are also discussed.
Researchers from all fields, including Policy Management and Environment and Information Studies, will apply Bioinformatics Data Skills (V Buffalo, 2015) to tackle problems in diverse areas (e.g., life science, language, music, design, etc.).
This class focuses on the skills bioinformaticians use to explore and extract information from complex, large datasets. These data skills give you freedom; you’ll be able to look at any data (in any format, and files of any size) and begin exploring data to extract meaning.
Throughout the class, I will emphasize working in a robust and reproducible manner. Reproducibility means that your work can be repeated by other researchers and they can arrive at the same results. For this to be the case, your work must be well documented, and your methods, code, and data all need to be available so that other researchers have the materials to reproduce everything. If a workflow run on a different machine yields a different outcome, it is neither robust nor fully reproducible. These are themes that reappear throughout the class.
This class focuses primarily on handling tabular plain-text data formats. Tabular data is terrific for honing your data skills. Even if your goal is to analyze other types of data in the future, tabular data serves as great example data to learn with. Developing the text-processing skills necessary to work with tabular data will be applicable to working with many other data types. Thus, this class will teach you useful computational tools and data skills that will be helpful in your research.
The aim of this course is to introduce the basics of medical and health data science, data collection, data management, data analysis and biostatistics to better comprehend medical literature and publications. By the end of the course, skills and methodology for basic statistical analysis needed for medical publication will be acquired.
The aim of this course is to introduce the basics of medical and health data science, data collection, data management, data analysis and biostatistics to better comprehend medical literature and publications. By the end of the course, skills and methodology for basic statistical analysis needed for medical publication will be acquired.
Researchers from all disciplines, including Policy Management and Environment and Information Studies, will apply sequence analysis methods to tackle problems in their fields (biology, language, manuscript, music, etc.).
Sequence analysis is a broad field, covering any kinds of analyses of textual sequences; e.g. those representing genomes (DNA) and proteins (amino acids). The biological sequence analyses include predicting gene function, inferring phylogenetic relationships, and ancestral reconstruction (Coghlan, 2017; Hall, 2017). For instance, phylogenetic trees inferred from viral sequence data can be used to estimate viral emergence, characterize the geographic spread of the virus, and identify instances of adaptive mutations (Martin et al., 2021). The sequence analysis methods have been used not only in the field of biology, but also in genealogy of manuscripts (Barbrook et al., 1998) and quantitative evaluation of melodic similarity (Savage et al., 2018). Thus, text-processing skills necessary to analyze sequence data can be applied to the analysis of data in other fields.
This course will provide the introduction to the main tools and databases used in the analysis of sequence data and explains how these can be used together to answer biological questions.
Sequence analysis is a broad field, covering any kinds of analyses of textual sequences; e.g. those representing genomes (DNA) and proteins (amino acids). The biological sequence analyses include determining genome structures, identifying protein-coding regions (genes), predicting gene function, inferring phylogenetic relationships, and ancestral reconstruction (Coghlan, 2011; Hall, 2017). Recent studies showed that genomics and phylogenetics can track spread and evolution of novel coronavirus ([https://nextstrain.org/]). The sequence analysis methods have been used not only in the field of biology, but also in genealogy of manuscripts (Barbrook et al., 1998) and quantitative evaluation of melodic similarity (Savage et al., 2018). Thus, text-processing skills necessary to analyze sequence data can be applied to the analysis of data in other fields.
This course will provide the introduction to the main tools and databases used in the analysis of sequence data and explains how these can be used together to answer biological questions. Examples of analysis include retrieving DNA and protein sequences from public databases, DNA sequence statistics (length, GC content, DNA words, and local variation in base composition), pairwise sequence alignment (dotplot, global sequence alignment, and local sequence alignment), multiple sequence alignment, and phylogenetic inference, etc.
Students from all disciplines will use the sequence analysis methods to tackle problems in their fields (biology, language, manuscript, music, etc.).
The aim of this course is to introduce the basics of medical and health data science, data collection, data management, data analysis and biostatistics to better comprehend medical literature and publications. By the end of the course, skills and methodology for basic statistical analysis needed for medical publication will be acquired.
In recent years, the Bayesian approach has been attracting attention not only in the natural sciences, such as biostatistics and spatial statistics, but also in the social sciences, such as marketing, policy analysis, and econometrics. In this class, we will cover the basics and applications of Bayesian statistics, assuming a basic knowledge of classical statistics, and will include exercises in R and other languages. Markov chain Monte Carlo, empirical Bayes and hierarchical Bayes, Bayesian inference on regression and correlation, Bayesian econometrics, etc.
This class aims to study about statistical modeling such as linear regression model, general linear regression model and general linear mixture model.
In recent years, Bayesian statistics has been attracting attention in various fields such as economics, finance, medicine, psychology, and marketing. In this lecture, starting from the basics of classical statistics, I will explain Bayes' theorem, Bayesian inference, Markov chain Monte Carlo methods, model selection in Bayesian statistics, and so on. Exercises using the free statistical software Python will be included in this lecture.
This class aims to study about statistical modeling such as linear regression model, general linear regression model and general linear mixture model.
In recent years, the Bayesian approach has been attracting attention not only in the natural sciences, such as biostatistics and spatial statistics, but also in the social sciences, such as marketing, policy analysis, and econometrics. In this class, we will cover the basics and applications of Bayesian statistics, assuming a basic knowledge of classical statistics, and will include exercises in R and other languages. Markov chain Monte Carlo, empirical Bayes and hierarchical Bayes, Bayesian inference on regression and correlation, Bayesian econometrics, etc.
Provide lectures and exercises on various phenomenon in Earth and Planetary Science, which consists of space/planet, atmosphere/ocean, earthquake/volcano, rock/mineral, and geological earth history.
Since many phenomenon in Earth and Planetary Science are governed by equations, deep understanding can be obtained if one knows how to solve the equations. In classes, we focus on a particular phenomena and students will have a set of lecture and exercise on the phenomena.
This lecture is an introduction to Shannon's information theory. The essence of information theory is "what is information mathematically?". We will consider a mathematical model of "amount of information", "coding of information" etc and study their basic theory. In transmission and recording, we want to reduce the amount of data. On the other hand, in communication, there is a possibility of transmission error, and coding is required to minimize the transmission error. We will consider the basic idea and method for these problems.
Information theory is a basic theory of expression and transmission of "information". Typical applications include data compression, bit error detection / correction, and encryption. Information theory also plays an important role in machine learning algorithms.
The goal is to learn the quantitative treatment of "information" that is used in our daily life.
Sequence analysis is a broad field, covering any kinds of analyses of textual sequences; e.g. those representing genomes (DNA) and proteins (amino acids). The biological sequence analyses include determining genome structures, identifying protein-coding regions (genes), predicting gene function, inferring phylogenetic relationships, and ancestral reconstruction (Coghlan, 2011; Hall, 2017). Recent studies showed that genomics and phylogenetics can track spread and evolution of novel coronavirus ([https://nextstrain.org/]). The sequence analysis methods have been used not only in the field of biology, but also in genealogy of manuscripts (Barbrook et al., 1998) and quantitative evaluation of melodic similarity (Savage et al., 2018). Thus, text-processing skills necessary to analyze sequence data can be applied to the analysis of data in other fields.
This course will provide the introduction to the main tools and databases used in the analysis of sequence data and explains how these can be used together to answer biological questions. Examples of analysis include retrieving DNA and protein sequences from public databases, DNA sequence statistics (length, GC content, DNA words, and local variation in base composition), pairwise sequence alignment (dotplot, global sequence alignment, and local sequence alignment), multiple sequence alignment, and phylogenetic inference, etc.
Students from all disciplines will use the sequence analysis methods to tackle problems in their fields (biology, language, manuscript, music, etc.).
This class focuses on the skills bioinformaticians use to explore and extract information from complex, large datasets. These data skills give you freedom; you’ll be able to look at any bioinformatics data (in any format, and files of any size) and begin exploring data to extract biological meaning.
Throughout the class, I will emphasize working in a robust and reproducible manner. Reproducibility means that your work can be repeated by other researchers and they can arrive at the same results. For this to be the case, your work must be well documented, and your methods, code, and data all need to be available so that other researchers have the materials to reproduce everything. If a workflow run on a different machine yields a different outcome, it is neither robust nor fully reproducible. These are themes that reappear throughout the class.
This class focuses primarily on handling tabular plain-text data formats. Tabular data is terrific for honing your data skills. Even if your goal is to analyze other types of data in the future, tabular data serves as great example data to learn with. Developing the text-processing skills necessary to work with tabular data will be applicable to working with many other data types. Thus, this class will teach you useful computational tools and data skills that will be helpful in your research.
Researchers from all disciplines will use Bioinformatics Data Skills to tackle problems in their fields (e.g., biology, language, music, socio-economic factors contributing to the COVID-19 pandemic, etc.).
Researchers from all fields, including Policy Management and Environment and Information Studies, will apply Bioinformatics Data Skills (V Buffalo, 2015) to tackle problems in diverse areas (e.g., life science, language, music, design, etc.).
This class focuses on the skills bioinformaticians use to explore and extract information from complex, large datasets. These data skills give you freedom; you’ll be able to look at any data (in any format, and files of any size) and begin exploring data to extract meaning.
Throughout the class, I will emphasize working in a robust and reproducible manner. Reproducibility means that your work can be repeated by other researchers and they can arrive at the same results. For this to be the case, your work must be well documented, and your methods, code, and data all need to be available so that other researchers have the materials to reproduce everything. If a workflow run on a different machine yields a different outcome, it is neither robust nor fully reproducible. These are themes that reappear throughout the class.
This class focuses primarily on handling tabular plain-text data formats. Tabular data is terrific for honing your data skills. Even if your goal is to analyze other types of data in the future, tabular data serves as great example data to learn with. Developing the text-processing skills necessary to work with tabular data will be applicable to working with many other data types. Thus, this class will teach you useful computational tools and data skills that will be helpful in your research.
Sequence analysis is a broad field, covering any kinds of analyses of textual sequences; e.g. those representing genomes (DNA) and proteins (amino acids). The biological sequence analyses include determining genome structures, identifying protein-coding regions (genes), predicting gene function, inferring phylogenetic relationships, and ancestral reconstruction (Coghlan, 2011; Hall, 2017). Recent studies showed that genomics and phylogenetics can track spread and evolution of novel coronavirus ([https://nextstrain.org/]). The sequence analysis methods have been used not only in the field of biology, but also in genealogy of manuscripts (Barbrook et al., 1998) and quantitative evaluation of melodic similarity (Savage et al., 2018). Thus, text-processing skills necessary to analyze sequence data can be applied to the analysis of data in other fields.
This course will provide the introduction to the main tools and databases used in the analysis of sequence data and explains how these can be used together to answer biological questions. Examples of analysis include retrieving DNA and protein sequences from public databases, DNA sequence statistics (length, GC content, DNA words, and local variation in base composition), pairwise sequence alignment (dotplot, global sequence alignment, and local sequence alignment), multiple sequence alignment, and phylogenetic inference, etc.
Students from all disciplines will use the sequence analysis methods to tackle problems in their fields (biology, language, manuscript, music, etc.).
This class focuses on the skills bioinformaticians use to explore and extract information from complex, large datasets. These data skills give you freedom; you’ll be able to look at any bioinformatics data (in any format, and files of any size) and begin exploring data to extract biological meaning.
Throughout the class, I will emphasize working in a robust and reproducible manner. Reproducibility means that your work can be repeated by other researchers and they can arrive at the same results. For this to be the case, your work must be well documented, and your methods, code, and data all need to be available so that other researchers have the materials to reproduce everything. If a workflow run on a different machine yields a different outcome, it is neither robust nor fully reproducible. These are themes that reappear throughout the class.
This class focuses primarily on handling tabular plain-text data formats. Tabular data is terrific for honing your data skills. Even if your goal is to analyze other types of data in the future, tabular data serves as great example data to learn with. Developing the text-processing skills necessary to work with tabular data will be applicable to working with many other data types. Thus, this class will teach you useful computational tools and data skills that will be helpful in your research.
Researchers from all disciplines will use Bioinformatics Data Skills to tackle problems in their fields (e.g., biology, language, music, socio-economic factors contributing to the COVID-19 pandemic, etc.).
It analyzes the data in the classes. And ultimately make up material creation to seek a settlement to the approval person.Not only the technical skills of data analysis , learn the importance of objective setting and explanatory variables