Syllabus Search Result

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

The aim of this course is to acquire some general experimental procedures and knowledge for biological experiment studies.

The aim of this course is to provide knowledge of experiments for life science. In the class, students study the basic skills for experiments of DNA and protein.

This course aims to acquire basic experimental techniques and knowledge for biological experiment studies. Specifically, this course covers E. coli culturing, DNA extraction, and gel electrophoresis in order to acquire core techniques for genetic engineering and protein science.

The aim of this course is to provide knowledge of experiments for life science. In the class, students study the basic skills for experiments of DNA and protein.

This course aims to acquire basic experimental techniques and knowledge for biological experiment studies. Specifically, this course covers E. coli culturing, DNA extraction, and gel electrophoresis in order to acquire core techniques for genetic engineering and protein science.

This course will examine quantitative research methods and statistical analysis of data with a particular focus on analyzing, understanding, and interpreting statistical results in research. The course will utilize the basic foundations of quantitative methods (e.g., correlation, regression, means comparisons, and factor analysis) and examine how these are used in designing and reporting research.

This course is for people who have some prior experience with statistics, but you do not need a high level of ability (or confidence) in math to succeed. We will look at what is required to analyze a variety of statistical tests, and while this means that students will need to run sample data and report results, the focus will be on what the results mean rather than the specific calculations that lead us to those results. To that end, this course looks at the concepts, interpretations, and applications of statistics rather than the math itself. This course will be discussion-based and NOT lecture based, so students should also come prepared and ready to participate each class.

This course will examine quantitative research methods and statistical analysis of data with a particular focus on analyzing, understanding, and interpreting statistical results in research. The course will utilize the basic foundations of quantitative methods (e.g., correlation, regression, means comparisons, and factor analysis) and examine how these are used in designing and reporting research.

This course is for people who have some prior experience with statistics, but you do not need a high level of ability (or confidence) in math to succeed in this course. We will look at what is required to analyze a variety of statistical tests, and while this means that students will need to run sample data and report results, the focus will be on what the results mean rather than the specific calculations that lead us to those results. To that end, this course looks at the concepts, interpretations, and applications of statistics rather than the math itself. This course will be discussion-based and NOT lecture based, so students should also come prepared and ready to participate each class.

This course will examine quantitative research methods and statistical analysis of data with a particular focus on analyzing, understanding, and interpreting statistical results in research. The course will utilize the basic foundations of quantitative methods (e.g., correlation, regression, means comparisons, and factor analysis) and examine how these are used in designing and reporting research.

This course is for people who have some prior experience with statistics, but you do not need a high level of ability (or confidence) in math to succeed in this course. We will look at what is required to analyze a variety of statistical tests, and while this means that students will need to run sample data and report results, the focus will be on what the results mean rather than the specific calculations that lead us to those results. To that end, this course looks at the concepts, interpretations, and applications of statistics rather than the math itself. This course will be discussion-based and NOT lecture based, so students should also come prepared and ready to participate each class.

Due to the development of advanced information technology, highly accurate spatial information can be utilized. In urban planning, environmental science and area marketing, using these data, modeling of spatial phenomena and elucidation of phenomena is required to plan and implement detailed measures for individual entities. Particularly in recent years, new academic fields called geostatistics and space econometric economics are being formed, and these methodologies have been applied to its application to environmental science, humanities and social sciences. In this course, students are expected to acquire more advanced spatial modeling techniques through lectures and exercises. Students are expected to exercise by selecting socioeconomic data (population, land price etc.) or environment related data (air pollution observation value, etc.) according to their interest.

Due to the development of advanced information technology, highly accurate spatial information can be utilized. In urban planning, environmental science and area marketing, using these data, modeling of spatial phenomena and elucidation of phenomena is required to plan and implement detailed measures for individual entities. Particularly in recent years, new academic fields called geostatistics and space econometric economics are being formed, and these methodologies have been applied to its application to environmental science, humanities and social sciences. In this course, students are expected to acquire more advanced spatial modeling techniques through lectures and exercises. Students are expected to exercise by selecting socioeconomic data (population, land price etc.) or environment related data (air pollution observation value, etc.) according to their interest.

Due to the development of advanced information technology, highly accurate spatial information can be utilized. In urban planning, environmental science and area marketing, using these data, modeling of spatial phenomena and elucidation of phenomena is required to plan and implement detailed measures for individual entities. Particularly in recent years, new academic fields called geostatistics and space econometric economics are being formed, and these methodologies have been applied to its application to environmental science, humanities and social sciences. In this course, students are expected to acquire more advanced spatial modeling techniques through lectures and exercises. Students are expected to exercise by selecting socioeconomic data (population, land price etc.) or environment related data (air pollution observation value, etc.) according to their interest.

In this lecture, the object oriented scripting language AppleScript are mainly studied. Because Python language that has been learned in the previous lecture classes, the main target of this lecture is to follow up developing the ability of basic programming with communicating with applications. Using AppleScript and its standard libraries, students studies the paradigm of inter-application programming, especially using the additional numeric libraries. The style of lecture is to the discipline of the typing keys of keyboard according to program coding, and to the practice of making programs.

Learning programming by the C language that is used for constructing operating systems and other software. This course requires basic knowledge of a programming, but doesn't require any C language knowledge. You are required to install compiling and development environment of C language. For Mac OS, XCODE can be installed as part of OS. For Windows, free software package such as "cygwin" can be installed for this lecture.

The main objective of this course is to exercise development of web applications based on a framework Ruby on Rails where we use a script programming language Ruby which is becoming popular recently. First, we will learn the language specification and programming techniques of Ruby. Next, we will learn how to use Ruby on Rails, and then make our own practical applications.

In this lecture, we study on numerical analysis with python scripting language. We use Numpy, Scipy and Sympy library for numerical analysis. We analysis the real numerical data values including image data and audio data. The lecture introduces the concept and the algorithm for numerical analysis and then students make the program by themselves or with the libraries. The following items will be studied; the equation solving, the operations on matrices and vectors, the solutions of linear equation using Gaussian elimination, Fast Fourier Transform, regression analysis, interpolation, numerical integration, approximate solution of ordinary differential equation. While the level of the mathematical contents of this lecture is based on the mathematics for science course in university, the programming level is easier than the contents. We start this lecture with revising the Python scripting language briefly.

In this lecture, the object oriented scripting language AppleScript are mainly studied. Because Python language that has been learned in the previous lecture classes, the main target of this lecture is to follow up developing the ability of basic programming with communicating with applications. Using AppleScript and its standard libraries, students studies the paradigm of inter-application programming, especially using the additional numeric libraries. The style of lecture is to the discipline of the typing keys of keyboard according to program coding, and to the practice of making programs.

In this lecture, we study on numerical analysis with python scripting language. We use Numpy, Scipy and Sympy library for numerical analysis. We analysis the real numerical data values including image data and audio data. The lecture introduces the concept and the algorithm for numerical analysis and then students make the program by themselves or with the libraries. The following items will be studied; the equation solving, the operations on matrices and vectors, the solutions of linear equation using Gaussian elimination, Fast Fourier Transform, regression analysis, interpolation, numerical integration, approximate solution of ordinary differential equation. While the level of the mathematical contents of this lecture is based on the mathematics for science course in university, the programming level is easier than the contents. We start this lecture with revising the Python scripting language briefly.

The main objective of this course is to exercise development of web applications based on a framework Ruby on Rails where we use a script programming language Ruby which is becoming popular recently. First, we will learn the language specification and programming techniques of Ruby. Next, we will learn how to use Ruby on Rails, and then make our own practical applications.

Learning programming by the C language that is used for constructing operating systems and other software. This course requires basic knowledge of a programming, but doesn't require any C language knowledge. You are required to install compiling and development environment of C language. For Mac OS, XCODE can be installed as part of OS. For Windows, free software package such as "cygwin" can be installed for this lecture.

The main objective of this course is to exercise development of web applications based on a framework Ruby on Rails where we use a script programming language Ruby. First, we will learn the language specification and programming techniques of Ruby. Next, we will learn how to use Ruby on Rails, and then make our own practical applications.

Learning programming by the C language that is used for constructing operating systems and other software. This course requires basic knowledge of a programming, but doesn't require any C language knowledge. You are required to install compiling and development environment of C language. For Mac OS, XCODE can be installed as part of OS. For Windows, free software package such as "cygwin" can be installed for this lecture.