25 items found.
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
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.
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 objective of this course is to learn the advanced micro econometrics and pursue your own research topic by using the knowledge and skills that you acquired.
In this lecture we overview how mathematical science is useful to analyze real problems.The point is, not to memorize formulas and results, to understand the process how to formulate real problem to mathematical problem.
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 chi-square tests) 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.
NOTE: This class will be held both online and via weekly Zoom discussions. Lectures, readings, & assignments will be delivered in an on-demand format (due every Tuesday, 9:00 A.M.), and will be supplemented with regular Q&A/Discussion sessions over Zoom each week at 14:45.
This course is designed to be an introduction to understanding and evaluating data and making rational decisions based on that data. This year, the focus is on multivariate analysis techniques. What you will learn is the representation and summary statistics of quantitative and qualitative data, correlations and principal components, factor analysis, and analysis of covariance structures.
The focus is on mastering concepts and interpreting the results of data and statistical analysis, rather than detailed computational techniques.
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 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 modelings with differential equations, and in the later part, mathematical analysis of perceptual phenomena in human psychology are also discussed.
Computers are used everywhere in modern information society. Whenever a computer is used, some algorithm is used. The purpose of this lecture is to introduce algorithms used in various situations and to make them more interesting.
The aim of this course is to acquire some general experimental procedures and knowledge for biological experiment studies.
Data mining has gained interest among business practitioners in a variety of fields. Almost every organization collects data, which can be analyzed to support making better decisions and improving policies.
Electronic data capture has become inexpensive and ubiquitous due to innovations such as the internet, e-commerce, point-of-sale devices. As a result, data mining is a rapidly growing field concerned with developing techniques to assist managers in making intelligent use of these repositories. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.
This course will examine data mining methods and provide an opportunity for hands-on exercises with algorithms for data mining using R-language software and cases.
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.
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.).
In this lesson, we learn "sports analytics" from a multifaceted perspective.
It is predicted that cutting-edge technological innovation will bring about social innovation due to the Fourth Industrial Revolution and Society 5.0. Needless to say, there are various developments and applications of mathematical science and statistical science in the background.
This class aims to deepen our understanding of how mathematics and statistics have contributed to the development of society and science and technology, and how they can bring about social change in the future.
Based on “Introduction of Statistics,” this course will enhance student’s understanding of the theories and practices of data science and develop the following statistical abilities: discovering the problems of the current status, hypothesizing and building the models based on data, and verifying them. It will focus on applicative topics of linear models (model selection, logistic regression, and generalized linear model etc.) and the various methods of multivariate analyses such as principal component analysis, discriminant analysis, variance analysis, factor analysis, cluster analysis, and tree-model.
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 not only in economics and finance, but also in various fields such as business administration, medicine, psychology, and marketing.
In this lecture, we will explain the differences between Bayesian statistics and non-Bayesian statistics, Bayes' theorem which is the basis of Bayesian statistics, Bayesian updating from prior to posterior probability, Markov chain Monte Carlo method which is a numerical analysis method, model selection in Bayesian statistics, application of Bayesian estimation to normal distribution models and regression analysis models, and hierarchical Bayesian models which handle individual differences.
In the lecture, we will include many exercises using Python.
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.
Computers are used everywhere in modern information society. Whenever a computer is used, some algorithm is used. The purpose of this lecture is to introduce algorithms used in various situations and to make them more interesting.
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 (for example, cross-entropy can be adopted as an objective function in neural networks).
The goal is to learn the quantitative treatment of "information" that is used in our daily life.
We study Complex Analysis. We understand some properties of complex functions
such as Cauchy's theorem, Cauchy's formula, Residues theorem, etc, which are
completely different form real functions. We don't touch the proofs, but we
understand what theorems imply, and master some calculations.
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 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.).