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In this class, students are expected to study about basic statistics by analyzing data empirically. Basic techniques such as data collection, statistical analysis and presentation are introduced.
Lectures include (1) description of data such as average, variance and correlation, (2) basics of probability theories such as population and samples, stochastic distributions and sample distributions, and (3) statistical models such as regression analysis and analysis of variance.
Lecturers might change contents of syllabus.
By the end of the course, students will gain basic understanding of statistics as well as methods to analyze data using statistical software.
In this class, students are expected to study about basic statistics by analyzing data empirically. Basic techniques such as data collection, statistical analysis and presentation are introduced.
Lectures include (1) description of data such as average, variance and correlation, (2) basics of probability theories such as population and samples, stochastic distributions and sample distributions, and (3) statistical models such as regression analysis and analysis of variance.
Lecturers might change contents of syllabus.
This class is an introduction to probability. Probability is a theory that quantifies uncertain phenomena and is the fundamental mathematics in a wide range of fields such as data science, economics, and engineering. After covering some basics of probability (joint probability, conditional probability, Baye's theorem etc), we will learn quantitate aspects of probability distributions (random variable, expectation values, variance etc).
Probability and statics are well established branches of mathematics that has applications in all areas of technology today. This course mainly presents a solid foundation for probability and the introduction of statics, explaining its ideas and techniques necessary for a firm understanding of the topic.
This class is an introduction to probability. Probability is a theory that quantifies uncertain phenomena and is the fundamental mathematics in a wide range of fields such as data science, economics, and engineering. After covering some basics of probability (joint probability, conditional probability, Baye's theorem etc), we will learn quantitate aspects of probability distributions (random variable, expectation values, variance etc).
In the first half we study set theory and mathematical logic. These are useful of logical thinking. In the latter half we study probability. We overview permutation and combination, which you have learned at high school, and then, we study probability. Our goal is Bayesian Theory. This is new for all. Mathematics in university is different from one in high school. Even if you are no good at calculation and memory, you have a chance to enjoy mathematics in university.
This class is an introduction to calculus. Differential and integral calculus is a theory for analyzing changes and accumulations of targets, respectively, and has many applications in data science, economics, science and engineering, etc. In fact, calculus and linear algebra are considered as the most important mathematics at universities. In this class, we will learn not only calculus of one variable functions but also polynomial approximation of one variable functions and calculus of multivariate functions.
This class is an introduction to calculus. Differential and integral calculus is a theory for analyzing changes and accumulations of targets, respectively, and has many applications in data science, economics, science and engineering, etc. In fact, calculus and linear algebra are considered as the most important mathematics at universities. In this class, we will learn not only calculus of one variable functions but also polynomial approximation of one variable functions and calculus of multivariate functions.
This class is an introduction to calculus. Differential and integral calculus is a theory for analyzing changes and accumulations of targets, respectively, and has many applications in data science, economics, science and engineering, etc. In fact, calculus and linear algebra are considered as the most important mathematics at universities. In this class, we will learn not only calculus of one variable functions but also polynomial approximation of one variable functions and calculus of multivariate functions.
This course will cover fundamentals of calculus, which is essentially important for various research fields. Beginning with some preliminaries, we will study derivatives and integrals. For either topic, we will start from single function, and then it will be extended to multiple functions. A number of practices are prepared for deeper understanding and practical usage of derivatives and integrals.
This class is an introduction to linear algebra. Linear algebra is a theory about vectors and matrices, and has many applications in data science, economics, engineering etc. In fact, linear algebra and calculus are considered as the most important mathematics at universities. In this class, we will learn the basic ideas of linear algebra from both algebraic and geometric point of views.
This class is an introduction to linear algebra. Linear algebra is a theory about vectors and matrices, and has many applications in data science, economics, engineering etc. In fact, linear algebra and calculus are considered as the most important mathematics at universities. In this class, we will learn the basic ideas of linear algebra from both algebraic and geometric point of views.
We will learn about the properties of vectors and matrices as these are basic concepts. We will also learn how to solve simultaneous equations using matrices. After that, we will learn about the uses of linear algebra used in our lives, including applications to technology such as computer search, computer graphics, error correction and quantum computing. Linear algebra is among the most fundamental and useful fields of mathematics, and the material here will benefit learners in many other classes at SFC.
This class is an introduction to linear algebra. Linear algebra is a theory about vectors and matrices, and has many applications in data science, economics, engineering etc. In fact, linear algebra and calculus are considered as the most important mathematics at universities. In this class, we will learn the basic ideas of linear algebra from both algebraic and geometric point of views.
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 modern times, many problems around us are mathematically abstracted and solved by using computers to perform calculations based on mathematical theory. Gain a better understanding of high school mathematics, linear algebra, and calculus by knowing how and how math was used to solve real problems.
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.
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.
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.
In this lecture, we will learn about optimization problems. The optimization problem is to find a solution that minimizes (or maximizes) the objective function under certain constraints. This can appear in a wide range of situations, from assigning part-time shifts to matching residents and hospitals. In this lecture, we will cover linear programming problems, nonlinear programming problems, and integer programming problems.