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  • STAN 3200: Statistical Programming and Data Science with R

    4.00 Credits
    New College of Florida

    Real data is messy and complicated. Students often begin data analysis projects focusing on statistical models or inference, only to discover that the bulk of their time is spent tidying and organizing their data. What if our data is coded in a way that is hard to understand? What if the variables we want to study aren't directly recorded in the data but have to be calculated for each case? What if we need to combine data from multiple data sets? How can we organize larger data science projects to keep track of our data, graphs, reports, etc.? This course teaches practical skills in data science using the statistical software R. The first part of the course covers how to set up, visualize, wrangle, and transform messy data in order to answer interesting questions. We will consider the special issues involved in working with categorical data, text data, and dates/times. In the second half of the course, we will expand our applications of these skills and also use R to create simulations exploring statistical concepts and theories from a computational perspective. This course is ideal for anyone who wants to learn R or improve their practical data handling skills. Students should either have completed or currently be enrolled in Dealing with Data 1 (or an equivalent course).
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  • STAN 3230: Data Visualization and Communication

    4.00 Credits
    New College of Florida

    Finding meaning in complex data sets often requires identifying patterns and relationships that are not immediately evident when staring at spreadsheets of numbers. Transforming the data into a graphic form can overcome this problem - when it is done right. This class covers the principles and practice of data visualization and communication. We will look at guidelines and tools for data reporting and reproducible research. We will explore how to design data visualizations based on best practices and principles, taking into account the purpose of the viz; we will also learn how to implement visualizations for a variety of types of data. Along the way, we will gain experience critically responding to and evaluating the effectiveness of various designs. This class is aimed at undergraduate students who have some knowledge of programming (preferably with R/RStudio) and have completed at least one semester of statistics.
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  • STAN 3275: Linear Models

    4.00 Credits
    New College of Florida

    This course will predominantly focus on numerous practical and theoretical aspects of linear models, their extensions (e.g. polynomial regression, interactions) and generalized linear models (e.g. logistic and Poisson regression). Linear models constitute one of the most ubiquitous tools in dealing with data, presenting a crucial component for a statistician's skill set. On top of covering all the main practical underpinnings of linear models, this course will emphasize developing students' quantitative maturity via deriving mathematical properties and being able to formulate modeling equations for various statistical models. Finally, the course will be consistently accompanied by coding practices in R statistical software, including some in-class coding labs. Enrollment Cap: 15.
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  • STAN 3350: Introduction to Categorical Data Analysis

    4.00 Credits
    New College of Florida

    This course focuses on methods for analyzing categorical response data. In contrast to continuous data, categorical data consist of observations classified into two or more categories. Traditional tools of statistical data analysis (such as linear regression) are not designed to handle such data and pose inappropriate assumptions. We will develop methods specifically designed for modeling categorical data and consider many applications in the social and biological sciences as well as in medical research, engineering and economics. The course will use the free statistical software R to carry out all statistical analysis and has two main parts. The first part will discuss statistical inference for parameters of categorical distributions and for measures of association arising in contingency tables. The longer second part will focus on statistical modeling of categorical response data via generalized linear models, with a heavy focus on logistic regression models.
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  • STAN 3700: R for Data Science

    4.00 Credits
    New College of Florida

    Real data is messy and complicated. Students often begin data analysis projects focusing on statistical models or inference, only to discover that the bulk of their time is spent tidying and organizing their data. What if our data is coded in a way that is hard to understand? What if we need to combine data from multiple data sets? How can we organize larger data science projects to keep track of our data, graphs, reports, etc. in a reproducible way? This course explores how to use the statistical software R in order to import, visualize, wrangle, transform, and analyze data. We will consider the special issues involved in working with categorical data, text data, and dates/times. This course is ideal for anyone who wants to learn R or improve their practical data handling skills. It is recommended that students have taken Dealing with Data 1 (or an equivalent class).Enrollment Cap: 20. Prerequisite: Dealing with Data I or an equivalent class.
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  • STAN 3780: Applied Time Series Analysis

    4.00 Credits
    New College of Florida

    Time series refer to any data measured at different points in time. While many common statistical techniques and tools are also useful for exploring time series data, some unique properties of time series data require new approaches. This course provides an introduction to methods of time series analysis, with a focus on practical skills. We will begin with understanding the structure of time-dependent data, along with common techniques for visualizing and exploring such data. We will then review common statistical methods and models for non-time series data before moving on to developing new models. Throughout the class, we will incorporate a variety of rich data sets from both natural and social sciences. Familiarity with linear regression and some basic probability are recommended. Pre-requisite: Dealing with Data II (or permission from the instructor).
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  • STAN 4200: Introduction to Time Series Analysis

    4.00 Credits
    New College of Florida

    Time series refer to any data measured at different points in time. While many common statistical techniques and tools are also useful for exploring time series data, some unique properties of time series data require new approaches. This course provides an introduction to methods of time series analysis, building upon students' background knowledge in statistical inference and regression analysis. We will begin with understanding the structure of time-dependent data and common techniques for exploring such data. We will then review necessary skills and topics from probability and math before moving on to techniques for modeling and analyzing both time and frequency domain data. Throughout the class, we will incorporate a variety of rich data sets from both natural and social sciences. Familiarity with linear regression and calculus-based probability are recommended. Pre-requisite: Dealing with Data II (or permission from the instructor).
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  • STAN 4300: Statistical Estimation and Inference

    4.00 Credits
    New College of Florida

    This course will introduce students to advanced mathematical and inferential procedures for a deeper understanding of statistical estimation and inference. Many topics from introductory statistics classes, such as random variables, the central limit theorem, statistical estimation and hypotheses testing will be revisited and put on a more rigorous mathematical footing. The course starts with a review of random variables (both discrete and continuous) and an extension to random vectors. Basic properties of random samples and statistics such as the sample mean and sample standard deviation are discussed, followed by a discussion of sampling distributions, including bootstrap and permutation approaches. Deriving and evaluating point estimators, with a focus on maximum likelihood and Bayes estimation is thoroughly covered, followed by principles of interval estimation and some discussion of hypothesis testing, including likelihood ratio tests. Although the course will use the R programming language, it will feel more theoretical because it explains the concepts and ideas behind the procedures underlying applied data analysis. Enrollment Cap: 20. Course Attributes: Environmental Studies.
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  • STAT 2100: Introduction to Statistics

    4.00 Credits
    New College of Florida

    This course will introduce students to applied statistics in the social and behavioral sciences. The course will employ a conceptual approach to using descriptive and inferential statistics. Topics will include frequency distributions, central tendency and variability, probability, confidence intervals, hypothesis testing, inferences about means, analysis of variance, correlation, regression, power, and non-parametric analysis. Students will be introduced to computer programs, Excel and SAS, for doing statistical analysis.
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  • STAT 2125: Statistics for Economics and the Social Sciences

    4.00 Credits
    New College of Florida

    This course covers introductory statistics for economics and the social sciences. Topics include descriptive statistics, tabular and graphical displays and numerical measures of location and variability; five number summaries and box plots; measures of association between two variables; random experiments, counting rules, and assigning probabilities; basic relationships of probability, conditional probability, and Bayes' Theorem; discrete probability distributions, expected value and variance, and bivariate and binomial distributions; continuous probability distributions, uniform, normal, and exponential distributions; sampling, point estimation, and sampling distribution of x-bar and p-bar; interval estimation, population means (sigma known and unknown), population proportion, and determining sampling size; null and alternative hypotheses and type I and II errors; hypothesis testing of population mean (sigma known and unknown) and population proportion; inferences about the difference between two population means (sigma known and unknown), matched samples, and inferences about the difference between two population proportions; comparing multiple proportions, tests of independence, and goodness of fit; and simple linear regression, least squares method, coefficient of determination, testing for significance, estimation and predictions, and residual analysis. The course prepares students for further studies in statistics, including econometrics. The workshop will feature an inverted classroom model in which students participate in problem solving exercises. Attendance at the workshop is highly recommended as it has proved very useful to students in the past. The course is at the introductory level and has no prerequisites. However it requires making a serious commitment to spending a lot of time working out and solving problems.
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