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  • MOOC 
    Machine Learning and Computational Statistics

    Machine Learning and Computational Statistics

    This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build. This course was designed as part of the core curriculum for the Center for Data Science's Masters degree in Data Science. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that class is intended as a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this DS-GA class (e.g. data cleaning, cross-validation, and sampling bias).

    Course related:
    LGT6801 Guided Study in Logistics I
    Subjects:
    Computing and Mathematics and Statistics
    Keywords:
    Big data Data mining Machine learning Mathematical statistics -- Data processing
    Resource Type:
    MOOC
     
  • MOOC 
    Statistics and R

    Statistics and R

    This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research. Given the diversity in educational background of our students we have divided the course materials into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. We start with simple calculations and descriptive statistics. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

    Subjects:
    Statistics and Research Methods and Mathematics and Statistics
    Keywords:
    Life sciences -- Statistical methods Mathematical statistics -- Data processing R (Computer program language)
    Resource Type:
    MOOC