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  • MOOC 
    Data Science Methodology

    Data Science Methodology

    Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.
    This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.
    Accordingly, in this course, you will learn:
    - The major steps involved in tackling a data science problem.
    - The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
    - How data scientists think!

    Course related:
    LGT6801 Guided Study in Logistics I, LGT6202: Stochastic Models and Decision under Uncertainty, LGT6802 Guided Study in Logistics II, and LGT6803: Guided Study in Logistics III
    Subjects:
    Business Information Technology and Computing
    Keywords:
    Electronic data processing Data mining Problem solving
    Resource Type:
    MOOC
     
  • 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