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Courses

  • Statistical Inference (40005)
    • תקציר הקורס:

    Abstract:

    Descriptive statistics, estimation, properties of estimators and estimation methods, Confidence intervals, testing of statistical hypothesis, tests about the expectation, proportion and the variance of a population, goodness of fit and independence tests,

    simple linear regression, non-parametric statistics. Using the R software.
  • Data Mining (40225)
    • תקציר הקורס:

    Abstract:

    Data mining is a term from the field of computer science that describes automatic processes of information discovery. In this course, we will review the supervised versus unsupervised learning methods and get to know the EDA (Exploratory Data Analysis) process in depth. In addition, we will review advanced learning methods (such as association rules, anomaly detection). The course contains both theoretical lectures on how the aforementioned methods work and practical exercises of implementation and different uses of the Python language.
  • Data Analysis In R Language (40235)
    • תקציר הקורס:

    Abstract:

    R language has gained popularity in the past decade

    and is gradually becoming the world standard in statistical analysis.

    Its advantages: free, usable for large database analysis and multiple cores at the same time, it is flexible and has broad support of the developer community.

     

    The course will focus on presenting R applications for the main tools in statistical analysis, data structuring, and modelling.
  • Demand Prediction And Analysis (66004)
    • תקציר הקורס:

    Abstract:

    The course focuses on the methodology and on relevant applications for demand forecasting on short and long term in order to meet the needs of those who practice engineering and systems.

    The course will develop prediction demand models (e.g., travel demand forecasting, forecasting energy / water, and a choice of alternatives).

    The course consists of four parts: (1) Data science; (2) Regression; (3) Time Series; (4) Structural models.

    The examples in the course will include the last examples from real world systems and examples of research that used tools of regression and time series analysis.

    Classroom practice and homework will be done with the software R.
  • Structural and Database Program (66006)
    • תקציר הקורס:

    Abstract:

    The main objective of the course is the provision of foundational tools and knowledge, which will be prerequisite in future courses. The tool we will use is R.

    Paradigms of data science will be presented via introductions to programming, data engineering, statistical inference, and visualization.

    During the course, students will develop a analytics application in R
  • Business Analytics (66022)
    • תקציר הקורס:

    Abstract:

    Planning and production of advance data visualizations.

    Dashboard development and delivery as a project.