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Big Data Analytics

Data Analysis with SAS

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  • Overview

    This objective of this course is to provide students with a strong foundation in fundamental concepts of statistics that are both theoretical and applied. The course will teach enough statistical theory so that students can become educated consumers of analytical methodology, with an emphasis on application of these techniques to reach sound conclusions from real-world data.

    The material will begin with basic concepts and methods, such as probability, exploratory analysis, and inferential testing, and progress to more complex material, such as regression modeling. Analytical challenges unique to large datasets will also be explored. All analytical techniques will be illustrated with examples using SAS statistical software.

     

  • Who Should Take This Course

    WHO SHOULD ATTEND

    This course is for business/systems analysts, IT architects, data administrators/analysts, and managers.

    PREREQUISITES

    Participants should have strong mathematical problem solving skills; a basic knowledge of the core concepts of probability and statistics; and some experience with computer programming.

  • Why You Should Take This Course

    Upon completion of this course, students will be able to:

    • Choose, implement and interpret statistical methods to answer scientific questions.
    • Understand elementary statistical theories and concepts that form the basis of many analytical techniques used in practice, as well as the assumptions that must be met in order for these approaches to be valid.
    • Gain a basic familiarity with the SAS statistical software package,
    • Perform many commonly used SAS procedures and interpret their output.
  • Schedule
  • Course Outline

    Basic Probability: Effect, Error, and Confidence

    • Basic probability concepts:
      • distributions and parameters
      • continuous and discrete probability distributions
      • parameter estimation and confidence intervals
      • the Central Limit Theorem
      • normal approximations
    • Summarizing and characterizing data distributions using exploratory analysis methods and basic graphical techniques
    • Definition and detection of outliers

    Statistical Inference:  Comparisons and Tests

    • Hypothesis testing: type I and II error, p-values, statistical significance and power.
    • Multiple testing issues and alpha inflation.
    • Decision tree for choosing the appropriate statistical test.
  • FAQs
    Is there a discount available for current students?

    UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.

    What is the cancellation and refund policy?

    Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.

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