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Big Data Analytics

Data Analysis with Excel

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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, descriptive statistics, exploratory analysis, and inferential testing. The course progresses to more complex material, such as regression modeling. Analytical challenges unique to large and/or heterogeneous datasets will also be explored. All analytical techniques will be illustrated with examples using Microsoft Excel. Students will analyze a variety of real world sample data sets during the course.

  • Who Should Take This Course

    WHO SHOULD ATTEND

    The target audience is anyone with a business need to analyze data in their organization, including but not limited to: business decision makers, business analysts, data analysts, data scientists, intelligence analysts, project managers.

    PREREQUISITES

    Participants should have strong mathematical problem solving skills, and experience with the basic features of Microsoft Excel. Experience working with data sets and/or databases is helpful.

  • Why You Should Take This Course

    This course will help students gain the practical ability to choose, implement and interpret statistical methods to answer scientific and/or business questions. Students will 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. Students will also gain familiarity with the statistical, data import, data analysis, and graphical capabilities of Microsoft Excel, and how to use them to effectively analyze and visualize data and communicate results and conclusions.

  • Schedule
  • Course Outline

    Using Excel Review
    • Review the basic features of Microsoft Excel to ensure students are all at the same baseline
    competence.
    • Introduce the statistical, data import, data analysis, and graphical capabilities of Microsoft
    Excel which will all be used in subsequent modules of the course.

    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 and normal approximations.
    • Conditional probability.
    • How to summarize and characterize 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.
    • Common types of bias that arise in statistics and how to detect and avoid them.

    Statistical Modeling:  Regression and Prediction
    The General Linear Model will be introduced:
    • Bivariate techniques: correlation, simple linear regression and logistic regression.
    • More complex forms such as multiple linear regression, repeated measures models, and multiple
    logistic regression.
    • Model fitting strategies and diagnostics.
    • Statistical inference and hypothesis testing in regression.
    • Define/contrast the concepts of interaction and confounding, how to detect and address each using multiple regression

    Machine Learning (Optional)
    • Various techniques in Machine Learning can be explored within Microsoft Excel, such as K- Means Clustering, Naïve Bayes, Network Graph Analysis, and Optimization

    Special Applications for Large Datasets (Optional)
    • Hazards of Big Data: confusing correlation and causation, biased samples, distinguishing signals
    from noise.
    • How Big Data analysis can give rise to false positives.
    • Tools to handle large datasets.

  • 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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