• Specialization: Statistical Modeling for Data Science Applications
  • Instructor: Dr. Brian Zaharatos, Senior Instructor of Applied Mathematics, Director of the Professional Master’s Degree in Applied Mathematics
  • Prior knowledge needed: Basic calculus (differentiation and integration), linear algebra, probability theory, basic statistical inference

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Learning Outcomes

Successful completion of this course demonstrates your achievement of the following learning outcomes for the MS-DS on Coursera:

  • Acquire, clean, wrangle, and manage data.
  • Correctly perform exploratory data analyses in order to assist with the generation of scientific hypotheses.
  • Apply principles and methods of probability theory and statistics to draw rational conclusions from data.
  • Construct an appropriate statistical model in order to answer important scientific or business-related questions.
  • Assess the validity of a statistical model when applied to a particular dataset.
  • Be sensitive to ethical issues that are involved in dealing with data science applications arising in real world situations.
  • Clearly communicate the results of a data science analysis to a non-technical audience.
  • Use peer feedback, self-reflection and video analysis to improve collaboration skills.
  • Create reproducible statistical workflows.
  • Act ethically in the role of professional data scientist.

Course Content

Duration: 11h 7m

In this module, we will introduce the basic conceptual framework for experimental design and define the models that will allow us to answer meaningful questions about the differences between group means with respect to a continuous variable. Such models include the one-way Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) models.

Duration:8h 51m

In this module, we will learn how statistical hypothesis testing and confidence intervals, in the ANOVA/ANCOVA context, can help answer meaningful questions about the differences between group means with respect to a continuous variable.

Duration: 9h 20m

In this module, we will study the two-way ANOVA model and use it to answer research questions using real data.

Duration: 10h 9m

In this module, we will study fundamental experimental design concepts, such as randomization, treatment design, replication, and blocking. We will also look at basic factorial designs as an improvement over elementary “one factor at a time” methods. We will combine these concepts with the ANOVA and ANCOVA models to conduct meaningful experiments.

 

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