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

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

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: 9h

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

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

Duration: 10h

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 鈥渙ne factor at a time鈥� methods. We will combine these concepts with the ANOVA and ANCOVA models to conduct meaningful experiments.

Duration: 5h 30min

You will complete a proctored exam worth 20% of your grade made up of multiple choice questions. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.

Note: This page is periodically updated. Course information on the Coursera platform supersedes the information on this page. Click听View on Coursera听button听above for the most up-to-date information.