Access our archived course in Computer Science and Technology to take a sneak peek into how you can gain exposure into the themes and skills associated with a Master of Science in Computer Science.

Watch the video below to learn more about the content that you will experience throughout this course.

Course Faculty

Nicholas Beauchamp, Assistant Professor of Political Science at Northeastern University.

Martin Schedlbauer, Associate Clinical Professor, College of Computer and Information Science at Northeastern University


  • Week 1: An introduction to R and Computational Statistics featuring Nick Beauchamp & Martin Schedlbauer

    Learning Objectives

    • Gain an introduction to R, which is a powerful statistical programming language.
    • Explore how to apply Bayes’ Theorem to calculate unknown conditional probabilities.
    • Learn how to calculate simple probabilities.

    View this week’s videos in less than 30 minutes!

    Supporting Materials

    Use the following links to support your learning experience with R:
    » R cheat sheet
    » How to save your history in R
    » R Installation and Administration

    Use this site as a resource for calculating and organizing your data sets:
    » Sampling Distributions

  • Week 2: Basic R Programming featuring Martin Schedlbauer

    Learning Objectives

    • Create and manipulate scalars, vectors, data sets, arrays, and data frames.
    • Organize code with functions.
    • List and manage objects.

    View this week’s videos in less than 22 minutes!


    Supporting Materials

    Use the following links to support your learning experience with R:
    » Programming in R
    » R Tutorial (suggested sections to read: 1, 2, 13, 15)
    » R: Basics of R Objects & Manipulating Data
    » R Prettifier: Inside R

  • Week 3: Information Design and Visual Analytics

    Learning Objectives

    • Evaluate the ways in which we perceive information, including the advantages and limitations.
    • Explore perception methods, such as limited-hold memory task, detecting change, peripheral drift, and pre-attentive detection.
    • Consider the opportunities for improving visualizations based on an understanding of the implications on perception and visual variables.

    View this week’s featured video in less than 11 minutes!


    Supporting Materials

    This lesson presented several interesting examples of perception. Click on each link below and explore these on your own.