1.2 Course Learning Objectives

At this point in the course, there is so much that is before us! As we settle in to study for the semester, it is useful to have a point of view of where we’re trying to go, and what we are going to see along the way.

Allow a justification by analogy:

Suppose that you decide that you would like to be a chef – all of the time watching cooking shows has revealed to you that this is your life’s true calling – and so you enroll in a culinary program.

One does not begin such a program by baking croissants and souffle. They begin the program with knife skills, breaking down ingredients and the basic techniques that build up to produce someone who is not a cook, but a chef – someone who can combine ingredients and techniques to produce novel ideas.

At the same time, however, one has not gone to school just to become a cucumber slicer. The knife skills are instrumental to the eventual goal – of being a chef – but not the goal itself.

At the beginning of the program, we’re teaching these core, fundamental skills. How to read and reason with mathematical objects, how to use conditional probability with the goal of producing a model, and eventually, eventually to create novel work as a data scientist.

At the end of this course, students will be able to:

1.2.1 Understand the building blocks of probability theory that prepare learners for the study of statistical models

  1. Understand the mathematical objects of probability theory and be able to apply their properties.
  2. Understand how high-level concepts from calculus and linear algebra are related to common procedures in data science.
  3. Translate between problems that are defined in business or research terms into problems that can be solved with math.

1.2.2 Understand and apply statistical models in common situations

  1. Understand the theory of statistics to prepare students for inferrential statements.
  2. Understand model parameters and high level strategies to estimate them: means, least squares, and maximum likelihood.
  3. Choose an appropriate statistic, and conduct a hypothesis test in the Neyman-Pearson framework.
  4. Interpret the results of a statistical test, including statistical significance and practical significance.
  5. Recognize limitations of the Neyman-Pearson hypothesis testing framework and be a conscientious participant in the scientific process

1.2.3 Analyze a research question using a linear regression framework

  1. Explore and wrangle data with the intention of understanding the information and relationships that are (and are not) present
  2. Identify the goals of your analysis
  3. Build a model that achieves the goals of an analysis

1.2.4 Interpret the results of a model and communicate them in manner appropriate to the audience

  1. Identify their audience and report process and findings in a manner appropriate to that audience.
  2. Construct regression oriented reports that provide insight for stakeholders.
  3. Construct technical documents of process and code for collaboration and reproducability with peer data scientists.
  4. Read, understand, and assess the claims that are made in technical, regression oriented reports

1.2.5 Contribute proficient, basic work, using industry standard tools and coding practices to a modern data science team.

Demonstrate programming proficiency by translating statistical problems into code. 1. Understand and incorporate best practices for coding style and data carpentry 2. Utilize industry standard tooling for collaboration