Tentative calendar (weekly topics), subject to change depending on
the pace of the course.
Notes (:file_folder:) involves material discussed in class.
Reading (:book:) involves material that expands lecture topics, as well as coding examples that you should practice on your own.
Misc (:newspaper:) is supporting material that is worth taking a look at.
0. Course Introduction
📇 Dates: Jan 22-25
📎 Topics: Welcome to Stat 133. We begin with the usual review of the course policies/logistics, expectations, topics in a nutshell, etc. Then, we move on with an unconventional introduction to computing with data using my favorite analogy "Data Analysis is a lot like Cooking".
📎 Topics: First things first. At the conceptual level we'll discuss how data analysis projects usually start with a Research Question. Also, we'll describe how Data can actually be seen from a triangular perspective (i.e. my "3 Views of Data"). At the practical level, you'll begin learning basic survival skills for R, followed by an overall review of the RStudio workspace. Then we move on to discuss basic data types and their implementation in R around vectors and other data structures.
2. More Data Structures: Arrays, Lists, and Dataframes
📇 Dates: Feb 04-08
📎 Topics: In this week you'll keep learning more about R data structures like arrays and lists. More specifically, we'll focus on fundamental concepts like atomicity, vectorization, recycling, and subsetting. And given that we are studying vectors and its cousins, we'll briefly review the traditional base graphics approach that is based on R vectors.
📎 Topics: Because data tables are so ubiquitous, you will have the chance to practice some data manipulation operations on data frames. Also, we'll discuss some considerations when importing tables (in R). Likewise, we begin a comprehensive discussion on concepts for data visualization.
📎 Topics: We continue reviewing more concepts of data visualization. At the practical level, it's important that you learn how to manipulate them via R data frames in a more modern and syntactic way. How? By following the data plying framework provided by the package "dplyr".
📎 Topics: Data Analysis Projects (DAPs) are made of files and directories. Therefore, we need to review some fundamental concepts such as the file-system, the command line interface, and some basic shell commands.
6. Housekeeping: Version Control with Git and GitHub
📇 Dates: Mar 04-08
📎 Topics: We continue talking about filestructure topics, and we introduce basic notions of version control systems (VCS) using Git, and the companion hosting platform GitHub.
On the Data side, we begin our discussion about Tables: the most common form in which data is stored, handled, and manipulated. Consequently, we need to talk about the typical storage formats of tabular data, and the relationship between tables and R data frames.
7. Transition to Programming Basics for Data Analysis (part 1)
📇 Dates: Mar 11-15
📎 Topics: You don’t need to be an expert programmer to be a data scientist, but learning more about programming allows you to automate common tasks, and solve new problems with greater ease. We'll discuss how to write basic functions, the notion of R expressions, and an introduction to conditionals.
📎 Topics: In addition to writing functions to reduce duplication in your code, you also need to learn about iteration, which helps you when you need to do the same operation several times. Namely, we review control flow structures such as for loops, while loops, repeat loops, and the apply family functions.
10. Manipulating Character Strings and Testing Functions
📇 Dates: Apr 01-05
📎 Topics: At its heart, computing involves working with numbers. However, a considerable amount of information and data is in the form of text. Therefore, you also need to learn about character strings, and how to perform basic manipulation of strings. In parallel, we'll keep working on writing functions, especially focusing on testing functions.
📎 Topics: To unleash the power of strings manipulation, we need to take things to the next level and learn about Regular Expressions. Namely, Regular expressions are a tool that allows us to describe a certain amount of text called "patterns". We'll describe the basic concepts of regex and the common operations to match text patterns.
📎 Topics: Random numbers have many applications in science and computer programming, especially when there are significant uncertainties in a phenomenon of interest. In this part of the course we'll look at some basic problems involving working with random numbers and creating simulations. Jointly, we will briefly discuss Shiny apps to better visualize the results of some simulations. This type of apps are a nice companion to R, making it quick and simple to deliver interactive analysis and graphics on any web browser.
📎 Topics: Packages are the fundamental units of reproducible R code. They include reusable functions, the documentation that describes how to use them, and sample data. In this part we'll start describing how to turn your code into an R package.
📎 Topics: Creating an R package can seem overwhelming at first. So we'll keep working on the creation of a relatively basic package. This will give you the opportunity to apply most of the concepts seen in the course.
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