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Introduction to Statistical Programming in R

Course Description

In this course, we will use the statistical programming language R to solve problems and analyze and graphically represent data. R is a popular programming language for statistics and data mining and is a great first language to learn. The first part of the course will cover the basics of programming in R, and the second part will cover introductory probability and statistics and how R can be used to analyze real-world data sets. Examples will include coloring maps in the world by various attributes, making sense of COVID-19 data, and analyzing the stock market.

These tools will prepare you for a variety of fields in college, including public health, statistics, economics, and biology. R is a good introductory programming language with excellent graphical capabilities, and can easily be picked up by someone who has no experience programming.

All material can be accessed through Canvas. R is a free, open-source language, so everyone can download this software for free. We will be using the R Studio GUI to run R, which is also free software. Short lectures will be posted on Canvas each day along with exercises so that you can practice what you have learned.

Students will be divided into groups and will be encouraged to work together on problems throughout the course. During the final week, each group will work together on a project that utilizes what they have learned in the course.

By the end of this course, you will gain experience in the following:
• Basics of programming in R. If you have not had exposure to any programming language, this course will also be a good introduction to programming in general.
• Introduction to probability
• Introduction to statistics
• Data management and analysis
• Working together with a group to analyze data and present results

The course will give you an overview of managing, analyzing, and presenting results from real-world data, which will be useful in many different fields that you might pursue in the future.

Prerequisites

No programming or statistical background is necessary. In the past, this course has been best suited to rising juniors or seniors, but rising sophomores have also been successful in learning the material. Algebra I would be helpful but is not required.

Sections

Two Sections Available to Choose From:

Online sections of Pre-College courses are offered in one of the following modalities: Asynchronous, Mostly asynchronous, or Blended. Please review full information regarding the experience here.


Dates: June 27, 2022 - July 15, 2022
Duration: 3 Weeks
Meeting Times: Online - Asynchronous
Status: Closed
Format: Online
Instructor(s): Melissa Eliot
Course Number: 10395

Dates: July 18, 2022 - August 05, 2022
Duration: 3 Weeks
Meeting Times: Online - Asynchronous
Status: Closed
Format: Online
Instructor(s): Melissa Eliot
Course Number: 10396