Fundamentals of Data Science
South Amboy Enrichment: Summer 2021
July 6th-29th. Mon., Tu., Thu. 9-11am EST
From presidential campaigns to medical trials to Netflix recommendations, data analysis is at the heart of the 21st century world. Fundamentals of Data Science is a six-week summer course that will teach you how to collect, analyze, and visualize data. Students will conduct an independent research project into any topic of their choice, culminating in a final analysis that can be used as a writing sample for college, internship, and job applications.
Students will learn:
- how to collect, analyze, and interpret data.
- programming
- visualization techniques: create compelling figures, tables, maps, and more.
Content Schedule
Lesson 0: The Tools of Data Science
First, we'll setup and get familiar with the tools you'll be using throughout this course - a programming language called R and a free application to make working with R easier called RStudio.
- Installing R & RStudio
- Using and installing R packages
Lesson 1: First Steps of Data Science
What is data? What are the different types of data? How do we store and interact with data?
- Objects
- Classes
- Functions
- Programming principles
Lesson 2: Working with Data
Here, we dive more deeply into data manipulation to learn how to work with data to answer questions that we are interested in.
- Data manipulation with tidyverse
- Data cleaning (filtering, adding or removing rows / columns, etc.)
- Using and creating our own datasets
Lesson 3: Data Visualization
We enter the world of data visualization in R. How can we use data visualization to communicate ideas and analyses? What makes a successful graphic?
- Data visualization with ggplot2
- Aesthetics, facets, themes, labels, etc.
- Scatter plots, line plots, bar plots, etc.
Lesson 4: Summarizing Data
How do we learn from data? We improve our data manipulation and visualization skills by calculating statistics and visualizing trends.
- Summary statistics: means, medians, quantiles, etc.
- Investigating bivariate patterns
- Visualizing univariate measures: histograms, densities, boxplots, etc.
Lesson 5: Mapping and Merging Data
So far, we have worked exclusively with a single dataset in normal spreadsheet format. How do we work with more complex data, like geographic data for maps? How can we combine information from multiple data sources?
- Spatial analysis and visualization
- Merging and joining datasets
- Conditional data manipulation (ifelse, case_when, etc.)
Lesson 6: Review and Advanced Plotting
We have already covered an astounding amount of material. Here, we take a step back and review what we have learned so far. We also cover a few advanced plotting techniques.
- Developing and analyzing a research question.
- Practice with exploring trends between groups.
- More plotting techniques: reordering, scales, combining multiple plots, etc.
Lesson 7: Text Data
Many interesting research questions involve text - song lyrics, books, campaign speeches, Tweets, etc. Here, we'll learn how to work with text data in more detail:
- String detection, cleaning, etc.
- Dates and time strings
- Text-specific visualizations: wordclouds, etc.
- More plotting techniques: reordering, scales, combining multiple plots, etc.
Lesson 8: Relationships Between Variables
A great deal of scientific research explores the relationship between multiple variables. Does increased school funding improve student outcomes? Does smoking cause lung cancer? These are fundamentally questions of causal inference, which we will discuss here:
- Conceptualizing / visualizing statistical relationships
- Correlation vs. causation
- "Controlling" for variables.
Final Project
The purpose of data science is to gain knowledge and insights from data. The ultimate goal of this course is to prepare you to use the tools of data science to conduct an independent analysis of your own. This is your opportunity to apply everything we’ll learned in the class to a topic that you are passionate about.
The final project instructions are found here.
- 7/8: In-class - introduce project, start thinking about research question.
- 7/12: Due: Draft of research question. In-class - discussion and review.
- 7/13: In-class - search for datasets.
- 7/15: Due: Research question and data source.
- 7/19: In-class - Read in dataset to R, start manipulation.
- 7/20: In-class - More data manipulation, work on plots.
- 7/22: Due: at least one plot from your analysis.
- 7/26: In-class - work on final project.
- 7/27: In-class - work on final project.
- 7/29: Due: final written report.