Section outline

  • DSCI 100

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    Introduction to Data Science with R

    Data science is the process of answering questions with data. This course provides a broad introduction to the principles and practice of modern data science using the statistical programming language R. Students will learn to acquire, clean, transform, explore, visualize, and model data to answer meaningful questions and communicate evidence-based conclusions. Emphasis is placed on developing computational thinking, data literacy, effective data communication, and reproducible analytical workflows. No prior experience with data science, statistics, or programming is required.

    Instructor: Anjali Thapar (BYC 212, x5008, athapar@brynmawr.edu)
    Class Meetings: Park 336, Tuesdays and Thursdays 2:40-4:00 pm 

    Prof. Anjali's Office Hours: Wednesdays 11:30 am to 1 pm and by appointment.

    TA Drop-IN Session Hours:

    • Fridays from 4:00 to 5:00 pm (Zoom Only)
    • Sundays from 3:00 to 5:00 pm (Bettws-y-Coed, Room 127, In-Person and Zoom)
    • Mondays from 7:00 to 9:00 pm (Bettws-y-Coed, Room 127, In-Person and Zoom from 7-8 pm)

    Zoom link for all TA Drop-In Sessions: https://brynmawr-edu.zoom.us/j/99320486778

    Required Readings: We will primarily be using free online resources for this course. The links to the resources will be posted to the course Moodle page.

    The following textbook is a free online supplemental resource: 

    Wickham, H., Cetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science (2e). Available at https://r4ds.hadley.nz/.

    NOTE: Students will need to bring a laptop to class. Students who do not have a personal laptop can check out laptops from Canaday Library and/or contact the instructor.

  • Welcome to DSCI 100

    Welcome to DSCI 100! Before our first meeting on Tuesday, Sept. 1, there is one thing that you must do. There are also two additional things that I encourage you to complete sometime this week.

    Before our first class, please:

    • Create a free Posit Cloud account using the link below. This is required so that you will be ready to work in R during class.

    Sometime this week, please also:

    • Complete the DSCI 100 Introduction Survey (see link below).
    • Record the pronunciation of your name (see link below).
    • Link to RStudio on Posit.Cloud URL

      Click on this link to access the RStudio Cloud workspace for DSCI B100. You will be prompted to create an account. Please use your official college BMC or HC username and email address when creating this account. 

    • DSCI 100 - Introductory Survey URL

  • Week 1: Introduction to Data Science, R, and R Studio

    This first week will feel like a whirlwind tour of Data Science and R. In preparation for our first meeting, please make sure to join the class's workspace on Posit.Cloud (see link under Welcome to DSCI 100). We will be working in R during our first class, so please complete this step before we meet on Tuesday.

  • Week 2: Exploring Variables, Distributions, and Relationships

    This week we will learn more about Quarto files, work with data in R, and discuss exploratory data analysis techniques. By the end of this week, you will be able to create, import, and save data in R; create and interpret univariate and multivariate plots; conduct and report summary statistics.

    Background Readings/Videos for this week:

    • Watch the 2 brief videos on Exploring and Summarizing Categorical and Numerical Data (9/8)
    • Readings
      • Skim Chapter on Describing Data Using Distributions and Graphs (9/8)
      • Skim Chapter on Measures of Central Tendency and Spread (9/10)

    Assignments for this week:

    • Individual Assignment #1 (Due: Target Deadline 9/13 at Submission Deadline 9/14 at midnight)

  • Week 3: Data Visualization with ggplot2

    This week we will work with ggplot2 to create effective visualizations for exploring, understanding, and communicating patterns in the data. We will also discuss best practices for effective graphical communication and practice interpreting and evaluating data visualizations.

    Background Readings/Videos for this week:

    • Watch the ggplot2 introductory videos (before class on Tuesday, 9/15)
    • Readings
      • Introduction to ggplot2(before class on Tuesday, 9/15) 
      • R4DS: Data Visualization with ggplot2 (before class on Thursday 9/17)

  • Week 4: Data Wrangling with dplyr

    Now that we are getting more comfortable using ggplot2 to visualize data, we are going to turn our attention to data wrangling. Data wrangling is the process of transforming and organizing raw data into a form that is appropriate for exploration, visualization, and analysis. It is the bread and butter of data science, and learning to wrangle data efficiently is an essential skill. This week, we will work with the dplyr package to select, filter, arrange, modify, and summarize data.

    Background Tutorials/Readings for this week:

    • Complete the Introductory dplyr Tutorial posted to Class Workspace on Posit Cloud (before class on Tuesday, 9/22)
    • Reading: Chapter on Transforming Data with dplyr (before class on Thursday, 9/24)

    Assignments for this week:

    • Individual Assignment#2 (Due: Target Deadline 9/27 at midnight/Submission Deadline 9/28 at midnight)