Joanna Bieri

Applied Math Professor

Department of Economics

University of Redlands

Introduction to Data Science
This is a backup copy of the 2024 class - it is only for reference for DATA 201 students

Office Location:
Duke Hall #209
Phone: 909-748-8630
E-Mail: joanna_bieri@redlands.edu
(Email or Teams are my preferred contact methods)

Data Science Lab:
Wednesday 6-7:30pm Duke 200

Office Hours:
Click here for my schedule.
You can also email me for an appointment!

Link to our Canvas - for submitting work and checking grades:
Section 1 - 1:10-2:30pm
Section 2 - 2:40-3:55pm

Link to our GitHub - for getting assignments and version control

Check out Data Science in a Box - Intro to Data Science - using R.
Our course follows much of this content and thus is licensed under Creative Commons Attribution-ShareAlike 4.0 International.



• Important Course Documents
  
  Course Syllabus
  
  Schedule of Topics - Updated 10/9/24
  NOTE: as the semester progresses we may change up the schedule a bit to suit our class pace and interests. The most recent schedule will be posted here.
  
  Final Projects
  Cheat Sheet of Commands1
  Cheat Sheet of Commands2
  
  
• Daily Assignments - Reading - Handouts
  
  Each day I will post the lecture videos, homework, reading, and other information. Make sure to check here for each day of class.
  • Day 1 - Tuesday - 9/3 - Click Here
    
    The overall goal of this class is to engage in curious exploration. Please be patient with yourself and others! Along the way you will learn how to make pretty graphs, some basics of programming in python, and how to think like a Data Scientist.
    
    PRE-CLASS:

    Each class day will have content that you should explore before class. I build my lectures, labs, and discussions assuming that you have done the work before class. Each day you will submit what you have completed before class. It does not have to be perfect, but it must show your attempt to engage in the material.
    
    ** If you are one to leave tabs open, remember to occasionally refresh the page!

    CLASS TIME:

    Each class will have daily in class content.
    Notes - What is Data Science?
    Slides - What is Data Science?
    Notes (how to) - Set up your computer (important that this is done this week)
    GitHub - Fork the Day1 files to your GitHub and then clone them to your local machine.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 2 - click on the Day 2 link and complete the PRE-CLASS materials.

    
    
  • Day 2 - Thursday - 9/5 - Click Here
    
    PRE-CLASS:

    Video: How to be Successful This video is to walk you through being a successful student in this class.
    Notes - Hello World!
    Video: Hello World!
    GitHub Link - Day2 files
    
    Take the quiz:
    Section 1
    Section 2
    
    ** If you are one to leave tabs open, remember to occasionally refresh the page!

    CLASS TIME:

    Slides - Hello World - Our first data science exploration!
    Time to work on homework
    Get help with technology
    

    ANNOUNCEMENTS:

    Start Prepping for Day 3 - click on the Day 3 link and complete the PRE-CLASS materials.

    
    
  • Day 3 - Tuesday - 9/10 - Click Here
    
    PRE-CLASS:

    Notes - Data and Visualization
    Video - Data and Visualization
    GitHub Link - Day3 files
    
    Take the quiz:
    Section 1
    Section 2
    
    

    CLASS TIME:

    Slides - Data and Visualization
    Homework questions
    Extra in class Plots!
    Get help with technology
    

    ANNOUNCEMENTS:

    Start Prepping for Day 4 - click on the Day 4 link and complete the PRE-CLASS materials.

    
    
  • Day 4 - Thursday - 9/12
    
    PRE-CLASS:

    Notes - Numerical and Categorical Data
    Video - Numerical and Categorical Data
    GitHub Link - Day4 files
    
    Take the quiz:
    Section 1
    Section 2
    
    

    CLASS TIME:

    Slides - Numerical and Categorical Data
    Homework questions
    Extra in class Plots!
    

    ANNOUNCEMENTS:

    No Class next week
    
    Catch up on Class Work and Independent exploration time:
    Once you are 100% caught up in the class, you should choose a book (or a series of articles) to read about issues, ethics, or impacts of data science. (see my examples in class or search for your own sources online). Later in the class we will have discussions about ethics in data science - you will have a head start!
    

    
    

  Tuesday 9/17 and Thursday 9/19 no class.

  • Day 5 - Tuesday - 9/24
    
    PRE-CLASS:

    Notes - Data Wrangling
    Video - Data Wrangling
    GitHub Link - Day5 files
    
    Take the quiz:
    Section 1
    Section 2
    
    

    CLASS TIME:

    Slides - Data Wrangling
    Homework questions
    In class - Data Wrangling Code Session.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 6 - click on the Day 6 link and complete the PRE-CLASS materials.

    
    
  • Day 6 - Thursday - 9/26
    
    PRE-CLASS:

    Notes - Data Wrangling Continued
    Video - Data Wrangling Continued
    GitHub Link - Day6 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Data Wrangling Continued
    Homework questions
    In class - Work on the Exploration Together.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 7 - click on the Day 7 link and complete the PRE-CLASS materials.

    
    
  • Day 7 - Tuesday - 10/1
    
    PRE-CLASS:

    Notes - Data Wrangling Joins and Merges
    Video - Data Wrangling Joins and Merges
    GitHub Link - Day7 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Joins and Merges
    Homework questions
    Start working on the Practice Exam.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 8 - click on the Day 8 link and complete the PRE-CLASS materials.

    
    
  • Day 8 - Thursday - 10/3
    
    PRE-CLASS:

    Notes - Reading in Data and Data Types - fixing Data Errors
    Video - Reading in Data and Data Types - fixing Data Errors
    GitHub Link - Day8 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Reading in Data and Data Types
    Homework questions
    Talk about the Final Projects
    Talk about Exam 1 - Cheat Sheet of Commands
    

    ANNOUNCEMENTS:

    Fall Break - No Class Tuesday 10/8
    Exam 1 - you must work on the exam and submit it before class on Thursday 10/10 - click on the Exam 1 link for more information.

    
    
  • Exam 1 - Thursday- 10/10
    
    PRE-CLASS:

    - Work on Exam1 - after it becomes available on Canvas
    - This exam is open notes, open computer, etc --- but you cannot use any type of human or AI intervention. This includes asking how to do something online, aksing ChatGPT, or working with friends.
    - The pre-class work on the exam must be COMPLETELY YOUR OWN!
    
    Submit the Exam - First Draft
    Exam 1 - Section 1 (1:15pm)
    Exam 1 - Section 2 (2:40pm)
    
    After you submit the exam, you will be able to work in groups on the exam during our class time. You can then go home and work individually on finishing up your best possible work on the exam. Submit your final version of the exam as an additional attempt.

    CLASS TIME:

    Work on Exam 1 in Groups

    ANNOUNCEMENTS:

    Start Prepping for Day 9 - click on the Day 9 link and complete the PRE-CLASS materials.

    
    
  • Day 9 - Tuesday - 10/15
    
    PRE-CLASS:

    Notes - Importing, Re-coding, and Visualizing Data
    Video - Importing, Re-coding, and Visualizing Data
    GitHub Link - Day9 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Importing, Re-coding, and Visualizing Data
    Homework questions
    

    ANNOUNCEMENTS:

    Start Prepping for Day 10 - click on the Day 10 link and complete the PRE-CLASS materials.
    Get your Data Science Ethics Reading Materials!

    
    
  • Day 10 - Thursday - 10/17
    
    PRE-CLASS:

    Notes - Effective Visualization and Data Storytelling
    Video - Effective Visualization and Data Storytelling
    GitHub Link - Day10 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Effective Visualization and Data Storytelling
    Homework questions
    

    ANNOUNCEMENTS:

    Start Prepping for Day 11 - click on the Day 11 link and complete the PRE-CLASS materials.
    Get your Data Science Ethics Reading Materials!

    
    
  • Day 11 - Tuesday - 10/22
    
    PRE-CLASS:

    Notes - Getting Data and Simpsons Paradox
    Video - Getting Data and Simpsons Paradox
    GitHub Link - Day11 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Getting Data and Simpsons Paradox
    Homework questions
    

    ANNOUNCEMENTS:

    Start Prepping for Day 12 - click on the Day 12 link and complete the PRE-CLASS materials.
    You should be reading your Data Ethics Book!

    
    
  • Day 12 - Thursday - 10/24
    
    PRE-CLASS:

    Helpful lecture and video for preparing your final project proposal (due 10/31)
    Notes - Doing Data Science
    Video - Doing Data Science
    Day 12 - Webscraping!
    Notes - Web Scraping
    Video - Web Scraping
    GitHub Link - Day12 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Web Scraping
    Homework questions
    

    ANNOUNCEMENTS:

    Start Prepping for Day 13 - click on the Day 13 link and complete the PRE-CLASS materials.
    Come to class next week prepared to discuss your reading with your classmates and professor!
    
    LOTS OF POSSIBLE ARTICLES - DATA ETHICS
    The Ethics of Managing People's Data
    Big data ethics and 10 controversial experiments
    Introduction to Data Ethics
    The Importance of Ethical Data Collection
    Ethics of Artificial Intelligence
    Is Ethical AI even possible
    Dealing with bias in AI
    Who is making sure the AI machines aren't racist?
    Algorithmic Bias in Healthcare
    Health Care Bias Is Dangerous. But So Are ‘Fairness’ Algorithms
    Shedding light on AI bias with real world examples
    Ethics In The Age Of Data: Navigating The Crossroads Of Privacy And Progress
    The Ethics of Data Ownership: Who Owns Your Digital Identity?
    Conducting Research with Tribal Communities: Sovereignty, Ethics, and Data-Sharing Issues
    Ethical Research with Indigenous Peoples: Doing Right by Respecting Native Rights
    Emancipatory Data Science: A Liberatory Framework for Mitigating Data Harms and Fostering Social Transformation
    

    
    
  • Day 13 - Tuesday - 10/29
    
    PRE-CLASS:

    Notes - Data Ethics - Misrepresentation and Data Privacy
    Video - Data Ethics - Misrepresentation and Data Privacy
    GitHub Link - Day13 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Data Ethics - Misrepresentation and Data Privacy
    In Class Discussion
    

    ANNOUNCEMENTS:

    Start Prepping for Day 14 - click on the Day 14 link and complete the PRE-CLASS materials.
    You should be reading/finishing your Data Ethics Book!
    Exam 2 will cover:
    

    - Loading data and doing basic analysis - shape, describe(), columns, look for weird data.
    - The data in this exam will include NaNs
    - Masking data to find specific information.
    - Creating new columns in the data frame - doing a calculation using other columns.
    - Using groupby(), value_counts(), and sort_values()
    - YMerging two data frame into one data frame
    - Visualizing data both recreating a given plot and coming up with a plot of your own.
    - Communicating in written form what the results, data frames (tables) and visualizations (plots) mean in terms of the data.
    
    

    
    
  • Day 14 - Thursday - 10/31
    
    PRE-CLASS:

    Notes - Data Ethics - Algorithmic Bias
    Video - Data Ethics - Algorithmic Bias
    GitHub Link - Day14 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Data Ethics - Algorithmic Bias
    In class Discussion
    

    ANNOUNCEMENTS:

    Work on Exam 2 - You should submit your work before you come to class where we will work on the exam in groups.
    My solutions to Exam 1 are now posted on Canvas - you can see what kind of writing and explanation I expect
    
    Start writing your Final Project Proposal - DUE THURSDAY 11/7/24 (updated to Tuesday 11/12/24)

    
    
  • Exam 2 - Thursday- 11/07
    
    PRE-CLASS:

    - Work on Exam2 - after it becomes available on Canvas
    - This exam is open notes, open computer, etc --- but you cannot use any type of human or AI intervention. This includes asking how to do something online, asking ChatGPT, or working with friends.
    - The pre-class work on the exam must be COMPLETELY YOUR OWN!
    
    Submit the Exam - First Draft
    Exam 2 - Section 1 (1:15pm)
    Exam 2 - Section 2 (2:40pm)
    
    After you submit the exam, you will be able to work in groups on the exam during our class time. You can then go home and work individually on finishing up your best possible work on the exam. Submit your final version of the exam as an additional attempt.
    
    Cheat Sheet of Commands 1
    Cheat Sheet of Commands 2
    Video - Common Errors in Python
    Video - Homework Day 11 - Solutions - live programming
    

    CLASS TIME:

    Work on Exam 2 in Groups

    ANNOUNCEMENTS:

    Start Prepping for Day 15 - click on the Day 15 link and complete the PRE-CLASS materials.
    Finish writing your Final Project Proposal - DUE TUESDAY 11/12/24 - Here is info about the proposals Final Projects
    

    
    
  • Day 15 - Thursday - 11/14
    
    PRE-CLASS:

    Notes - Introduction to Modeling and Algorithms
    Video - Modeling with Plotly Trendlines
    Video - Modeling with Linear Regression
    GitHub Link - Day15 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Introduction to Modeling and Algorithms
    Work on the assignment in class
    

    ANNOUNCEMENTS:

    Start Prepping for Day 16 - click on the Day 16 link and complete the PRE-CLASS materials.
    

    
    
  • Day 16 - Tuesday - 11/19
    
    PRE-CLASS:

    Notes - Modeling Nonlinear Relationships
    Video - Modeling Nonlinear Relationships
    GitHub Link - Day16 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Modeling Nonlinear Relationships
    Work on the assignment in class
    

    ANNOUNCEMENTS:

    Start Prepping for Day 17 - click on the Day 17 link and complete the PRE-CLASS materials.
    
    Linear Regression Kaggle Search - probably good data for linear regressions
    
    

    
    
  • Day 17 - Thursday - 11/21
    
    PRE-CLASS:

    Notes - Modeling with Multiple Predictors
    Video - Modeling with Multiple Predictors
    GitHub Link - Day17 files
    
    Take the quiz:
    Section 1 (1:15pm)
    Section 2 (2:40pm)
    
    

    CLASS TIME:

    Slides - Modeling with Multiple Predictors
    Work on the assignment in class
    Bike Rental Image to Reproduce
    

    ANNOUNCEMENTS:

    Start Prepping for Day 18 - click on the Day 18 link and complete the PRE-CLASS materials.
    
    

    
    
  • Day 18 - Tuesday - 11/26 - no in person class meeting
    
    PRE-CLASS:

    Notes - Classification and Categorical Data
    Video - Classification and Categorical Data
    GitHub Link - Day18 files
    
    No Quiz
    

    CLASS TIME:

    No Class - please watch the video and try the homework. I will answer questions about the homework during our next in person class.

    ANNOUNCEMENTS:

    You should be working on your Group Final Project. Come to class ready to have really productive group work time!
    
    

    
    
• Homework Solutions - Exam Review
  
  All Practice Problems and Programming Assignment solutions are available on Canvas
  
  
  
  
  




I catch all the buggies in my code!