Joanna Bieri

Applied Math Professor

Department of Economics

University of Redlands

Intermediate Data Science
Fall 2026

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:
TBA

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:
Class Canvas



• Important Course Documents
  
  Python for Data Analysis, Wes McKinney
  
  Course Syllabus
  
  Schedule of Topics
  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.
  
  
  Link to our GitHub - for getting assignments and version control
  
  
• 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 - 9/1 - Click Here
    
    PRE-CLASS:

    Most days there will be videos, homework assignments, and reading that you are expected to complete before class. It is okay if you don't get all the homework done, but you should attempt every part and keep trying until you are completely stuck and have questions to bring to class.

    CLASS TIME:

    Notes - Computer Setup - Review
    Slides - Computer Setup - Review
    Video - Set up your computer and be successful in this class (important that this is done this week!)
    Send me your GitHub username - I need this to set up your team's repository. Type just the username, not the whole web address.
    
    Once your team's repo is set up (see today's Computer Setup notes), HW_day1.ipynb will already be there - we will work on this in class.
    
    Reading: Python for Data Analysis
    Chapters 2.3 and all of Chapter 3 of our book are a good review of python basics. Please glance through these chapters before starting your prep for Day 2! We will be working a lot in Pandas, but it is important that you know how to deal with Python lists, dictionaries, sets, and tuples (p.47-64). It is helpful to learn how to do list comprehensions p.64, but as long as you can do a for loop you will be fine. We will make use of functions and lambdas as a way to organize our code p.65-76. We will practice at reading in different data types (mostly with pandas but sometimes with other methods) p.79-80.
    

    ANNOUNCEMENTS:

    Finish HW_day1.ipynb with your team - reach out if you need help!
    Video - Github Team Workflow
    Start Prepping for Day 2 - click on the Day 2 link and complete the PRE-CLASS materials.

    
    
  • Day 2 - Thursday - 9/3 - Click Here
    
    PRE-CLASS:

    Reading: Python for Data Analysis: Chapter 5
    NOTE: The book is a great reference for general data analysis. Don't feel like you have to read it line by line. The lecture notes will follow somewhat closely to the book. Have a Jupyter Notebook open so you can try some of the commands!
    
    Notes - Pandas
    Slides - Pandas - summary of functions
    Video - Pandas Review-Advanced
    Pull the latest from your team's repo, then make sure HW_day2.ipynb is there - we will work on this in class.
    
    Push your team's work to git before class starts - this shows your attempt and a timestamp on your progress.

    CLASS TIME:

    Pandas!
    Work on finishing up HW_day1.ipynb.
    Go through warm-up you try problems.
    Work on Titanic Data.
    

    ANNOUNCEMENTS:

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

    
    
  • Day 3 - Tuesday - 9/8 - Click Here
    
    PRE-CLASS:

    Reading: Python for Data Analysis: Chapter 6
    
    Career Reading: Build a Career in Data Science: Chapter 1.1 - What is Data Science. This is only a few pages - please read it, take notes, and come to class ready to chat about it. We discuss it at the start of class today!
    
    Notes - Data Reading Writing and File Types
    Slides - Data Reading Writing and File Types
    Video - Data Reading Writing and File Types - Part1 Overview
    Video - Data Reading Writing and File Types - Part2 Code Walkthrough
    Pull the latest from your team's repo so that Day3 is there, then push your team's work to git before class starts - this shows your attempt and a timestamp on your progress.

    CLASS TIME:

    Career Discussion: 1.1 What is Data Science.
    Collision Course - your first merge conflict, on purpose (~20 min)
    Go through warm-up you try problems.
    Work on HW_Day3 - reading your own data.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 4 - click on the Day 4 link and complete the PRE-CLASS materials.
    This week your team works on both HW_day3 and HW_day4. Open a Pull Request and get it merged by Sunday 9/13 at 11:59pm.

    
    
  • Day 4 - Thursday - 9/10 - Click Here
    
    PRE-CLASS:

    Reading: Python for Data Analysis: Chapter 7
    
    Career Reading: Build a Career in Data Science: Chapter 1.2 - Different Types of Data Science Jobs. Read it and take notes, we discuss it at the start of class on Tuesday 9/15.
    
    Notes - Data Cleaning and Preparation
    Slides - Data Cleaning and Preparation
    Video - Data Cleaning and Preparation
    Pull the latest from your team's repo so that Day4 is there, then push your team's work to git before class starts - this shows your attempt and a timestamp on your progress.

    CLASS TIME:

    Go through warm-up you try problems.
    Work on HW_Day4 - cleaning up messy data.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 5 - click on the Day 5 link and complete the PRE-CLASS materials.
    HW_day3 and HW_day4 are both due this Sunday, 9/13 at 11:59pm. Get your Pull Request reviewed and merged before then.

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




The Data Science Lifecycle!!