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


Final Project Information



• Important Course Documents
  
  Python for Data Analysis, Wes McKinney
  
  Course Syllabus
  
  Git Command Card - the weekly commands on half a page. Keep it next to you.
  
  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 sandbox and your team's repository. Type just the username, not the whole web address.
    
    Once your two repos are set up (see today's Computer Setup notes and the Git Command Card at the top of this page), HW_day1.ipynb will be in your sandbox - 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 (recorded for the old one-repo setup. Follow the Git Command Card instead.)
    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
    Video - Pandas Review-Advanced
    Pull the latest into your sandbox, then make sure HW_day2.ipynb is there - we will work on this in class.
    
    Push your team's work to your team repo before class starts - this shows your attempt and a timestamp on your progress.

    CLASS TIME:

    Slides - Pandas
    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
    Video - Data Reading Writing and File Types - Part1 Overview
    Video - Data Reading Writing and File Types - Part2 Code Walkthrough
    Pull the latest into your sandbox so that Day3 is there, then push your team's work to your team repo before class starts - this shows your attempt and a timestamp on your progress.

    CLASS TIME:

    Slides - Data Reading Writing and File Types
    Career Discussion: 1.1 What is Data Science.
    Notes - Merge Conflicts, On Purpose (~20 min, with a partner)
    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.
    We used Thursday 9/10 to catch up on git, so Day 4 moves to Tuesday 9/15. This week your team only works on HW_day3. Open a Pull Request and get it merged by Sunday 9/13 at 11:59pm.

    
    
  • Day 4 - Tuesday - 9/15 - 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 Thursday 9/17.
    
    Notes - Data Cleaning and Preparation
    Video - Data Cleaning and Preparation
    Pull the latest into your sandbox so that Day4 is there, then push your team's work to your team repo before class starts - this shows your attempt and a timestamp on your progress.
    
    Please do the Check In 1 quiz on Canvas - I really appreciate your feedback!
    

    CLASS TIME:

    Slides - Data Cleaning and Preparation
    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_day4 and HW_day5 are both due Sunday 9/20 at 11:59pm. Get your Pull Request reviewed and merged before then.

    
    
  • Day 5 - Thursday - 9/17 - Click Here
    
    PRE-CLASS:

    Reading: Python for Data Analysis: Chapter 8
    
    Notes - Data Wrangling
    Video - Data Wrangling
    Pull the latest into your sandbox so that Day5 is there, then push your team's work to your team repo before class starts - this shows your attempt and a timestamp on your progress.

    CLASS TIME:

    Slides - Data Wrangling
    Career Discussion: 1.2 Different Types of Data Science Jobs.
    Go through warm-up you try problems.
    Work on HW_Day5 - wrangling - joining - merging - pivoting - melting.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 6 - click on the Day 6 link and complete the PRE-CLASS materials.
    This week your team works on both HW_day4 and HW_day5. Open a Pull Request and get it merged by Sunday 9/20 at 11:59pm.

    
    
  • Day 6 - Tuesday - 9/22 - Click Here
    
    PRE-CLASS:

    Reading: Python for Data Analysis: Chapter 9
    Career Reading: Build a Career in Data Science: Chapter 1.3 - Choosing your Path. Read it and take notes, we discuss it at the start of class on Thursday 9/24.
    
    Notes - Visualization
    Video - Visualization
    Pull the latest into your sandbox so that Day6 is there, then push your team's work to your team repo before class starts - this shows your attempt and a timestamp on your progress.
    
    Show and Tell -- How do you avoid misrepresentation when creating visualizations? Please bring to class at least one example to share of data misrepresentation through visualization. You can look up examples online or create one of your own.

    CLASS TIME:

    Slides - Visualization
    Show and Tell: your misrepresentation examples.
    Go through warm-up you try problems.
    Work on HW_Day6 - plotting.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 7 - click on the Day 7 link and complete the PRE-CLASS materials.
    This week your team works on HW_day6. Open a Pull Request and get it merged by Sunday 9/27 at 11:59pm.

    
    
  • Day 7 - Thursday - 9/24 - Click Here
    
    PRE-CLASS:

    Reading:
    
    We will take a break from new content to talk about how to do high quality writing in data science. There is no video for this day, we will do the lecture live in class. Please read these two articles before class:
    The Most Undervalued Skill for Data Scientists
    Practical Advice for Data Science Writing
    
    Career Reading: Build a Career in Data Science: Chapter 1.3 - Choosing your Path. We discuss it at the start of class.
    
    

    CLASS TIME:

    Slides - Writing in Data Science
    Notes - Writing in Data Science (read these after class)
    Career Discussion: 1.3 Choosing your Path
    Writing in Data Science - live lecture and discussion
    Use the ideas on your HW 6 write-up
    

    ANNOUNCEMENTS:

    There is no new homework for Day 7. Use the extra time to catch up and make your HW 6 write-up really good. Your team's HW 6 Pull Request should be merged by Sunday 9/27 at 11:59pm.
    
    Start Prepping for Day 8.

    
    
  • Day 8 - Tuesday - 9/29 - Click Here
    
    PRE-CLASS:

    Reading: Python for Data Analysis: Chapter 10
    Career Reading: Build a Career in Data Science: Chapter 2.1 - Data Science Companies: Massive Tech. Read it and take notes, we discuss it at the start of class on Thursday 10/1.
    
    Notes - Data Aggregation and Grouping
    Video - Data Aggregation and Grouping
    Pull the latest into your sandbox so that Day8 is there, then push your team's work to your team repo before class starts - this shows your attempt and a timestamp on your progress.
    
    

    CLASS TIME:

    Slides - Data Aggregation and Grouping
    Go through warm-up you try problems.
    Work on HW_Day8 - aggregation and grouping.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 9 - click on the Day 9 link and complete the PRE-CLASS materials.
    HW 8 and HW 9 both go in the Week05 folder of your team repo. Open a Pull Request and get them merged by Sunday 10/4 at 11:59pm. Push your sandbox too, it is your backup: if something is missing from your team repo, I will look there.

    
    
  • Day 9 - Thursday - 10/1 - Click Here
    
    PRE-CLASS:

    Start Thinking: What kind of project do you want to do for your final project in this class. What is your area of interest? Can you start looking to see what kind of data is out there? Here are a few places to look:
    Kaggle Datasets — community-shared datasets, competitions, notebooks and easy CSV downloads.
    UCI Machine Learning Repository — classic machine-learning datasets (small to medium size).
    Google Dataset Search — search engine for datasets across many repositories.
    AWS Open Data Registry — large public datasets hosted on Amazon Web Services.
    Data.gov — U.S. government open data portal (census, health, transport, etc.).
    EU Open Data Portal — datasets from European Union institutions and agencies.
    Zenodo — general-purpose research data repository (DOI-enabled).
    Figshare — research outputs and datasets, often with metadata and DOIs.
    DataHub — community-curated datasets and data packages in multiple formats.
    U.S. Federal Data Catalog — searchable catalog of federal datasets and APIs.
    data.world — social platform for datasets, collaborative projects, and SQL queries.
    GitHub — Datasets Collection — many dataset repos and curated lists hosted on GitHub.
    NASA Open Data — Earth observation, space science, and mission datasets.
    World Bank Open Data — global development indicators and country statistics.
    FiveThirtyEight Data — cleaned datasets used in FiveThirtyEight stories (great for teaching/analysis).
    Microsoft Research Open Data — research datasets from Microsoft Research.
    KDnuggets Datasets Directory — curated links and dataset lists for ML and data science.
    NOAA (National Centers for Environmental Information) — weather, climate, and oceanographic datasets.
    FRED (Federal Reserve Economic Data) — U.S. and global economic time series (GDP, CPI, interest rates, etc.).
    
    
    Reading: Python for Data Analysis: Chapter 11
    Notes - Time Series Data
    Video - Time Series Data
    Pull the latest into your sandbox so that Day9 is there, then push your team's work to your team repo before class starts - this shows your attempt and a timestamp on your progress.
    
    

    CLASS TIME:

    Slides - Time Series Data
    Career Discussion: 2.1 Data Science Companies - Massive Tech
    Group brainstorming on Project Ideas
    Go through warm-up you try problems.
    Work on HW_Day9 - time series.
    
    

    ANNOUNCEMENTS:

    Start Prepping for Day 10 - click on the Day 10 link and complete the PRE-CLASS materials.
    HW 8 and HW 9 both go in the Week05 folder of your team repo. Open a Pull Request and get them merged by Sunday 10/4 at 11:59pm. Push your sandbox too, it is your backup: if something is missing from your team repo, I will look there.
    Exam 1 is handed out on Tuesday 10/6.

    
    
  • Tuesday - 10/6 - No Class - Start Exam 1 - Click Here
    
    NO CLASS TODAY:

    We are not meeting today. Use the class time to start working on Exam 1!
    The exam and the data are on Canvas (Exam 1 - Hand In). Work on it on your own.
    

    ANNOUNCEMENTS:

    Hand in your exam on Canvas AND push it to an Exam1 folder in your sandbox before class starts on Thursday 10/8.
    

    
    
  • Thursday - 10/8 - Exam 1 Group Work - Click Here
    
    PRE-CLASS:

    Hand in your exam on Canvas AND push it to an Exam1 folder in your sandbox before class starts.
    

    CLASS TIME:

    Work on Exam 1 with your team.
    

    ANNOUNCEMENTS:

    Your final version of Exam 1 is due tonight at 11:59pm, on Canvas and pushed to your sandbox. Add your self-assessment at the end.
    No class Tuesday 10/13, Fall Break!
    Start Prepping for Day 10 - click on the Day 10 link and complete the PRE-CLASS materials.

    
    
  • Tuesday - 10/13 - No Class - Fall Break!
    
    

    No class, enjoy your break!

    
    
  • Day 10 - Thursday - 10/15 - Click Here
    
    PRE-CLASS:

    Reading: The Ethics of Using Hacked Data: Patreon's Data Hack and Academic Data Standards (Poor and Davidson, Data & Society). A short case study, 7 pages. Read it and think about the questions on page 6, we talk about them in class.
    
    Notes - Web Scraping and Ethics in Data Acquisition
    Video - Web Scraping
    Video - Data Management and Ethics
    Pull the latest into your sandbox so that Day10 is there.
    
    

    CLASS TIME:

    Slides - Web Scraping and Ethics
    Go through warm-up you try problems.
    Work on HW_Day10 - web scraping and ethics.
    
    

    ANNOUNCEMENTS:

    HW 10 goes in the Week06 folder of your NEW team repo. Get it merged by Sunday 10/25 at 11:59pm (the class voted to move it).
    Final Project: your Draft Proposal is due Sunday 10/25 at 11:59pm. See the Final Project Information page.
    Career Reading: Build a Career in Data Science: Chapter 2.2 - Data Science Companies: Established Retailers. We discuss it in class on Tuesday 10/20.

    
    
  • Day 11 - Tuesday - 10/20 - Click Here
    
    PRE-CLASS:

    Reading: Introduction to Machine Learning with Python: Chapter 1.7 - A First Application: Classifying Iris Species
    
    Notes - Introduction to Machine Learning
    Video - Introduction to Machine Learning
    Pull the latest into your sandbox so that Day11 is there.
    
    

    CLASS TIME:

    Slides - Introduction to Machine Learning
    Go through warm-up you try problems.
    Work on HW_Day11 - K-nearest neighbors and the avocado ripeness data.
    
    

    ANNOUNCEMENTS:

    HW 11 goes in the Week07 folder of your team repo, along with HW 12. Get them both merged by Sunday 11/1 at 11:59pm (moved: Thursday 10/22 is a Catch-Up Day).
    Final Project: your Draft Proposal is due Sunday 10/25 at 11:59pm. See the Final Project Information page.
    Career Reading: Build a Career in Data Science: Chapters 2.3 and 2.4 - Data Science Companies: Startups. We discuss all of the chapter 2 company types together on Tuesday 11/10.

    
    
  • Catch-Up Day - Thursday - 10/22 - Pause and Get Caught Up
    
    No new material today. We are pausing to get caught up!
    Details coming soon.
    
    ANNOUNCEMENTS:

    HW 10 (Week06) is due Sunday 10/25 at 11:59pm. HW 11 and HW 12 (Week07) are now due Sunday 11/1 at 11:59pm.
    Final Project: your Draft Proposal is due Sunday 10/25 at 11:59pm. See the Final Project Information page.
    Career Reading: Build a Career in Data Science: Chapter 2.5 - Data Science Companies: Government Contractors. Read it by Tuesday 11/3. We discuss all of the chapter 2 company types together on Tuesday 11/10.
    Day 12 is now Tuesday 10/27 - click on the Day 12 link and complete the PRE-CLASS materials.

    
    
  • Day 12 - Tuesday - 10/27 - Click Here
    
    PRE-CLASS:

    Reading: Introduction to Machine Learning with Python: Chapters 2.1 and 2.2
    Career Reading: Build a Career in Data Science: Chapters 2.3 and 2.4 - Data Science Companies: Startups. We discuss all of the chapter 2 company types together on Tuesday 11/10.
    
    Notes - Linear and Logistic Regression
    Video - Linear and Logistic Regression
    Pull the latest into your sandbox so that Day12 is there.
    
    

    CLASS TIME:

    Slides - Linear and Logistic Regression
    Go through warm-up you try problems.
    Work on HW_Day12 - linear and logistic regression on the avocado data.
    
    

    ANNOUNCEMENTS:

    HW 11 and HW 12 both go in the Week07 folder of your team repo. Get them merged by Sunday 11/1 at 11:59pm.
    Final Project: your Project Proposal and Exploratory Data Analysis is due Tuesday 11/10 at 11:59pm. See the Final Project Information page.
    

    
    
  • Day 13 - Thursday - 10/29 - Click Here
    
    PRE-CLASS:

    Reading: Introduction to Machine Learning with Python: Chapters 2.3.1 to 2.3.3
    
    Notes - KNN, Linear Models and Regularization
    Video - KNN and Linear Models
    Video - Regularization and Standard Scaler
    Video notes: Elastic Net is optional. The final test result at the end is corrected in the notes.
    Pull the latest into your sandbox so that Day13 is there.
    
    

    CLASS TIME:

    Slides - KNN, Linear Models and Regularization
    Go through warm-up you try problems.
    Work on HW_Day13 - KNN, linear models and regularization on the life expectancy data.
    
    

    ANNOUNCEMENTS:

    HW 13 and HW 14 both go in the Week08 folder of your team repo. Get them merged by Sunday 11/8 at 11:59pm.
    Final Project: your Project Proposal and Exploratory Data Analysis is due Tuesday 11/10 at 11:59pm. See the Final Project Information page.
    Career Reading: Build a Career in Data Science: Chapters 4.1 and 4.3 - Building a Portfolio. We discuss portfolios, resumes and cover letters together on Tuesday 11/17.
    Don't forget to sign up for Spring classes: DATA 211!

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




The Data Science Lifecycle!!