Joanna Bieri

Applied Math Professor

Department of Economics

University of Redlands

Intermediate Data Science
Fall 2025

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 - Wednesday - 9/3 - 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!)
    
    Remember to pull the class files to your local machine and make a copy in your working directory! This is where you will find HW_day1.ipynb - 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 - come Friday if you need help!
    Start Prepping for Day 2 - click on the Day 2 link and complete the PRE-CLASS materials.

    
    
  • Day 2 - Monday - 9/8 - 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
    Remember to pull the class files to your local machine and make a copy in your working directory! This is where you will find HW_day2.ipynb - we will work on this in class.
    
    Rememer to push your class work to git before class starts - this is to show you attempt and a timestamp on your work.

    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 - Wednesday - 9/10 - Click Here
    
    PRE-CLASS:

    Reading: Python for Data Analysis: Chapter 6
    
    Reading: Build a Career in Data Science: Chapter 1.1 and 1.2. This is only 11 pages - please read it and take notes and come to class to chat about your thoughts!
    
    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
    Remember to push your class work to git before class starts - this is to show you attempt and a timestamp on your work.

    CLASS TIME:

    Go through warm-up you try problems.
    Work on HW_Day3 - reading your own data.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 4

    
    
  • Day 4 - Monday - 9/15 - Click Here
    
    PRE-CLASS:

    Reading: Python for Data Analysis: Chapter 7
    
    Reading: Build a Career in Data Science: Chapter 1.3. Choosing your path!
    
    Notes - Data Cleaning and Preparation
    Slides - Data Cleaning and Preparation
    Video - Data Cleaning and Preparation
    Remember to push your class work to git before class starts - this is to show you attempt and a timestamp on your work.

    CLASS TIME:

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

    ANNOUNCEMENTS:

    Start Prepping for Day 5

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

    Reading: Python for Data Analysis: Chapter 8
    
    Notes - Data Wrangling
    Slides - Data Wrangling
    Video - Data Wrangling
    Remember to push your class work to git before class starts - this is to show you attempt and a timestamp on your work.
    
    Please do the Check in Quiz - I really appreciate your feedback!
    

    CLASS TIME:

    Go through warm-up you try problems.
    Work on HW_Day5 - wrangling - joining - merging - pivoting - melting.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 6

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

    Reading: Python for Data Analysis: Chapter 9
    Reading: Build a Career in Data Science: Chapter 2.1. Data Science Companies - Massive Tech
    
    Notes - Visualization
    Slides - Visualization
    Video - Visualization
    Remember to push your class work to git before class starts - this is to show you attempt and a timestamp on your work.
    
    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:

    Go through warm-up you try problems.
    Work on HW_Day6 - plotting.
    

    ANNOUNCEMENTS:

    Start Prepping for Day 7.

    
    
  • Day 7 - Wednesday - 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. Here are some articles that I would like you to read before class
    The Most Undervalued Skill for Data Scientists
    Practical Advice for Data Science Writing
    
    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:

    Writing in Data Science
    Avoiding Misrepresentation
    Quarto Quarto Install
    

    ANNOUNCEMENTS:

    There is no new homework for Day 7. Please use the extra time to catch up on past homework and really go back through to practice good writing and formatting.
    
    Start Prepping for Day 8.

    
    
  • Day 8 - Wednesday - 10/1 - Click Here
    
    PRE-CLASS:

    Reading: Python for Data Analysis: Chapter 10
    Reading: Build a Career in Data Science: Chapter 2.2. Data Science Companies - Established Retailers
    
    Notes - Data Aggregation and Grouping
    Slides - Data Aggregation and Grouping
    Video - Data Aggregation and Grouping
    Remember to push your class work to git before class starts - this is to show you attempt and a timestamp on your work.
    
    

    CLASS TIME:

    What are your thoughts - Jobs in Massive Tech vs Retail?
    Work on Data Aggregation
    
    

    ANNOUNCEMENTS:

    Start Prepping for Day 9.

    
    
  • Day 9 - Monday - 10/6 - 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
    Slides - Time Series Data
    Video - Time Series Data
    Remember to push your class work to git before class starts - this is to show you attempt and a timestamp on your work.
    
    

    CLASS TIME:

    Group brainstorming on Project Ideas
    Time Series Data
    
    

    ANNOUNCEMENTS:

    Start Prepping for Day 10.

    
    
  • Day 10 - Wednesday - 10/8 - Click Here
    
    PRE-CLASS:

    Reading: Build a Career in Data Science: Chapter 2.3 and 2.4 The early-stage startup The late-stage successful startup
    
    Notes - Web Scraping
    Slides - Web Scraping
    Video - Web Scraping - Intro
    Video - Web Scraping - Dynamic Content
    Remember to push your class work to git before class starts - this is to show you attempt and a timestamp on your work.
    
    Please do the Check In
    

    CLASS TIME:

    What are your thoughts - Jobs at a Start-Up
    Get your Selenium Working
    Work on Web Scraping
    
    Choose groups for Ethics Discussion
    

    ANNOUNCEMENTS:

    Start Prepping for Day 11.
    Exam 1 will be handed out in class on Wednesday 10/15 - and you will have time to work on it over the weekend.

    
    
  • Day 11 - Wednesday - 10/15 - Click Here
    
    PRE-CLASS:

    Reading: Part of the homework is reading the case study. Make sure to do the group work BEFORE CLASS!
    
    Notes - Data Management and Ethics
    Slides - Data Management and Ethics
    Video - Data Management and Ethics
    Remember to push your class work to git before class starts - this is to show you attempt and a timestamp on your work.
    
    

    CLASS TIME:

    Groups lead in Class Discussion
    Talk about the Exam
    

    ANNOUNCEMENTS:

    Start Prepping for Day 12.
    Exam 1 will be due Sunday 10/19 at 11:59pm.

    
    
  • Day 12 - Monday - 10/20 - Click Here
    
    PRE-CLASS: - you really need to run the code before class. We have been spending WAY too much class time installing packages that should be installed outside of class when you start your homework!

    Review: What do you need help with? What do you need to finish up before we move to ML?

    CLASS TIME:

    Put it together, get it done, day!

    ANNOUNCEMENTS:

    Start Prepping for Day 14.
    

    
    
  • Day 13 - Wednesday - 10/22 - Click Here
    
    PRE-CLASS: - you really need to run the code before class. We have been spending WAY too much class time installing packages that should be installed outside of class when you start your homework!

    Reading: Build a Career in Data Science: Chapter 2.5 and 2.7 Government Contractors and Interview
    
    Reading: Intro to Machine Learning with Python Chapter 1.7
    
    Career Building - Do a job search using Linked In, Indeed, DataJobs, Kaggle, and others. Start a list of jobs that interest you along with qualifications, job descriptions, pay, and location.
    
    Notes - Intro to ML
    Slides - Intro to ML
    Video - Intro to ML
    Remember to push your class work to git before class starts - this is to show you attempt and a timestamp on your work.
    
    

    CLASS TIME:

    Introduction to ML
    Work on HW.

    ANNOUNCEMENTS:

    Start Prepping for Day 13.
    

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




The Data Science Lifecycle!!