Data Science Courses
Winter Quarter Courses and Workshops
The Fred Hutch Data Science Lab (DaSL) is excited to launch its second year of biomedical data science training and learning communities! At DaSL, we believe that everyone, regardless of their educational background, can excel at data science.
For Winter Quarter of 2025, we are offering the following in-person and online courses and workshops in the following topics. Each course or workshop will have learning community sessions to extend your skills.
Note that the topics change per quarter.
For more information about courses, look at our Course Catalog
Unsure About What to Learn?
Wracked with indecision, or not sure whether a course is for you? We will have a learning drop-in session for you to learn more about the Data Science Lab course offerings. Let us help you find your own learning path.
| Date | Time | Location |
|---|---|---|
| January 8 | 11:30-1 PM | Thomas Building, Table outside of Pelton Auditorium |
Full List of Winter Quarter Courses and Workshops
This is a list of the course/workshop offerings for Winter Quarter.
| Topic | Name | Type |
|---|---|---|
| Data Science Programming | Intermediate R | Course |
| Data Science Programming | Intro to Python | Course |
| Data Science Programming | Intermediate Python | Course |
| Data Science Programming | Intro to SQL/Big Data | Course |
| Data4All | Better Tables | Workshop |
| Data4All | AI for Coding | Workshop |
| Reproducible Research | Intro to Command Line | Workshop |
| Reproducible Research | Intro to Git/GitHub | Workshop |
| Reproducible Research | Intermediate Git/GitHub | Workshop |
Course Descriptions and Details for Winter Quarter
Note that all courses and workshops have a registration link in the description if they are still open. We do maintain a waiting list for each workshop/course.
Location/Teams Information and other information will be made available after registration.
Data Science Programming
Intermediate R
| Information | |
|---|---|
| Type | Course |
| Dates | Jan 22, 29, Feb. 5, 12, 26, Mar 6 |
| Time | Wednesday 12:00-1:30 |
| Commitment | 6 weeks of classes and optional data-thon |
| Audience | The course is intended for researchers who want to continue learning the fundamentals of R programming and how to deal with messy datasets. The audience should know how to subset dataframes and vectors and conduct basic analysis, and/or have taken our Intro to R course. |
| Registration | Register Link |
| Course Website | Website Link |
The course continues building programming fundamentals in R programming and data analysis. You will learn how to make use of complex data structures, use custom functions built by other R users, creating your own functions, and how to iterate repeated tasks that scales naturally. You will also learn how to clean messy data to a Tidy form for analysis, and conduct an end-to-end data science workflow.
Learning Objectives
- Apply tools for Tidying data to get a messy dataset into analysis-ready form, via data recoding, data transformations, and data subsetting.
- Design and Create simple, custom functions that can be reused throughout an analysis on multiple datasets.
- Explain and utilize iteration in programming to reduce repeated code and batch process collections (such as a folder of files or rows in a table)
Introduction to Python
| Information | |
|---|---|
| Type | Course |
| Dates | Jan. 21, 28, Feb. 4, 11, 25, 28, Mar. 4 |
| Time | Tuesdays 12:00-1:30 |
| Commitment | 6 weeks of classes and optional data-thon |
| Audience | Researchers who want to do more with their data analyses and visualizations. This course is appropriate for those who want to learn coding for the first time, or have explored programming and want to focus on fundamentals in Python. |
| Registration | Register Link |
| Course Website | Website Link |
You will learn the fundamentals of Python, a statistical programming language, and use it to wrangle data for analysis and visualization. The programming skills you will learn are transferable to learn more about Python. At the end of the class, you will be reproducing analysis from a scientific publication!
Learning Objectives:
- Analyze Tidy datasets in the Python programming language via data subsetting, joining, and transformations.
- Evaluate summary statistics and data visualization to understand scientific questions.
- Describe how the Python programming environment interprets complex expressions made out of functions, operations, and data structures, in a step-by-step way.
- Apply problem solving strategies to debug broken code.
Intermediate Python
| Information | |
|---|---|
| Type | Course |
| Dates | Jan. 23, 30, Feb. 6, 13, 27, 28, Mar. 6 |
| Time | Thursdays 12:00-1:30 |
| Commitment | 6 weeks of classes and optional Data-thon |
| Audience | The course is intended for researchers who want to continue learning the fundamentals of Python programming and how to write custom data analysis functions when dealing with messy datasets. The audience should know how to work with Lists and Pandas Dataframes and conduct basic data analysis, and/or have taken our Intro to Python course. |
| Registration | Register Link |
| Course Website | Website Link |
The course continues building programming fundamentals in Python programming and data analysis. You will learn how to make use of complex data structures, iterate repeated tasks that scales naturally , and create your own functions. You will apply these skills to develop a custom data analysis.
Learning Objectives (LOs)
- Understand and distinguish the use case of data structures to store different types of data.
- Implement code to iterate over a collection (such as files, elements of a column, or a list of objects) to batch process each item
- Implement code that has a branching structure depending on input data’s condition.
- Create simple, modular functions that can be reused.
- Describe the difference between copying an object vs. referencing an object and how that could affect variables in a data analysis.
Intro to SQL
| Information | |
|---|---|
| Type | Course |
| Dates | Feb. 7, 14, 28, Mar. 7 |
| Time | Fridays 12:00-1:30 |
| Commitment | 4 weeks of classes and optional Data-thon |
| Audience | Researchers and clinical support staff who need to extract and work with relevant data stored in the OMOP data model. |
| Registration | Register Link |
| Course Website | Website Link |
Data that we need to utilize and query is often stored in data sources such as databases or data warehouses. In this course, you will learn how to connect and query databases using Structured Query Language (SQL). In particular, we will focus on querying data in a commonly used data model for storing patient data called OMOP. By the end of this course, you will be prepared to construct complex queries to retrieve large data sets and automate these queries to produce automated reports and dashboards.
Learning Objectives
Explain data sources such as Databases and how to connect to them
- Query data sources using database engines and Structured Query Language (SQL) to filter, join, summarize, and update data
- Explain the OMOP data model and how it enables clinical data queries
- Schedule queries to pull data from data sources on a regular basis
Data4All
We all work with data in different ways. The DaSL Data4All workshops give you an opportunity to learn about data-related topics that are immediately applicable to your current position. There are no prerequisites for these courses. Everyone is welcome.
Attend multiple sessions and earn your Data4All badge to show others at FH and beyond that you work with data ethically and collaboratively.
Each Data4All workshop includes a list of DaSL training and resources to extend your own knowledgebase.
Note
Data4All workshop announcements will be available after the new year.Better Tables
TBD
AI for Coding
TBD
Reproducible Research
Scientific results and evidence are strengthened if those results can be replicated and confirmed by several independent researchers. When researchers employ transparency in their research - in other words, when they properly document and share the data and processes associated with their analyses - the broader research community is able to save valuable time when reproducing or building upon published results. Often, data or code from prior projects will be re-used by new researchers to verify old findings or develop new analyses.
These workshops aim to help you and your group make your work transparent and reusable by others.
Note
Reproducible workshop announcements will be available after the new year.Introduction to Command Line
TBD
Introduction to Git
TBD
Intermediate Git
TBD