Winter 2025 Info: Practical Data Science 🛠️
EECS 398-002/003, Winter 2025 at the University of Michigan
4 credits • Open to all majors • ULCS for Computer Science majors, Advanced Technical Elective or Application Elective for Data Science majors, Flexible Technical Elective for Electrical Engineering majors
Welcome! 👋 If you’re reading this, you’re likely interested in enrolling in Practical Data Science in Winter 2025. This page will give an overview of the course and provide logistical information you’ll need before you enroll.
But, the best way to see what the course is about is to browse the resources (slides, code, recordings, homeworks, exams, etc.) on our Fall 2024 course homepage!
If you have any questions at all, or want to set up a meeting to discuss whether the course makes sense for you, reach out to Suraj at rampure@umich.edu.
As a third year data science major, this has been my favorite course I’ve taken throughout my time in college. It’s the first course that’s felt tailored to my major and relevant to the industry. I’ve loved the combination of learning core Python skills and ML techniques, and have gained a breadth of understanding in just a semester. – current student
Read what other students have said in the Testimonials section below!
Content
Skills and tools for building practical data science projects, along with their theoretical underpinnings.
pandas,numpy,scikit-learn,BeautifulSoup, and Jupyter Notebooks, and also the math behind loss functions, gradient descent, linear and logistic regression, and other key ideas in machine learning.
This course will train students to use industry-standard tools to solve real-world problems, while giving them an understanding of how these tools work under the hood. After taking this course, students will be prepared to build data science portfolios, participate in research across campus, succeed in data science internships, and reason about abstract mathematical problems about the foundations of machine learning models.
The course will roughly be split into two halves.
Python and Jupyter Notebooks
numpy arraysTabular Data Manipulation in
pandasExploratory Data Analysis and Data Visualization
Web Scraping and APIs
SQL
Regular Expressions and Text Processing
Linear Regression through Linear Algebra
Feature Engineering in
scikit-learnRegularization and Cross-Validation
Gradient Descent
Logistic Regression
Decision Trees and Random Forests
Unsupervised Learning
Students will be expected to complete weekly homework assignments, which will mostly comprise of programming assignments in Python and Jupyter Notebooks, with theoretical questions sprinkled throughout (and the occasional purely theoretical homework).
The course will have in-person, on-paper midterm and final exams that involve a mix of multiple choice, short answer, fill in the blank code, math, and English problems (for context, see the Fall 2024 Midterm Exam and solutions here).
Other materials from the Fall 2024 offering can be found linked from the course homepage.
Prerequisites
The course is open to students from all majors.
The enforced prerequisites are discrete math (EECS 203), programming (EECS 280), calculus I, calculus II, and linear algebra. A probability and statistics course is an advisory prerequisite. Options include DATASCI 101, STATS 206, STATS 250, STATS 280, STATS 412, IOE 265, or ECON 451.
If you’re interested in the class but don’t meet one of the prerequisites, email me and we can chat about your background and a potential override. I encourage students of all backgrounds who are curious about data science to reach out!
All students, especially those who haven’t taken a linear algebra course and receive an override, should work through LARDS: Linear Algebra Review for Data Science before the semester starts.
Enrollment Logistics
If you’d like to enroll in the course, sign up for these EECS 398 sections:
- lecture LEC 002 or LEC 003, and
- discussion DIS 021, 022, 023, or 024.
There is only one live, in-person lecture, Monday and Wednesday 3-4:30PM in 1670 BBB (as is listed for LEC 002), and lecture attendance is not required.
- All students, regardless of section, will have access to lecture recordings.
- All students, regardless of enrolled section, can also attend the in-person lectures, space permitting on the day of the attendance. Usually, enough students consume material remotely that we don’t anticipate in-person space to be a bottleneck for anyone wishing to attend the class in-person.
In-person discussion attendance will be required or incentivized: make sure you can attend your enrolled discussion section.
Office hours will largely be in-person, but there will be some remote options as well.
Testimonials
The following quotes are by students who are currently in the course. They were submitted anonymously and have not been edited.
I couldn’t recommend this class enough! I’m a second year CS-LSA student and I’m taking this class as my first ULCS class with EECS 281. Despite not having the strongest foundation in linear algebra coming in, I’ve found the material very accessible with a bit of extra effort. The lectures, led by Suraj, are both engaging and informative; he has a talent in presenting complex ideas in a clear and interesting way that keeps you interested. The homework assignments are particularly cool—they push you to think creatively while applying what you’ve learned in class. The course is manageable with consistent effort, and I’ve found the resources provided by the staff to be helpful for filling in any gaps. If you’re interested in diving deeper into data science and machine learning concepts, I’d definitely recommend taking this course.
As a third year data science major, this has been my favorite course I’ve taken throughout my time in college. It’s the first course that’s felt tailored to my major and relevant to the industry. I’ve loved the combination of learning core Python skills and ML techniques, and have gained a breadth of understanding in just a semester. While the homework can be time-consuming and challenging, it’s so worth it to gain a better understanding of class content. Not only that, but the concepts taught have helped me excel while interviewing for data science roles, and even land multiple offers. Also, the course staff are amazing – they’re incredibly intelligent and friendly, I’ve always had a great experience in office hours. Would highly recommend this course to anyone interested in data science!
This is one of my favorite classes I’ve taken at umich. I have enjoyed the content of almost every lecture, and I wish I had taken this class before my past internship because it would’ve helped a ton. I really appreciate the structure of this course, with weekly homeworks and a good amount of late days, but not enough to fall behind. I actually don’t have a single complaint about how this class is run. The professor gives out a grade report after the midterm, so we can see how many points we have in the class so far, what our current grade is, and how many late days we have remaining. I took linear algebra at a community college and learned pretty much nothing, but I still felt as though I could keep up because of the provided linear algebra review. I would recommend this course to anyone!
As a CS senior, this is one of the best courses I’ve taken at Michigan! The course is incredibly well organized: each week’s homework and discussion worksheet aligns well with the content covered in lecture, staff is very open to feedback, and the workload is very reasonable with consistent effort and doable alongside other difficult courses. Suraj is also such an engaging lecturer, and his notes/presentations are a really great mix of content, interactive demos, and practice problems. I would definitely recommend this course!
Coming into this class, I hadn’t had too much experience with Python and Data Science in general, so I was a bit unsure of how it would go. However, the result was completely unexpected: I now find myself really enjoying the course content not only because it is taught in an engaging way but also because I can see the value in the practical applications that come out of these concepts. Every week, even if I was unsure about a certain topic, I knew I could either come to office hours and get the help I needed, or reinforce those concepts through the homework problems, which really helped me build confidence in my understanding of the material. Most importantly, the course can really help you realize an interest for this content that you may have never known to have, as it did with me: I now am pursuing future courses like Machine Learning because the content in this class was taught so well and gave me a good foundation for future ULCS classes. Overall, the course is meant to challenge you, but it is quite rewarding in the end, and it is an enjoyable experience!
Frequently Asked Questions
If I plan to take, or have already taken, a dedicated machine learning course (such as EECS 445), should I still take this course?
Yes! The first half of this course introduces students to several tools and skills that aren’t typically covered in other machine learning courses, like using more sophisticated features in pandas, scraping data from the internet, finding patterns in text data, etc. While the second half of the class does overlap a bit with more traditional machine learning courses, this course covers the content from a more introductory and practical perspective. Students who have already seen machine learning will reinforce their understanding of the relevant concepts through hands-on, real-world examples (e.g. hyperparameter tuning in sklearn). Students who haven’t already seen machine learning will develop an intuition for how various machine learning algorithms work from the ground up, both practically and theoretically, giving them a strong foundation upon which further machine learning courses can build off of.
What specific topics from linear algebra will the course use?
In addition to matrix-vector multiplication, we will expect students to be familiar with the ideas of linear independence, spans, projections, and orthogonality. We will review these ideas when necessary, but it will help to have seen them already.