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About the Course
Course Overview
Welcome to the Data Science Society at Berkeley’s very own DeCal: Introduction to Real World Data Science, a semester-long course designed to equip students with essential data science skills through hands-on, project-based learning. Our program takes a departure from the traditional classroom setting, empowering members to work collaboratively on an original data science project of their choosing, giving them full creative control. Throughout the course, students will receive mentorship from our Decal’s committee members. We will cover every stage of the data science lifecycle – from formulating a data-centric project proposal, to discovering insights through exploratory data analysis, and concluding with the engineering and evaluation of machine learning models.
This introductory course covers a wide variety of data science concepts, including topics both traditionally and not traditionally taught in the lecture halls of Berkeley. We aim to equip students with programming expertise, through engaging lessons that mirror the data science lifecycle. As students progress, they will directly apply these concepts in their own projects, gaining first-hand experience with each stage of their project’s lifecycle.
In this course, students will not only develop and refine their technical skills, but also learn from real-world data scientists through panels and speaker events. Data 198: Introduction to Real World Data Science will challenge students and prepare them in the search for post-graduate opportunities, all while maintaining the low-pressure environment of a DeCal that prioritizes learning above all else.
This course is a 2-unit, application-based P/NP class designed as a hands-on supplement for those interested in learning data science at UC Berkeley. Emphasizing concepts that require minimal prior knowledge, the course will guide students through a data science project (undergoing the entire data science lifecycle) which will help you build up your portfolio. Grading is based on the satisfactory completion of this project, with a few additional checkpoints and weekly mini-vitamins to reinforce learning.
Learning Outcomes
- Understand the data science lifecycle and develop core skills in data manipulation, visualization, model development, and model evaluation to make predictions and answer real-world questions.
- Synthesize data science concepts and apply them collaboratively in industry settings.
- Develop the ability to communicate complex data-based procedures and findings to academic peers.
- Understand how to ideate an original project based on personal curiosity and data collection.
Prerequisites
The curriculum and format is designed specifically for students who have not previously taken computer science, and data science courses. Students with some prior experience in computing are welcome to enroll, and often find that this course offers a new perspective that blends computational and inferential thinking. Students who have taken several statistics or computer science courses should instead take a more advanced course like Data 100.
Support
You are not alone in this DATA 198: Introduction to Real World Data Science the staff and instructors are here to support you as you learn the material. It’s expected that some aspects of the course will take time to master, and the best way to master challenging material is to ask questions.
Your TA and tutor will be your main point of contact for all course related questions/grade clarifications. The TAs and tutors are here to support you so please lean on your lab TA if you need more support in the class or have any questions/concerns.
Important Note: We don’t use any personal or Berkeley emails to handle Decal logistics or requests. Any emails of this form to Facilitator’s or TA’swill be disregarded.
- For personal inquiries during the semester, please message your TA on private messaging platforms or their personal Berkeley email.
- For inquiries before or after the semester, email dss.data198@gmail.com
Course Components
Live Lecture
Live lectures will be held on Mondays, 6:30PM to 8:30PM, at Wurster 102; students are expected to attend live lectures synchronously, as we will not be recording lectures unless enforced otherwise by university guidelines. Slides and lecture examples will be provided on the course website during class time.
Grades
Grades will be assigned using the following weighted components.
| Activity | Grade |
| Attendance | 30% (First two classes are mandatory) |
| Mini-Vitamins | 20% |
| Checkpoints/Reflections | 20% |
| Final Project | 30% |
Attendance
This class will be difficult if you do not keep up with the material. Prior to each class, you should review previous material and be engaged in class discussion. Students will be expected to attend the DeCal, with attendance being taken strictly during the end of lecture.
The first two meetings of the DeCal are mandatory, no exceptions. If you miss any of the first two meetings, your spot will be taken by the next person on the waitlist.
There are up to two unexcused absences allowed without penalty. Excused attendances (i.e. family emergencies, midterm conflicts, medical reasons) should be approved by Course Directors at least 24 hours prior to the class time. To get approved for an excused absence, you may choose one of two options:
- Contact your TA and Tutor about your absence
- Send a message to dss.data198@gmail.com explaining your absence.
Mini Vitamins
We will also have mini-vitamins due the night after each lecture (Monday 11:59PM). These mini-vitamins are graded on correctness, and designed to be a quick concept check for the topics covered during lecture.
You are allowed to collaborate with each other, as well as their TA and tutor on mini-vitamin questions, but you must answer the questions yourselves. Students will submit these mini-vitamins on Bcourses.
Assignments
In order to properly pace students through the final project, there will be 6 assignments in total: 4 project checkpoints and 2 reflections. Completing these assignments are essential for doing well on the final project, and ultimately the DeCal. Project checkpoints will be submitted on Bcourses.
Final Project
Over the course of the semester, along with the support of their TAs and tutors, students will work on a data science project in a domain of their choice. Students are expected to present their work during the end of semester final research symposium.
Projects are an essential component to any technical course, especially one that aims to synthesize computing with real-world applications.
The project for this class will be graded on three points: Content, Presentation, and Collaboration.
Projects are designed to be open-ended, and meant for applying the skills, topics, and techniques learned during the course. This allows for students to experience a date science domain of their choosing, while still grounding themselves in the material of the course.
Late project submissions are deducted 10% for every day they are late.
No project submissions of any kind will be accepted after Nov 15th, as to provide staff with enough time to grade before the final grade submission deadline
Reminder: This course is 2 unit P/NP and a grade of 70% or higher is required to pass the course.
Supervision & Responsibility of Instructor of Record
- The Instructor of Record for DATA 198 - Introduction to Real-World Data Science will be TBD
- The lecturers will share instructional materials with the Instructor of Record, via email/Google Drive, at least two weeks before the instruction, and expect to get feedback, via email/comments on materials within a week.
- The lecturers will then have ~5 days to revise and rehearse for new materials.
- The lecturers will schedule meetings with the Instructor of Record, at least a week before instruction begins, to seek advice and/or explanation on confusing/uncertain concepts.
- Our Instructor of Record will also help with creating bCourses site, if necessary. If eventually the lecturers decide to use Git/GitHub purely, Instructor of Record will be invited to join the repository.
- The Instructor of Record also holds the responsibility for supervising the awarding of all final grades and for reporting the grades to the Office of the Registrar. The lecturers choose to use the same end-of-semester course evaluation form as that being used by the Instructor of Record for his other course(s). Our Instructor of Record is welcomed to do in-class observations, and to give any help/suggestion during break. Our Instructor of Record agrees to help with any other unforeseeable issues if needed, unless he sees his involvements being inappropriate.
Learning Cooperatively
As you work on your assignments, we encourage you to discuss course content with your project partners. No matter your academic background, you will learn more if you work alongside others than if you work alone. Ask questions, answer questions, and share ideas liberally.
If some emergency takes you away from the course for an extended period, or if you decide to drop the course for any reason, please don’t just disappear silently! You should inform your TA, Tutor, and project partners immediately, so that nobody is expecting you to do something you can’t finish.
Academic Honesty
You must write your answers in your own words, and you must not share your completed work. The exception to this rule is that you can share everything related to a project with your project partners and turn in one project between all of you.
Please read Berkeley’s Code of Conduct carefully. Penalties for academic misconduct are severe, and include reporting to the Center for Student Conduct. They might also include a NP in the course or even dismissal from the university. It’s just not worth it!
When you need help, reach out to your TA and tutor. You are not alone! Directors and staff are here to help you succeed. We expect that you will work with integrity and with respect for other members of the class, just as staff will work with integrity and with respect for you.
A Parting Thought
The main goal of the course is that you should learn, and have a fantastic experience doing so. Please keep that goal in mind throughout the semester. Welcome to Data Science Society at Berkeley’s Data 198: Introduction to Real World Data Science!