Data Science for Product Managers
Additional course information available on Canvas.
The goal of this course is to provide you with the tools to understand and perform data science as it relates to product managers. You will learn and perform customer focused data analysis through a combination of lectures, readings, and practical skills development. Over the course of the semester, you will learn about data science and the entire data pipeline from collecting and analyzing to interacting with data.
This course requires comfort with programming, at a minimum python and associated libraries (pandas, scikit-learn, altair, etc.).
The learning goals of the course are as follows:
- To understand basic data manipulation (import, export, columns, subsets, data frames)
- To introduce common problems with data such as structural problems, outliers, incomplete data, and dirty data
- To introduce basic concepts in data interpretation including feature generation, statistical analysis and classification (assumptions of data, bad data, missing data, outliers and winsoring, data shape)
- To introduce concepts in data visualization including what makes a good visualization and the use of visualization in both exploratory data science and presentations to others
- To provide practical applied examples of the data science using machine learning techniques
- To explore and understand Data Science Ethics and ML Ethics
Schedule and Readings
Subject to modification
Introduction and the Data Science Pipeline Slides
Exploratory Data Analysis with Tableau Slides
Visual Encodings with Colab and Altair Slides
- Required Getting Started by Marian Dörk
- Required Data Types, Graphical Marks, and Visual Encoding Channels by Jeffrey Heer, Dominik Moritz, Jake VanderPlas, and Brock Craf
Practical Machine Learning Slides
- Required Black Boxes are not Required by Cynthia Rudin in Data Skeptic Podcast
- Optional Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead by Cynthia Rudin in Nature Machine Intelligence
Practical Machine Learning II Slides
Interpretability (Guest Lecture) Slides
Optional TA session Slides
Effective Visual Encodings Slides
- Optional Toward a Deeper Understanding of the Role of Interaction in Information Visualization by Ji Soo Yi, Youn ah Kang, John T. Stasko and Julie A. Jacko in IEEE Transactions on Visualization and Computer Graphics 2007
Telling Stories with Data Slides
- Required Basic Principles of Visualization (Chapter 5) by Alberto Cairo in The Truthful Art
- Optional What to consider when choosing colors for data visualization by Lisa Charlotte Muth 2018
Guest Lecture: Kunal Khadilkar (Data Scientist at Abobe) Slides
Optional TA help session Slides
The class will involve programming and debugging. You should not take the course if you find programming or debugging extremely difficult because you will have to master several programming languages/concepts/libraries in very short order. That being said, the assignments that require these will have useful resources for brushing up on the topics.
There is no required textbook for this course. Readings are drawn from a variety of books, readings and online postings, and will be provided by the instructor.
Amount of Work
This is a “6 unit” mini. As per university policy, this means that this course is expected to take students 12 hours per week, including class time. Surveys of previous students show that this is accurate.
How to Submit Assignments
All assignments must be turned in using Canvas.
The tentative breakdown for grading is below. As a reminder, here is the university policy on academic integrity.
There will be 5 assignments, each totaling to 90% of your final grade. All assignments in this course are individual: you are required to do them by yourself. Each person must do their own work independently. Participation will comprise the remaining 10%:
- Assignment 0: 5%
- Assignment 1: 20%
- Assignment 2: 20%
- Assignment 3: 20%
- Assignment 4: 25%
- Participation: 10%
Participation includes interaction during classroom activities, as well as sharing stories, questions and comments related to past and upcoming lectures.
Assignments are due before the beginning of class (12:30 PM ET) on the specified day. A penalty of 10 points out of 100 (one letter grade) will be immediately applied after the start time of class. An additional 5 points will be subtracted for each additional 24-hour period late. You are responsible to confirm that your Canvas submittal was successfully uploaded.
Respect for Diversity
It is our intent that students from all diverse backgrounds and perspectives be well served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength and benefit. It is our intent to present materials and activities that are respectful of diversity: gender, sexuality, disability, age, socioeconomic status, ethnicity, race, and culture. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students or student groups. In addition, if any of our class meetings conflict with your religious events, please let us know so that we can make arrangements for you.
Accommodations for Students with Disabilities
If you have a disability and are registered with the Office of Disability Resources, we encourage you to use their online system to notify us of your accommodations and discuss your needs with us as early in the semester as possible. We will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, we encourage you to contact them at email@example.com.
Health and Well-being
If you are experiencing COVID-like symptoms or have a recent COVID exposure, do not attend class if we are meeting in-person. Please email the instructors for accomodations.
If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help; call 412-268-2922 and visit their website at www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help. If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:
If you have questions about this or your coursework, please let the instructors know. Thank you, and have a great semester.