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TECH 07 — Breaking into Data Science

Quarter: Fall
Day(s): Mondays
Course Format: Live Online (About Formats)
Duration: 8 weeks
Date(s): Oct 2—Dec 4
Time: 7:00—8:50 pm (PT)
Refund Deadline: Oct 4
Unit: 1
Tuition: $500
Instructor(s): Grace Tang
Class Recording Available: Yes
Status: Open
Please Note: No class on October 16 and November 20
ACCESS THE SYLLABUS » (subject to change)
Live Online(About Formats)
7:00—8:50 pm (PT)
Oct 2—Dec 4
8 weeks
Refund Date
Oct 4
1 Unit
Grace Tang
Please Note: No class on October 16 and November 20
ACCESS THE SYLLABUS » (subject to change)
An essential skill for every data scientist is asking the right questions about the data in order to extract relevant and actionable insights. This means understanding the context and purpose of the data and asking questions that unlock its hidden value. It is the kind of work that is challenging, exciting, and impactful. Fortunately, students can learn these foundational skills without enrolling in a multi-year program.

This unique course will provide a gentle introduction to the data science landscape to help students determine if it suits them and which roles might be most fitting for them. Students will leave the course with a starter portfolio of projects to showcase their work, which they can build upon in the future. Using real-world open data sets and open source tools, students will create a portfolio from scratch using free online resources such as GitHub, W3Schools, Kaggle, and data.gov. They'll practice skills like sourcing open data sets; ingesting, cleaning, and processing data; troubleshooting; and finally, telling a compelling story through the interpretation of data. They'll also explore how data science is applied in technology, retail, government, and energy by covering several case studies on topics such as predictive modeling, A/B testing, and anomaly detection.

Prior programming experience in Python or R is recommended but not required. Supplementary resources will be provided to students without programming experience for self-study.

Data Scientist

Grace Tang received a PhD in neuroscience from Stanford, where she studied how personality traits, emotions, and external stimuli affect decision-making. Since graduating in 2014, she has been a data scientist at various companies, including Uber, Grab, and Netflix. She is passionate about helping newcomers break into data science, especially from nontraditional fields like the social sciences.

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.