Course Syllabus
Date | Lecture Topic | Relevant Resources |
January 6 | Course overview and plan (Minin) | lecture1_introduction.pdf Download lecture1_introduction.pdf |
January 8 | Dusting off your databases (Li) | lecture02-li (pptx) Links to an external site. |
January 13 | Data wrangling concepts and issues | lecture03-li (pptx) Links to an external site. |
January 15 | Wrangling with Pandas and Dataframes I | lecuture04-li (ipynb) Links to an external site., Files Links to an external site. |
January 20 | ||
January 22 | Wrangling with Pandas and Dataframes II | lecuture05-li (ipynb) Links to an external site., Files (ditto) |
January 27 | Data analytics using GUI-based workflows | lecture06-li Links to an external site. |
January 29 |
Postgres, Twitter, and Tweepy |
|
February 3 | Exploratory data analysis and data visualization I | lecture8_dataviz.pdf Download lecture8_dataviz.pdf |
February 5 | Exploratory data analysis and data visualization II | lecture9_dataviz.pdf Download lecture9_dataviz.pdf |
February 10 | Clustering | |
February 12 | Clustering and PCA |
housing_data.csv Download housing_data.csv pca_demo.ipynb Download pca_demo.ipynb ISLR_unsupervized_learning.pdf Download ISLR_unsupervized_learning.pdf |
February 17 | no class | |
February 19 | Supervised learning and regression |
regression_demo.ipynb Download regression_demo.ipynb ISLR_regression_classification.pdf Download ISLR_regression_classification.pdf |
February 24 | Resampling methods | ISLR_resampling.pdf Download ISLR_resampling.pdf |
February 26 | Project idea meetings | |
March 2 | Project planning meetings | |
March 4 | Project planning meetings | |
March 9 | Oral project proposal meetings | |
March 11 | Oral project proposal presentations |
Assignments, Projects, and Grading
Winter Grading Criteria (for 170A)
Homework: 40%
Project proposal: 50%
Class participation: 10%
Late homeworks will not be graded - please submit whatever you have completed by the homework deadline.
A single grade will be assigned at the end of Spring quarter for this class, with 50% weight on the Winter grade and 50% on the Spring grade.
Homework and Class Participation
The first quarter will involve a mix of lectures and homework assignments intended to dust off, sharpen, or introduce the skills, tools, and techniques that you will need to successfully execute your course project. Since you are now seniors, and this is your Data Science grand finale, individual initiative and engagement will be expected of all students. The homework assignments may be "looser" than what you are used to -- you will have to seek out some of the information needed to complete the assignments and to make choices about how to attack some of the challenges -- i.e., spoon feeding will be kept to a minimum. The lectures will aim for interactivity, and class participation will be encouraged (and in fact expected).
Academic Honesty Policy
Students will be expected to adhere to the UCI and ICS Academic Honesty policies (see http://www.editor.uci.edu/catalogue/appx/appx.2.htm#academic and http://www.ics.uci.edu/ugrad/policies/index.php#academic_honesty to read their details). Any student found to somehow be involved in cheating or aiding others in doing so will be academically prosecuted to the maximum extent possible: that means that you could fail this course in its entirety. (Ask around - it's happened.) Just say no to cheating!
Software Platform(s)
This course will make use of the Python ecosystem, including the Python language, various Python packages/tools for data analysis and machine learning, Jupyter notebooks, and open source databases (PostgreSQL). For convenience and package completeness, students are advised to download the most recent Anaconda distribution of Python and friends (https://www.anaconda.com/download/ Links to an external site.) and the most recent EDB distribution of PostgreSQL (https://www.enterprisedb.com/downloads/postgres-postgresql-downloads Links to an external site.).
Course Summary:
Date | Details | Due |
---|---|---|
Wed Jan 15, 2020 | Assignment HW1 | due by 11:45pm |
Wed Jan 22, 2020 | Assignment HW2 | due by 11:45pm |
Wed Jan 29, 2020 | Assignment HW3 | due by 11:45pm |
Wed Feb 5, 2020 | Assignment HW4 | due by 11:45pm |
Wed Feb 12, 2020 | Assignment HW5 | due by 11:45pm |
Wed Feb 19, 2020 | Assignment HW6 | due by 11:45pm |
Wed Feb 26, 2020 | Assignment HW7 | due by 11:45pm |
Wed Mar 4, 2020 | Assignment HW8 --- Project Proposal First Draft | due by 11:45pm |
Mon Mar 9, 2020 | Assignment Project proposal presentations | due by 9am |
Assignment Presentation Notes and Questions (1) | due by 12:30pm | |
Wed Mar 11, 2020 | Assignment Presentation Notes and Questions (2) | due by 12:30pm |
Thu Mar 19, 2020 | Assignment Revised Proposal | due by 11:45pm |
Assignment Project Proposals and Slides |