|Date||Lecture Topic||Relevant Resources|
|January 6||Course overview and plan (Minin)||lecture1_introduction.pdf|
|January 8||Dusting off your databases (Li)||lecture02-li (pptx)|
|January 13||Data wrangling concepts and issues||lecture03-li (pptx)|
|January 15||Wrangling with Pandas and Dataframes I||lecuture04-li (ipynb), Files|
|January 22||Wrangling with Pandas and Dataframes II||lecuture05-li (ipynb), Files (ditto)|
|January 27||Data analytics using GUI-based workflows||lecture06-li|
Postgres, Twitter, and Tweepy
|February 3||Exploratory data analysis and data visualization I||lecture8_dataviz.pdf|
|February 5||Exploratory data analysis and data visualization II||lecture9_dataviz.pdf|
|February 12||Clustering and PCA|
|February 17||no class|
|February 19||Supervised learning and regression|
|February 24||Resampling methods||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)
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!
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/) and the most recent EDB distribution of PostgreSQL (https://www.enterprisedb.com/downloads/postgres-postgresql-downloads).
The syllabus page shows a table-oriented view of the course schedule, and the basics of course grading. You can add any other comments, notes, or thoughts you have about the course structure, course policies or anything else.
To add some comments, click the "Edit" link at the top.