Course Overview

This course exposes non-CS students to basic data science and data processing concepts and provides students with tool kits to conduct machine learning-based analysis. The course is designed with the powerful GUI-based data analysis platform called Texera.

Adopting workflows in data science represents a new trend toward optimizing project efficiency and enhancing collaboration. These structured processes streamline data analysis and modeling, ensuring consistency and reproducibility in managing complex tasks. By embracing workflows, data scientists can tackle the challenges of data processing and model development more effectively, marking a significant shift towards more systematic and reliable data science practices.

The course is intended for two purposes:

It introduces students to modern data analysis techniques including data cleaning, data wrangling, data visualization, and machine learning,
It teaches students how to use these tools and technologies to conduct real-world analysis projects.

Course Information

Lecture time: M/W 11:00 am - 12:20 pm in SST 220A, in-person lecture only.

Staff	Office Hours	Email
Instructor: Yicong Huang	Fri 4-5pm, ICS 458A	yicongh1 AT uci.edu
Assistant: Shengquan Ni	Tue 1-2pm, ICS 458A	shengqun AT uci.edu
Guest Lecturer: Chen Li	N/A	chenli AT ics.uci.edu

Lectures

Lecture ID	Day	Date	Topic	Resources
1	Mon	04/01/2024	Course Introduction, Syllabus, Introduction to data science	Lecture 1 Introduction.pdf
2	Wed	04/03/2024	Relational Data Model, Data Schema	Lecture 2 Data Model, Schema.pdf
3	Mon	04/08/2024	Data Cleaning, Sources, Filter, Projection, Limit, Distinct, Set Operators	Lecture 3 Data Cleaning, Simple Transformations.pdf
4	Wed	04/10/2024	Exploratory Analysis, Aggregate, Entity-Relation Data Model, Join, Cartesian Product, Sort	Lecture 4 Aggregate, Join, Sort.pdf
5	Mon	04/15/2024	Exploratory Analysis and Data Visualizations, Quiz 1	Lecture 5 Data Visualizations.pdf
6	Wed	04/17/2024	Python and Python UDF Operator	Lecture 6 Python and Python UDF.pdf
7	Mon	04/22/2024	Python UDF Tuple API	Lecture 7 Python UDF Tuple API.pdf
8	Wed	04/24/2024	Python UDF Tuple API, Timeseries Analysis	Lecture 8 Timeseries.pdf
9	Mon	04/29/2024	Image Processing	Lecture 9 Image Processing.pdf
10	Wed	05/01/2024	Image Processing (cont.), Visualization, Quiz 2	Lecture 10 Image Processing (cont.).pdf
11	Mon	05/06/2024	Machine Learning Intro, Supervised Learning, Regression, Linear Regression	Lecture 11 Machine Learning Intro, Supervised Learning, Regression.pdf
12	Wed	05/08/2024	Classification, Logistic Regression, KNN, Decision Tree, Random Forest	Lecture 12 Classification.pdf
13	Mon	05/13/2024	Unsupervised Learning, Clustering, Python UDF Table API, Customized Training	Lecture 13 Unsupervised Learning, Clustering, Table API, Customized Training.pdf
14	Wed	05/15/2024	Natural Language Processing, Sentiment Analysis, Quiz 3	Lecture 14 Natural Language Processing, Sentiment Analysis.pdf
15	Mon	05/20/2024	Workshop, Case Study	Lecture 15 Capstone Project.pdf
16	Wed	05/22/2024	Workshop
	Mon	05/27/2024	Memorial Day. No Lecture
17	Wed	05/29/2024	Workshop
18	Mon	06/03/2024	Workshop, Quiz 4
19	Wed	06/05/2024	Capstone Project Showcase

Assignments

Assignment	Topic	Days	Due Date	Weight (of the entire course)
1	Tweet Analysis	13	Apr 14, 8 PM	10%
2	Timeseries and Image Data Analysis	13	Apr 28, 8 PM (Task 3 due on May 5th, 8 PM)	10%
3	Data Analysis with ML	11	May 19, 8PM	10%

Capstone Project

	Days	Due Date	Weight (of the entire course)
Project Proposal	5	05/23	5%
Project Capstone Showcase	17	06/05	35%

Quizzes

We will not have midterms or finals. Instead, we will do in-class quizzes during lectures. We will announce quizzes ahead of time.

	Date	Weight (of the entire course, the lowest one will be dropped)
1	04/15	10%
2	05/01	10%
3	05/15	10%
4	06/03	10%

Survey

You will participate in a survey evaluating the experience of using workflows for data science projects. The participation will account for 3% of your course grade, regardless of your feedback.

Texera Course Service

In this course, we will use Texera system deeply to illustrate concepts, carry out assignments/projects, and facilitate communication.

Online Texera Service

Our team maintains a Texera live service at https://texera-ics80.ics.uci.edu. Please log in with your UCI email using the Google Login feature.

The service will be available to you every day 9 am - 9 pm. We will conduct maintenance outside this time. (Note: this means your execution may be force-killed after 9 pm.) You can check back this dashboard for the latest status of the service.

Given that the Texera staff maintains this service and it is shared across the entire course, we urge you to handle it responsibly. Do not overwhelm it with large datasets or execute tasks that require a large amount of time (i.e. more than 1 hour). Your activities on the platform will be monitored. Activities including but not limited to crawling, cyber-attacking, and crypto-mining are strictly forbidden.

Texera Get Started Guide.

Account Activation Notice: After logging in with your UCI account, you might find that you are unable to access the dashboard. This is because your account requires manual activation by our team. Please allow for a delay of up to one day for this activation process to be completed.

Online Discussion Forum

Texera embeds a discussion forum, you can access it within Texera (backup independent link). This system is highly catered to getting you help fast and efficiently from classmates, the TA(s), and the instructor. Rather than emailing questions to any of us on the teaching staff, we'll ask you to post your questions on the Forum. Optionally, you can make a private post. Please ensure that the content you post is meaningful and adheres to ethical standards.

Grading Breakdown

Assignments: 30%
Quizzes: 30%
Capstone Project: 40%
Participation in Survey (Optional): extra 3%
Participation in EEE Class Evaluation (Optional): extra 1%

For all the graded projects and exams, if you disagree with the grading, you can discuss them with us within one week after they are returned. After that, all the grades will be finalized.

Prerequisites

This course is designed for individuals who may have little to no experience in coding, so there are no strict CS course prerequisites. The second half of the course will include an introduction to the programming language Python and its rich ecosystem for AI and ML. To better consume the course materials, we recommend you take ICS 31 and ICS 32 as they provide a basic introduction to programming.

Textbooks

Many online tutorials.

Working in Teams

You will work individually on Assignments and Quizzes.
You are expected to work in a team of 2 to collaborate on the Capstone Project. Work in groups will be graded on a per-group basis.

Policy on Academic Honesty

All students will be expected to adhere to the UCI and ICS Academic Honesty policies (see https://conduct.uci.edu/students/academic-integrity/index.php for details). Any student found to be involved in cheating or aiding others in doing so will be academically prosecuted to the maximum extent possible: that means you will fail this course. Just say no to cheating!