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Exploratory Data Analysis and Visualization

Exploratory Data Analysis and Visualization
COM SCI X 450.2

Learn the iterative process of exploratory data analysis (EDA), data analysis techniques, data exploration, and visualization. Course tools include R for data analysis and Tableau for data visualization.

Typically Available
Fall
Winter
Spring
Summer
Duration
As few as 11 weeks
Units
4.0
Current Formats
Online
In Person
Cost
Starting at $1,100.00

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What you can learn.

Analyze and prepare raw datasets for exploration by applying foundational data cleaning and preprocessing techniques.
Create effective visualizations that support data exploration, communication, and early analytical insight.
Develop visual representations for presentations and dashboards using principles of exploratory analysis and business intelligence.
Work confidently with new or unfamiliar visualization libraries to expand analytical and communication capabilities.

About This Course

This course provides a practical introduction to the core concepts, methods, and visualization techniques that support modern data exploration and analytics. Students will learn how data science uses historical information to understand past behavior and how predictive analytics can help anticipate future outcomes. Emphasis is placed on exploratory data analysis, an iterative process for uncovering patterns and insights, and on effective data visualization, a rapidly growing area within business intelligence.

Learners will develop skills in preparing and analyzing raw data, applying exploratory techniques, and creating meaningful visual representations using Tableau. Through hands‑on work with diverse datasets, students will practice building visualizations that support analysis, communication, and decision‑making. The course also introduces data visualization in R, enabling students to expand their toolkit and work confidently with unfamiliar visualization libraries.

By the end of the course, students will be able to analyze and prepare data for processing, produce visualizations in R, and create visual outputs for exploration, presentation, and dashboard development.

Winter 2027 Schedule

Date
Details
Format
 
-
Wednesday 6:00PM - 9:30PM PT
REG#
410974
Fee:
$1,100.00
In Personformat icon
UCLA
Updating...
Notes
Enrollment limited; early enrollment advised. Visitors not permitted. Enrollment deadline: January 10, 2027.
Deadline
Refunds only available from November 02, 2026 to January 17, 2027
Course Requirements
Internet access required to retrieve course materials.
Schedule
Type
Date
Time
Location
Lecture
Wed Jan 6, 2027
6:00PM PT - 9:30PM PT
UCLA
Physics & Astronomy Bldg. 2434
Lecture
Wed Jan 13, 2027
6:00PM PT - 9:30PM PT
UCLA
Physics & Astronomy Bldg. 2434
Lecture
Wed Jan 20, 2027
6:00PM PT - 9:30PM PT
UCLA
Physics & Astronomy Bldg. 2434
Lecture
Wed Jan 27, 2027
6:00PM PT - 9:30PM PT
UCLA
Physics & Astronomy Bldg. 2434
Lecture
Wed Feb 3, 2027
6:00PM PT - 9:30PM PT
UCLA
Physics & Astronomy Bldg. 2434
Lecture
Wed Feb 10, 2027
6:00PM PT - 9:30PM PT
UCLA
Physics & Astronomy Bldg. 2434
Lecture
Wed Feb 17, 2027
6:00PM PT - 9:30PM PT
UCLA
Physics & Astronomy Bldg. 2434
Lecture
Wed Feb 24, 2027
6:00PM PT - 9:30PM PT
UCLA
Physics & Astronomy Bldg. 2434
Lecture
Wed Mar 3, 2027
6:00PM PT - 9:30PM PT
UCLA
Physics & Astronomy Bldg. 2434
Lecture
Wed Mar 10, 2027
6:00PM PT - 9:30PM PT
UCLA
Physics & Astronomy Bldg. 2434
Lecture
Wed Mar 17, 2027
6:00PM PT - 9:30PM PT
UCLA
Physics & Astronomy Bldg. 2434
-
This section has no set meeting times.
Instructor:
REG#
410929
Fee:
$1,100.00
Onlineformat icon
Updating...
Notes
Enrollment limited; early enrollment advised. Enrollment deadline: January 10, 2027.
Deadline
Refunds only available from November 02, 2026 to January 17, 2027
Course Requirements
Internet access required to retrieve course materials.

This course applies toward the following programs

data visualization graohic

Data Science

certificate
certificate Learn to leverage the power of big data to extract insights and improve decision making for real-world problems. Gain hands-on experience in data management and visualization, machine learning, statistical models, and more for a career in data science.

Learn to leverage the power of big data to extract insights and improve decision making for real-world problems. Gain hands-on experience in data management and visualization, machine learning, statistical models, and more for a career in data science.