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Data Science

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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.

Courses
4 Required
0 Elective
Methods
Online
F-1 Visa Eligible

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This program is perfect for...

Multidisciplinary professionals seeking to learn or enhance data analytics skills
Aspiring data scientists looking to build skills in data management, visualization, and programming
Technologists and software developers interested in a career shift to data analytics

What you can learn.

Explore data sets with Python
Gain expertise in exploratory data analysis (EDA) and advanced data visualization, using R and Tableau
Learn foundational tools and techniques for analyzing data, delivering insights, and building forecasting models
Apply machine learning techniques using Python to train, evaluate, improve performance, and present statistical analyses
Understand the management of big data, types of NoSQL data stores, and the Hadoop ecosystem

Build the foundation for a career in data science.

Today’s data scientist is a business-savvy, data-driven problem solver with the insights to ask the right questions and the skills to find the correct answers. The role is part art and part science, requiring big data management, programming, statistics, and machine learning for extracting, analyzing, and communicating findings.

 

Data Science at UCLA Extension

This certificate is approved for international students to study full time in the U.S. on an F-1 visa.

International students must meet English language proficiency requirements for admission into the program.

While most certificate programs begin each quarter, some have limited intakes for students studying on F-1 visa. Find this program on the Browse Certificate Programs page to confirm entry quarters before applying.

Learn more:

Get Started

Step 1: Enroll in a Course
Courses in this program are open enrollment and paid for individually. You may take courses without committing to the program.
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Have questions? Talk to an Academic Advisor
Step 2: Become a Candidate
Establish candidacy in the certificate program to receive tailored information, lock in course requirements, and gain access to exclusive student benefits. Registration Fee: $200
Apply Now
Important Note for International Students: Candidacy fees are included in your program fees and do not need to be paid separately.

Cost and Fees

All courses in this program are paid for individually, unless otherwise noted. An application form is required to establish candidacy in this program. From the 'Apply Now' button, complete the online application and pay the application fee if applicable.
Application and Candidacy Fee
$200
Estimated Textbooks and Materials
$374
Estimated Program Tuition
$5475

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Payment Options

Required Courses

Customize your experience

Suggested Prerequisite

COM SCI X 450.00 is a suggested prerequisite course for students with limited or no prior programming, statistics, or data analytics knowledge. 
COM SCI X 450.00

Data Science Fundamentals

Designed for students with limited programming, statistics, or data analytics knowledge, this data science overview course is ideal for beginners, business analysts, or non-tech managers interested in the field.
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Required Courses

COM SCI X 450.1

Introduction to Data Science

This course covers the key knowledge domains in data science, including data development and management, machine learning and natural language processing, statistical analysis, data visualization, and inference.
View Course
COM SCI X 450.2

Exploratory Data Analysis and Visualization

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.
View Course
COM SCI X 450.3

Big Data Management

Learn tools for distributed data storage and large data set processing. This course focuses on architectures and technologies, including NoSQL and Hadoop.
View Course

Machine Learning Course

Students must complete one of the following courses.
COM SCI X 450.4

Machine Learning Using Python

Learn machine learning origins, principles, and practical applications, as well as implementation via the Python programming language. Students will learn to train a model, evaluate its performance, and improve its performance.
View Course
COM SCI X 450.41

Machine Learning Using R

Learn machine learning origins, principles, and practical applications, as well as implementation via the R programming language. Students will learn to train a model, evaluate its performance, and improve its performance.
View Course
COM SCI X 450.42

Deep Learning

Gain a robust understanding of deep learning through both theory and hands-on implementation, spanning domains such as computer vision, natural language processing (NLP) and graph data analysis. Explore neural network architectures, optimization techniques, and advanced models (CNNs, RNNs, GANs, GNNs). 
View Course

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